Metamorphic Virus Detection in Portable Executables Using Opcodes Statistical Feature

Abstract: Metamorphic viruses engage different mutation techniques to escape from string signature based scanning. They try to change their code in new offspring so that the variants appear non-similar and have no common sequences of string as signature. However, all versions of a metamorphic virus have similar task and performance. This obfuscation process helps to keep them safe from the string based signature detection. In this study, we make use of instructions statistical features to compare the similarity of two hosted files probably occupied by two mutated forms of a specific metamorphic virus. The introduced solution in this paper is relied on static analysis and employs the frequency histogram of machine opcodes in different instances of obfuscated viruses. We use Minkowski-form histogram distance measurements in order to check the likeness of portable executables (PE). The purpose of this research is to present an idea that for a number of special obfuscation approaches the presented solution can be used to identify morphed copies of a file. Thus, it can be applied by antivirus scanner to recognize different versions of a metamorphic virus.


Adding noise to the input of a model trained with a regularized objective

Abstract: Regularization is a well studied problem in the context of neural networks. It is usually used to improve the generalization performance when the number of input samples is relatively small or heavily contaminated with noise. The regularization of a parametric model can be achieved in different manners some of which are early stopping (Morgan and Bourlard, 1990), weight decay, output smoothing that are used to avoid overfitting during the training of the considered model. From a Bayesian point of view, many regularization techniques correspond to imposing certain prior distributions on model parameters (Krogh and Hertz, 1991). Using Bishop's approximation (Bishop, 1995) of the objective function when a restricted type of noise is added to the input of a parametric function, we derive the higher order terms of the Taylor expansion and analyze the coefficients of the regularization terms induced by the noisy input. In particular we study the effect of penalizing the Hessian of the mapping function with respect to the input in terms of generalization performance. We also show how we can control independently this coefficient by explicitly penalizing the Jacobian of the mapping function on corrupted inputs.


Affine trajectory correction for nonholonomic mobile robots

Abstract: Planning trajectories for nonholonomic systems is difficult and computationally expensive. When facing unexpected events, it may therefore be preferable to deform in some way the initially planned trajectory rather than to re-plan entirely a new one. We suggest here a method based on affine transformations to make such deformations. This method is exact and fast: the deformations and the resulting trajectories can be computed algebraically, in one step, and without any trajectory re-integration. To demonstrate the possibilities offered by this new method, we use it to derive position and orientation correction algorithms for the general class of planar wheeled robots and for a tridimensional underwater vehicle. These algorithms allow in turn achieving more complex applications, including obstacle avoidance, feedback control or gap filling for sampling-based kinodynamic planners.


Practical and Efficient Split Decomposition via Graph-Labelled Trees

Abstract: Split decomposition of graphs was introduced by Cunningham (under the name join decomposition) as a generalization of the modular decomposition. This paper undertakes an investigation into the algorithmic properties of split decomposition. We do so in the context of graph-labelled trees (GLTs), a new combinatorial object designed to simplify its consideration. GLTs are used to derive an incremental characterization of split decomposition, with a simple combinatorial description, and to explore its properties with respect to Lexicographic Breadth-First Search (LBFS). Applying the incremental characterization to an LBFS ordering results in a split decomposition algorithm that runs in time $O(n+m)\alpha(n+m)$, where $\alpha$ is the inverse Ackermann function, whose value is smaller than 4 for any practical graph. Compared to Dahlhaus' linear-time split decomposition algorithm [Dahlhaus'00], which does not rely on an incremental construction, our algorithm is just as fast in all but the asymptotic sense and full implementation details are given in this paper. Also, our algorithm extends to circle graph recognition, whereas no such extension is known for Dahlhaus' algorithm. The companion paper [Gioan et al.] uses our algorithm to derive the first sub-quadratic circle graph recognition algorithm.


Practical and Efficient Circle Graph Recognition

Abstract: Circle graphs are the intersection graphs of chords in a circle. This paper presents the first sub-quadratic recognition algorithm for the class of circle graphs. Our algorithm is O(n + m) times the inverse Ackermann function, {\alpha}(n + m), whose value is smaller than 4 for any practical graph. The algorithm is based on a new incremental Lexicographic Breadth-First Search characterization of circle graphs, and a new efficient data-structure for circle graphs, both developed in the paper. The algorithm is an extension of a Split Decomposition algorithm with the same running time developed by the authors in a companion paper.


Computer Arithmetic Preserving Hamming Distance of Operands in Operation Result

Abstract: The traditional approach to fault tolerant computing involves replicating computation units and applying a majority vote operation on individual result bits. This approach, however, has several limitations; the most severe is the resource requirement. This paper presents a new method for fault tolerant computing where for a given error rate, the hamming distance between correct inputs and faulty inputs as well as the hamming distance between a correct result and a faulty result is preserved throughout processing thereby enabling correction of up to transient faults per computation cycle. The new method is compared and contrasted with current protection methods and its cost / performance is analyzed.


Hiding Information in a Stream Control Transmission Protocol

Abstract: The STCP (Stream Control Transmission Protocol) is a candidate for a new transport layer protocol that may replace the TCP (Transmission Control Protocol) and the UDP (User Datagram Protocol) protocols in future IP networks. Currently, the SCTP is implemented in, or can be added to, many popular operating systems (Windows, BSD, Linux, HPUX or Sun Solaris). This paper identifies and presents all possible "places" where hidden information can be exchanged using an SCTP. The paper focuses mostly on proposing new steganographic methods that can be applied to an SCTP and that can utilise new, characteristic SCTP features, such as multi-homing and multi-streaming. Moreover, for each method, the countermeasure is covered. When used with malicious intent, a method may pose a threat to network security. Knowledge about potential SCTP steganographic methods may be used as a supplement to RFC5062, which describes security attacks in an SCTP protocol. Presented in this paper is a complete analysis of information hiding in an SCTP, and this analysis can be treated as a "guide" when developing steganalysis (detection) tools.


Correlation Testing for Affine Invariant Properties on $\mathbb{F}_p^n$ in the High Error Regime

Abstract: Recently there has been much interest in Gowers uniformity norms from the perspective of theoretical computer science. This is mainly due to the fact that these norms provide a method for testing whether the maximum correlation of a function $f:\mathbb{F}_p^n \rightarrow \mathbb{F}_p$ with polynomials of degree at most $d \le p$ is non-negligible, while making only a constant number of queries to the function. This is an instance of {\em correlation testing}. In this framework, a fixed test is applied to a function, and the acceptance probability of the test is dependent on the correlation of the function from the property. This is an analog of {\em proximity oblivious testing}, a notion coined by Goldreich and Ron, in the high error regime. In this work, we study general properties which are affine invariant and which are correlation testable using a constant number of queries. We show that any such property (as long as the field size is not too small) can in fact be tested by Gowers uniformity tests, and hence having correlation with the property is equivalent to having correlation with degree $d$ polynomials for some fixed $d$. We stress that our result holds also for non-linear properties which are affine invariant. This completely classifies affine invariant properties which are correlation testable. The proof is based on higher-order Fourier analysis. Another ingredient is a nontrivial extension of a graph theoretical theorem of Erd\"os, Lov\'asz and Spencer to the context of additive number theory.


Symbolic Algorithms for Qualitative Analysis of Markov Decision Processes with B\"uchi Objectives

Abstract: We consider Markov decision processes (MDPs) with \omega-regular specifications given as parity objectives. We consider the problem of computing the set of almost-sure winning states from where the objective can be ensured with probability 1. The algorithms for the computation of the almost-sure winning set for parity objectives iteratively use the solutions for the almost-sure winning set for B\"uchi objectives (a special case of parity objectives). Our contributions are as follows: First, we present the first subquadratic symbolic algorithm to compute the almost-sure winning set for MDPs with B\"uchi objectives; our algorithm takes O(n \sqrt{m}) symbolic steps as compared to the previous known algorithm that takes O(n^2) symbolic steps, where $n$ is the number of states and $m$ is the number of edges of the MDP. In practice MDPs have constant out-degree, and then our symbolic algorithm takes O(n \sqrt{n}) symbolic steps, as compared to the previous known $O(n^2)$ symbolic steps algorithm. Second, we present a new algorithm, namely win-lose algorithm, with the following two properties: (a) the algorithm iteratively computes subsets of the almost-sure winning set and its complement, as compared to all previous algorithms that discover the almost-sure winning set upon termination; and (b) requires O(n \sqrt{K}) symbolic steps, where K is the maximal number of edges of strongly connected components (scc's) of the MDP. The win-lose algorithm requires symbolic computation of scc's. Third, we improve the algorithm for symbolic scc computation; the previous known algorithm takes linear symbolic steps, and our new algorithm improves the constants associated with the linear number of steps. In the worst case the previous known algorithm takes 5n symbolic steps, whereas our new algorithm takes 4n symbolic steps.


The distribution of cycles in breakpoint graphs of signed permutations

Abstract: Breakpoint graphs are ubiquitous structures in the field of genome rearrangements. Their cycle decomposition has proved useful in computing and bounding many measures of (dis)similarity between genomes, and studying the distribution of those cycles is therefore critical to gaining insight on the distributions of the genomic distances that rely on it. We extend here the work initiated by Doignon and Labarre, who enumerated unsigned permutations whose breakpoint graph contains $k$ cycles, to signed permutations, and prove explicit formulas for computing the expected value and the variance of the corresponding distributions, both in the unsigned case and in the signed case. We also compare these distributions to those of several well-studied distances, emphasising the cases where approximations obtained in this way stand out. Finally, we show how our results can be used to derive simpler proofs of other previously known results.


Empirical Encounters with Computational Irreducibility and Unpredictability

Abstract: There are several forms of irreducibility in computing systems, ranging from undecidability to intractability to nonlinearity. This paper is an exploration of the conceptual issues that have arisen in the course of investigating speed-up and slowdown phenomena in small Turing machines. We present the results of a test that may spur experimental approaches to the notion of computational irreducibility. The test involves a systematic attempt to outrun the computation of a large number of small Turing machines (all 3 and 4 state, 2 symbol) by means of integer sequence prediction using a specialized function finder program. This massive experiment prompts an investigation into rates of convergence of decision procedures and the decidability of sets in addition to a discussion of the (un)predictability of deterministic computing systems in practice. We think this investigation constitutes a novel approach to the discussion of an epistemological question in the context of a computer simulation, and thus represents an interesting exploration at the boundary between philosophical concerns and computational experiments.


Probabilistic Analysis of Loss in Interface Adapter Chaining

Abstract: Interface adapters allow applications written for one interface to be reused with another interface without having to rewrite application code, and chaining interface adapters can significantly reduce the development effort required to create the adapters. However, interface adapters will often be unable to convert interfaces perfectly, so there must be a way to analyze the loss from interface adapter chains in order to improve the quality of interface adaptation. This paper describes a probabilistic approach to analyzing loss in interface adapter chains, which not only models whether a method can be adapted but also how well methods can be adapted. We also show that probabilistic optimal adapter chaining is an NP-complete problem, so we describe a greedy algorithm which can construct an optimal interface adapter chain with exponential time in the worst case.


Markov Decision Processes with Multiple Long-run Average Objectives

Abstract: We study Markov decision processes (MDPs) with multiple limit-average (or mean-payoff) functions. We consider two different objectives, namely, expectation and satisfaction objectives. Given an MDP with k limit-average functions, in the expectation objective the goal is to maximize the expected limit-average value, and in the satisfaction objective the goal is to maximize the probability of runs such that the limit-average value stays above a given vector. We show that under the expectation objective, in contrast to the case of one limit-average function, both randomization and memory are necessary for strategies even for epsilon-approximation, and that finite-memory randomized strategies are sufficient for achieving Pareto optimal values. Under the satisfaction objective, in contrast to the case of one limit-average function, infinite memory is necessary for strategies achieving a specific value (i.e. randomized finite-memory strategies are not sufficient), whereas memoryless randomized strategies are sufficient for epsilon-approximation, for all epsilon>0. We further prove that the decision problems for both expectation and satisfaction objectives can be solved in polynomial time and the trade-off curve (Pareto curve) can be epsilon-approximated in time polynomial in the size of the MDP and 1/epsilon, and exponential in the number of limit-average functions, for all epsilon>0. Our analysis also reveals flaws in previous work for MDPs with multiple mean-payoff functions under the expectation objective, corrects the flaws, and allows us to obtain improved results.


Fife's Theorem for (7/3)-Powers

Abstract: We prove a Fife-like characterization of the infinite binary (7/3)-power-free words, by giving a finite automaton of 15 states that encodes all such words. As a consequence, we characterize all such words that are 2-automatic.


An Effect of Spatial Filtering in Visualization of Coronary Arteries Imaging

Abstract: At present, coronary angiography is the well known standard for the diagnosis of coronary artery disease. Conventional coronary angiography is an invasive procedure with a small, yet inherent risk of myocardial infarction, stroke, potential arrhythmias, and death. Other noninvasive diagnostic tools, such as electrocardiography, echocardiography, and nuclear imaging are now widely available but are limited by their inability to directly visualize and quantify coronary artery stenoses and predict the stability of plaques. Coronary magnetic resonance angiography (MRA) is a technique that allows visualization of the coronary arteries by noninvasive means; however, it has not yet reached a stage where it can be used in routine clinical practice. Although coronary MRA is a potentially useful diagnostic tool, it has limitations. Further research should focus on improving the diagnostic resolution and accuracy of coronary MRA. This paper will helps to cardiologists to take the clear look of spatial filtered imaging of coronary arteries.


An Optimal Real-Time Scheduling Approach: From Multiprocessor to Uniprocessor

Abstract: An optimal solution to the problem of scheduling real-time tasks on a set of identical processors is derived. The described approach is based on solving an equivalent uniprocessor real-time scheduling problem. Although there are other scheduling algorithms that achieve optimality, they usually impose prohibitive preemption costs. Unlike these algorithms, it is observed through simulation that the proposed approach produces no more than three preemptions points per job.


Evolutionary Foundations of Mathematics

Abstract: We propose a simple cognitive model where qualitative and quantitative com- parisons enable animals to identify objects, associate them with their properties held in memory and make naive inference. Simple notions like equivalence re- lations, order relations are used. We then show that such processes are at the root of human mathematical reasoning by showing that the elements of totally ordered sets satisfy the Peano axioms. The process through which children learn counting is then formalized. Finally association is modeled as a Markov process leading to a stationary distribution.


An automatic volume control for preserving intelligibility

Abstract: A new method has been developed to adjust volume automatically on all audio devices equipped with at least one microphone, including mobile phones, personal media players, headsets, and car radios, that might be used in noisy environments, such as crowds, cars, and outdoors. The method uses a patented set of algorithms, implemented on the chips in such devices, to preserve constant intelligibility of speech in noisy environments, rather than constant signal-to-noise ratio. The algorithms analyze the noise background in real time and compensate only for fluctuating noise in the frequency domain and the time domain that interferes with intelligibility of speech. Advantages of this method of controlling volume include: Controlling volume without sacrificing clarity; adjusting only for persistent speech-interference noise; smoothing volume fluctuations; and eliminating static-like bursts caused by noise spikes. Practical human-factors approaches to implementing these algorithms in mobile phones are discussed.


Visualization techniques for data mining of Latur district satellite imagery

Abstract: This study presents a new visualization tool for classification of satellite imagery. Visualization of feature space allows exploration of patterns in the image data and insight into the classification process and related uncertainty. Visual Data Mining provides added value to image classifications as the user can be involved in the classification process providing increased confidence in and understanding of the results. In this study, we present a prototype visualization tool for visual data mining (VDM) of satellite imagery. The visualization tool is showcased in a classification study of highresolution imageries of Latur district in Maharashtra state of India.


IUPC: Identification and Unification of Process Constraints

Abstract: Business Process Compliance (BPC) has gained significant momentum in research and practice during the last years. Although many approaches address BPC, they mostly assume the existence of some kind of unified base of process constraints and focus on their verification over the business processes. However, it remains unclear how such an inte- grated process constraint base can be built up, even though this con- stitutes the essential prerequisite for all further compliance checks. In addition, the heterogeneity of process constraints has been neglected so far. Without identification and separation of process constraints from domain rules as well as unification of process constraints, the success- ful IT support of BPC will not be possible. In this technical report we introduce a unified representation framework that enables the identifica- tion of process constraints from domain rules and their later unification within a process constraint base. Separating process constraints from domain rules can lead to significant reduction of compliance checking effort. Unification enables consistency checks and optimizations as well as maintenance and evolution of the constraint base on the other side.


A Large Family of Multi-path Dual Congestion Control Algorithms

Abstract: The goal of traffic management is efficiently utilizing network resources via adapting of source sending rates and routes selection. Traditionally, this problem is formulated into a utilization maximization problem. The single-path routing scheme fails to react to instantaneous network congestion. Multi-path routing schemes thus have been proposed aiming at improving network efficiency. Unfortunately, the natural optimization problem to consider is concave but not strictly concave. It thus brings a huge challenge to design stable multi-path congestion control algorithms. In this paper, we propose a generalized multi-path utility maximization model to consider the problem of routes selection and flow control, and derive a family of multi-path dual congestion control algorithms. We show that the proposed algorithms are stable in the absence of delays. We also derive decentralized and scalable sufficient conditions for a particular scheme when propagation delays exist in networks. Simulations are implemented using both Matlab and NS2, on which evaluation of the proposed multi-path dual algorithms is exerted. The comparison results, between the proposed algorithms and the other two existing algorithms, show that the proposed multi-path dual algorithms with appropriate parameter settings can achieve a stable aggregated throughput while maintaining fairness among the involved users.


Contracting Graphs to Paths and Trees

Abstract: Vertex deletion and edge deletion problems play a central role in Parameterized Complexity. Examples include classical problems like Feedback Vertex Set, Odd Cycle Transversal, and Chordal Deletion. Interestingly, the study of edge contraction problems of this type from a parameterized perspective has so far been left largely unexplored. We consider two basic edge contraction problems, which we call Path-Contractibility and Tree-Contractibility. Both problems take an undirected graph $G$ and an integer $k$ as input, and the task is to determine whether we can obtain a path or an acyclic graph, respectively, by contracting at most $k$ edges of $G$. Our main contribution is an algorithm with running time $4^{k+O(\log^2 k)} + n^{O(1)}$ for Path-Contractibility and an algorithm with running time $4.88^k n^{O(1)}$ for Tree-Contractibility, based on a novel application of the color coding technique of Alon, Yuster and Zwick. Furthermore, we show that Path-Contractibility has a kernel with at most $5k+3$ vertices, while Tree-Contractibility does not have a polynomial kernel unless coNP $\subseteq$ NP/poly. We find the latter result surprising, because of the strong connection between Tree-Contractibility and Feedback Vertex Set, which is known to have a vertex kernel with size $O(k^2)$.


An Unmanned Aerial Vehicle as Human-Assistant Robotics System

Abstract: According to the American Heritage Dictionary [1],Robotics is the science or study of the technology associated with the design, fabrication, theory, and application of Robots. The term Hoverbot is also often used to refer to sophisticated mechanical devices that are remotely controlled by human beings even though these devices are not autonomous. This paper describes a remotely controlled hoverbot by installing a transmitter and receiver on both sides that is the control computer (PC) and the hoverbot respectively. Data is transmitted as signal or instruction via a infrastructure network which is converted into a command for the hoverbot that operates at a remote site.


Removing bias due to finite measurement of dynamic systems: case study on P2P systems

Abstract: Mesurer avec pr\'ecision la dynamique des graphes de terrain est une t\^ache difficile, car les propri\'et\'es observ\'ees peuvent \^etre biais\'ees pour diff\'erentes raisons, en particulier le fait que la p\'eriode de mesure soit finie. Dans ce papier, nous introduisons une m\'ethodologie g\'en\'erale qui nous permet de savoir si la fen\^etre d'observation est suffisamment longue pour caract\'eriser une propri\'et\'e donn\'ee dans n'importe quel syst\`eme dynamique. Nous appliquons cette m\'ethodologie \`a l'\'etude des dur\'ees de sessions et des dur\'ees de vie des fichiers sur deux jeux de donn\'ees P2P. Nous montrons que le comportement des propri\'et\'es est diff\'erent : pour les dur\'ees de sessions, notre m\'ethodologie nous permet de caract\'eriser avec pr\'ecision la forme de leur distribution. Par contre, pour les dur\'ees de vie des fichiers, nous montrons que cette propri\'et\'e ne peut pas \^etre caract\'eris\'ee, soit parce qu'elle n'est pas stationnaire, soit parce que la dur\'ee de notre mesure est trop courte.


Solving the Closest Vector Problem with respect to l_p Norms

Abstract: In this paper, we present a deterministic algorithm for the closest vector problem for all l_p-norms, 1 < p < \infty, and all polyhedral norms, especially for the l_1-norm and the l_{\infty}-norm. We achieve our results by introducing a new lattice problem, the lattice membership problem. We describe a deterministic algorithm for the lattice membership problem, which is a generalization of Lenstra's algorithm for integer programming. We also describe a polynomial time reduction from the closest vector problem to the lattice membership problem. This approach leads to a deterministic algorithm that solves the closest vector problem for all l_p-norms, 1 < p < \infty, in time p log_2 (r)^{O (1)} n^{(5/2+o(1))n} and for all polyhedral norms in time (s log_2 (r))^{O (1)} n^{(2+o(1))n}, where s is the number of constraints defining the polytope and r is an upper bound on the coefficients used to describe the convex body.


Investigating the Distribution of Password Choices

Abstract: In this paper we will look at the distribution with which passwords are chosen. Zipf's Law is commonly observed in lists of chosen words. Using password lists from four different on-line sources, we will investigate if Zipf's law is a good candidate for describing the frequency with which passwords are chosen. We look at a number of standard statistics, used to measure the security of password distributions, and see if modelling the data using Zipf's Law produces good estimates of these statistics. We then look at the the similarity of the password distributions from each of our sources, using guessing as a metric. This shows that these distributions provide effective tools for cracking passwords. Finally, we will show how to shape the distribution of passwords in use, by occasionally asking users to choose a different password.


Hue Histograms to Spatiotemporal Local Features for Action Recognition

Abstract: Despite the recent developments in spatiotemporal local features for action recognition in video sequences, local color information has so far been ignored. However, color has been proved an important element to the success of automated recognition of objects and scenes. In this paper we extend the space-time interest point descriptor STIP to take into account the color information on the features' neighborhood. We compare the performance of our color-aware version of STIP (which we have called HueSTIP) with the original one.


Inapproximability of NP-Complete Variants of Nash Equilibrium

Abstract: In recent work of Hazan and Krauthgamer (SICOMP 2011), it was shown that finding an $\eps$-approximate Nash equilibrium with near-optimal value in a two-player game is as hard as finding a hidden clique of size $O(\log n)$ in the random graph $G(n,1/2)$. This raises the question of whether a similar intractability holds for approximate Nash equilibrium without such constraints. We give evidence that the constraint of near-optimal value makes the problem distinctly harder: a simple algorithm finds an optimal 1/2-approximate equilibrium, while finding strictly better than 1/2-approximate equilibria is as hard as the Hidden Clique problem. This is in contrast to the unconstrained problem where more sophisticated algorithms, achieving better approximations, are known. Unlike general Nash equilibrium, which is in PPAD, optimal (maximum value) Nash equilibrium is NP-hard. We proceed to show that optimal Nash equilibrium is just one of several known NP-hard problems related to Nash equilibrium, all of which have approximate variants which are as hard as finding a planted clique. In particular, we show this for approximate variants of the following problems: finding a Nash equilibrium with value greater than $\eta$ (for any $\eta>0$, even when the best Nash equilibrium has value $1-\eta$), finding a second Nash equilibrium, and finding a Nash equilibrium with small support. Finally, we consider the complexity of approximate pure Bayes Nash equilibria in two-player games. Here we show that for general Bayesian games the problem is NP-hard. For the special case where the distribution over types is uniform, we give a quasi-polynomial time algorithm matched by a hardness result based on the Hidden Clique problem.


Noncooperative Games for Autonomous Consumer Load Balancing over Smart Grid

Abstract: Traditionally, most consumers of electricity pay for their consumptions according to a fixed rate. With the advancement of Smart Grid technologies, large-scale implementation of variable-rate metering becomes more practical. As a result, consumers will be able to control their electricity consumption in an automated fashion, where one possible scheme is to have each individual maximize its own utility as a noncooperative game. In this paper, noncooperative games are formulated among the electricity consumers in Smart Grid with two real-time pricing schemes, where the Nash equilibrium operation points are investigated for their uniqueness and load balancing properties. The first pricing scheme charges a price according to the average cost of electricity borne by the retailer and the second one charges according to a time-variant increasing-block price, where for each scheme, a zero-revenue model and a constant-rate revenue model are considered. In addition, the relationship between the studied games and certain competitive routing games from the computer networking community, known as atomic flow games, is established, for which it is shown that the proposed noncooperative game formulation falls under the class of atomic splittable flow games. The Nash equilibrium is shown to exist for four different combined cases corresponding to the two pricing schemes and the two revenue models, and is unique for three of the cases under certain conditions. It is further shown that both pricing schemes lead to similar electricity loading patterns when consumers are only interested in minimizing the electricity costs without any other profit considerations. Finally, the conditions under which the increasing-block pricing scheme is preferred over the average-cost based pricing scheme are discussed.


How to Play Unique Games against a Semi-Random Adversary

Abstract: In this paper, we study the average case complexity of the Unique Games problem. We propose a natural semi-random model, in which a unique game instance is generated in several steps. First an adversary selects a completely satisfiable instance of Unique Games, then she chooses an epsilon-fraction of all edges, and finally replaces ("corrupts") the constraints corresponding to these edges with new constraints. If all steps are adversarial, the adversary can obtain any (1-epsilon) satisfiable instance, so then the problem is as hard as in the worst case. In our semi-random model, one of the steps is random, and all other steps are adversarial. We show that known algorithms for unique games (in particular, all algorithms that use the standard SDP relaxation) fail to solve semi-random instances of Unique Games. We present an algorithm that with high probability finds a solution satisfying a (1-delta) fraction of all constraints in semi-random instances (we require that the average degree of the graph is Omega(log k). To this end, we consider a new non-standard SDP program for Unique Games, which is not a relaxation for the problem, and show how to analyze it. We present a new rounding scheme that simultaneously uses SDP and LP solutions, which we believe is of independent interest. Our result holds only for epsilon less than some absolute constant. We prove that if epsilon > 1/2, then the problem is hard in one of the models, the result assumes the 2-to-2 conjecture. Finally, we study semi-random instances of Unique Games that are at most (1-epsilon) satisfiable. We present an algorithm that with high probability, distinguishes between the case when the instance is a semi-random instance and the case when the instance is an (arbitrary) (1-delta) satisfiable instance if epsilon > c delta.


Fixed Block Compression Boosting in FM-Indexes

Abstract: A compressed full-text self-index occupies space close to that of the compressed text and simultaneously allows fast pattern matching and random access to the underlying text. Among the best compressed self-indexes, in theory and in practice, are several members of the FM-index family. In this paper, we describe new FM-index variants that combine nice theoretical properties, simple implementation and improved practical performance. Our main result is a new technique called fixed block compression boosting, which is a simpler and faster alternative to optimal compression boosting and implicit compression boosting used in previous FM-indexes.


Collective Construction of 2D Block Structures with Holes

Abstract: In this paper we present algorithms for collective construction systems in which a large number of autonomous mobile robots trans- port modular building elements to construct a desired structure. We focus on building block structures subject to some physical constraints that restrict the order in which the blocks may be attached to the structure. Specifically, we determine a partial ordering on the blocks such that if they are attached in accordance with this ordering, then (i) the structure is a single, connected piece at all intermediate stages of construction, and (ii) no block is attached between two other previously attached blocks, since such a space is too narrow for a robot to maneuver a block into it. Previous work has consider this problem for building 2D structures without holes. Here we extend this work to 2D structures with holes. We accomplish this by modeling the problem as a graph orientation problem and describe an O(n^2) algorithm for solving it. We also describe how this partial ordering may be used in a distributed fashion by the robots to coordinate their actions during the building process.


Extending and Implementing the Self-adaptive Virtual Processor for Distributed Memory Architectures

Abstract: Many-core architectures of the future are likely to have distributed memory organizations and need fine grained concurrency management to be used effectively. The Self-adaptive Virtual Processor (SVP) is an abstract concurrent programming model which can provide this, but the model and its current implementations assume a single address space shared memory. We investigate and extend SVP to handle distributed environments, and discuss a prototype SVP implementation which transparently supports execution on heterogeneous distributed memory clusters over TCP/IP connections, while retaining the original SVP programming model.


General Linearized Polynomial Interpolation and Its Applications

Abstract: In this paper, we first propose a general interpolation algorithm in a free module of a linearized polynomial ring, and then apply this algorithm to decode several important families of codes, Gabidulin codes, KK codes and MV codes. Our decoding algorithm for Gabidulin codes is different from the polynomial reconstruction algorithm by Loidreau. When applied to decode KK codes, our interpolation algorithm is equivalent to the Sudan-style list-1 decoding algorithm proposed by K/"otter and Kschischang for KK codes. The general interpolation approach is also capable of solving the interpolation problem for the list decoding of MV codes proposed by Mahdavifar and Vardy, and has a lower complexity than solving linear equations.


Courcelle's Theorem - A Game-Theoretic Approach

Abstract: Courcelle's Theorem states that every problem definable in Monadic Second-Order logic can be solved in linear time on structures of bounded treewidth, for example, by constructing a tree automaton that recognizes or rejects a tree decomposition of the structure. Existing, optimized software like the MONA tool can be used to build the corresponding tree automata, which for bounded treewidth are of constant size. Unfortunately, the constants involved can become extremely large - every quantifier alternation requires a power set construction for the automaton. Here, the required space can become a problem in practical applications. In this paper, we present a novel, direct approach based on model checking games, which avoids the expensive power set construction. Experiments with an implementation are promising, and we can solve problems on graphs where the automata-theoretic approach fails in practice.


Fairness Through Awareness

Abstract: We study fairness in classification, where individuals are classified, e.g., admitted to a university, and the goal is to prevent discrimination against individuals based on their membership in some group, while maintaining utility for the classifier (the university). The main conceptual contribution of this paper is a framework for fair classification comprising (1) a (hypothetical) task-specific metric for determining the degree to which individuals are similar with respect to the classification task at hand; (2) an algorithm for maximizing utility subject to the fairness constraint, that similar individuals are treated similarly. We also present an adaptation of our approach to achieve the complementary goal of "fair affirmative action," which guarantees statistical parity (i.e., the demographics of the set of individuals receiving any classification are the same as the demographics of the underlying population), while treating similar individuals as similarly as possible. Finally, we discuss the relationship of fairness to privacy: when fairness implies privacy, and how tools developed in the context of differential privacy may be applied to fairness.


An improved approximation algorithm for the minimum-cost subset k-connected subgraph problem

Abstract: The minimum-cost subset $k$-connected subgraph problem is a cornerstone problem in the area of network design with vertex connectivity requirements. In this problem, we are given a graph $G=(V,E)$ with costs on edges and a set of terminals $T$. The goal is to find a minimum cost subgraph such that every pair of terminals are connected by $k$ openly (vertex) disjoint paths. In this paper, we present an approximation algorithm for the subset $k$-connected subgraph problem which improves on the previous best approximation guarantee of $O(k^2\log{k})$ by Nutov (FOCS 2009). Our approximation guarantee, $\alpha(|T|)$, depends upon the number of terminals: [\alpha(|T|) \ \ =\ \ O(|T|^2) & if |T| < 2k O(k \log^2 k) & if 2k\le |T| < k^2 O(k \log k) & if |T| \ge k^2] So, when the number of terminals is {\em large enough}, the approximation guarantee improves significantly. Moreover, we show that, given an approximation algorithm for $|T|=k$, we can obtain almost the same approximation guarantee for any instances with $|T|> k$. This suggests that the hardest instances of the problem are when $|T|\approx k$.


Translation-based Constraint Answer Set Solving

Abstract: We solve constraint satisfaction problems through translation to answer set programming (ASP). Our reformulations have the property that unit-propagation in the ASP solver achieves well defined local consistency properties like arc, bound and range consistency. Experiments demonstrate the computational value of this approach.


Understanding Exhaustive Pattern Learning

Abstract: Pattern learning in an important problem in Natural Language Processing (NLP). Some exhaustive pattern learning (EPL) methods (Bod, 1992) were proved to be flawed (Johnson, 2002), while similar algorithms (Och and Ney, 2004) showed great advantages on other tasks, such as machine translation. In this article, we first formalize EPL, and then show that the probability given by an EPL model is constant-factor approximation of the probability given by an ensemble method that integrates exponential number of models obtained with various segmentations of the training data. This work for the first time provides theoretical justification for the widely used EPL algorithm in NLP, which was previously viewed as a flawed heuristic method. Better understanding of EPL may lead to improved pattern learning algorithms in future.


Symmetry Breaking for Distributed Multi-Context Systems

Abstract: Heterogeneous nonmonotonic multi-context systems (MCS) permit different logics to be used in different contexts, and link them via bridge rules. We investigate the role of symmetry detection and symmetry breaking in such systems to eliminate symmetric parts of the search space and, thereby, simplify the evaluation process. We propose a distributed algorithm that takes a local stance, i.e., computes independently the partial symmetries of a context and, in order to construct potential symmetries of the whole, combines them with those partial symmetries returned by neighbouring contexts. We prove the correctness of our methods. We instantiate such symmetry detection and symmetry breaking in a multi-context system with contexts that use answer set programs, and demonstrate computational benefit on some recently proposed benchmarks.


Communication Optimalement Stabilisante sur Canaux non Fiables et non FIFO

Abstract: A self-stabilizing protocol has the capacity to recover a legitimate behavior whatever is its initial state. The majority of works in self-stabilization assume a shared memory model or a communication using reliable and FIFO channels. In this article, we interest in self-stabilizing systems using bounded but non reliable and non FIFO channels. We propose a stabilizing communication protocol with optimal fault resilience. In more details, this protocol simulates a reliable and FIFO channel and ensures a minimal number of looses, duplications, creations, and re-ordering of messages.


Auto-Stabilisation et Confinement de Fautes Malicieuses : Optimalit\'e du Protocole min+1

Abstract: A self-stabilizing is naturally resilient to transients faults (that is, faults of finite duration). Recently, a new class of protocol appears. These protocols are self-stabilizing and are moreover resilient to a limited number of permanent faults. In this article, we interest in self-stabilizing protocols that tolerate very hard permanent faults: Byzantine faults. We introduce two new scheme of Byzantine containment in self-stabilizing systems. We show that, for the problem of BFS spanning tree construction, the well known self-stabilizing protocol min+1 provides without significant modification the best Byzantine containment with respect to these new schemes.


Synchronism vs Asynchronism in Boolean networks

Abstract: We show that synchronism can significantly impact on network behaviours, in particular by filtering unstable attractors induced by a constraint of asynchronism. We investigate and classify the different possible impacts that an addition of synchronism may have on the behaviour of a Boolean automata network. We show how these relate to some strong specific structural properties, thus supporting the idea that for most networks, synchronism only shortcuts asynchronous trajectories. We end with a discussion on the close relation that apparently exists between sensitivity to synchronism and non-monotony.


General Iteration graphs and Boolean automata circuits

Abstract: This article is set in the field of regulation networks modeled by discrete dynamical systems. It focuses on Boolean automata networks. In such networks, there are many ways to update the states of every element. When this is done deterministically, at each time step of a discretised time flow and according to a predefined order, we say that the network is updated according to block-sequential update schedule (blocks of elements are updated sequentially while, within each block, the elements are updated synchronously). Many studies, for the sake of simplicity and with some biologically motivated reasons, have concentrated on networks updated with one particular block-sequential update schedule (more often the synchronous/parallel update schedule or the sequential update schedules). The aim of this paper is to give an argument formally proven and inspired by biological considerations in favour of the fact that the choice of a particular update schedule does not matter so much in terms of the possible and likely dynamical behaviours that networks may display.


On the evolution of the instance level of DL-lite knowledge bases

Abstract: Recent papers address the issue of updating the instance level of knowledge bases expressed in Description Logic following a model-based approach. One of the outcomes of these papers is that the result of updating a knowledge base K is generally not expressible in the Description Logic used to express K. In this paper we introduce a formula-based approach to this problem, by revisiting some research work on formula-based updates developed in the '80s, in particular the WIDTIO (When In Doubt, Throw It Out) approach. We show that our operator enjoys desirable properties, including that both insertions and deletions according to such operator can be expressed in the DL used for the original KB. Also, we present polynomial time algorithms for the evolution of the instance level knowledge bases expressed in the most expressive Description Logics of the DL-lite family.


Laminar Families and Metric Embeddings: Non-bipartite Maximum Matching Problem in the Semi-Streaming Model

Abstract: In this paper, we study the non-bipartite maximum matching problem in the semi-streaming model. The maximum matching problem in the semi-streaming model has received a significant amount of attention lately. While the problem has been somewhat well solved for bipartite graphs, the known algorithms for non-bipartite graphs use $2^{\frac1\epsilon}$ passes or $n^{\frac1\epsilon}$ time to compute a $(1-\epsilon)$ approximation. In this paper we provide the first FPTAS (polynomial in $n,\frac1\epsilon$) for the problem which is efficient in both the running time and the number of passes. We also show that we can estimate the size of the matching in $O(\frac1\epsilon)$ passes using slightly superlinear space. To achieve both results, we use the structural properties of the matching polytope such as the laminarity of the tight sets and total dual integrality. The algorithms are iterative, and are based on the fractional packing and covering framework. However the formulations herein require exponentially many variables or constraints. We use laminarity, metric embeddings and graph sparsification to reduce the space required by the algorithms in between and across the iterations. This is the first use of these ideas in the semi-streaming model to solve a combinatorial optimization problem.


A Note on Parallel Algorithmic Speedup Bounds

Abstract: A parallel program can be represented as a directed acyclic graph. An important performance bound is the time to execute the critical path through the graph. We show how this performance metric is related to Amdahl speedup and the degree of average parallelism. These bounds formally exclude superlinear performance.


On Variants of the Matroid Secretary Problem

Abstract: We present a number of positive and negative results for variants of the matroid secretary problem. Most notably, we design a constant-factor competitive algorithm for the "random assignment" model where the weights are assigned randomly to the elements of a matroid, and then the elements arrive on-line in an adversarial order (extending a result of Soto \cite{Soto11}). This is under the assumption that the matroid is known in advance. If the matroid is unknown in advance, we present an $O(\log r \log n)$-approximation, and prove that a better than $O(\log n / \log \log n)$ approximation is impossible. This resolves an open question posed by Babaioff et al. \cite{BIK07}. As a natural special case, we also consider the classical secretary problem where the number of candidates $n$ is unknown in advance. If $n$ is chosen by an adversary from $\{1,...,N\}$, we provide a nearly tight answer, by providing an algorithm that chooses the best candidate with probability at least $1/(H_{N-1}+1)$ and prove that a probability better than $1/H_N$ cannot be achieved (where $H_N$ is the $N$-th harmonic number).


Automated Synthesis of Tableau Calculi

Abstract: This paper presents a method for synthesising sound and complete tableau calculi. Given a specification of the formal semantics of a logic, the method generates a set of tableau inference rules that can then be used to reason within the logic. The method guarantees that the generated rules form a calculus which is sound and constructively complete. If the logic can be shown to admit finite filtration with respect to a well-defined first-order semantics then adding a general blocking mechanism provides a terminating tableau calculus. The process of generating tableau rules can be completely automated and produces, together with the blocking mechanism, an automated procedure for generating tableau decision procedures. For illustration we show the workability of the approach for a description logic with transitive roles and propositional intuitionistic logic.


Searching Polyhedra by Rotating Half-Planes

Abstract: The Searchlight Scheduling Problem was first studied in 2D polygons, where the goal is for point guards in fixed positions to rotate searchlights to catch an evasive intruder. Here the problem is extended to 3D polyhedra, with the guards now boundary segments who rotate half-planes of illumination. After carefully detailing the 3D model, several results are established. The first is a nearly direct extension of the planar one-way sweep strategy using what we call exhaustive guards, a generalization that succeeds despite there being no well-defined notion in 3D of planar "clockwise rotation". Next follow two results: every polyhedron with r>0 reflex edges can be searched by at most r^2 suitably placed guards, whereas just r guards suffice if the polyhedron is orthogonal. (Minimizing the number of guards to search a given polyhedron is easily seen to be NP-hard.) Finally we show that deciding whether a given set of guards has a successful search schedule is strongly NP-hard, and that deciding if a given target area is searchable at all is strongly PSPACE-hard, even for orthogonal polyhedra. A number of peripheral results are proved en route to these central theorems, and several open problems remain for future work.


Learning invariant features through local space contraction

Abstract: We present in this paper a novel approach for training deterministic auto-encoders. We show that by adding a well chosen penalty term to the classical reconstruction cost function, we can achieve results that equal or surpass those attained by other regularized auto-encoders as well as denoising auto-encoders on a range of datasets. This penalty term corresponds to the Frobenius norm of the Jacobian matrix of the encoder activations with respect to the input. We show that this penalty term results in a localized space contraction which in turn yields robust features on the activation layer. Furthermore, we show how this penalty term is related to both regularized auto-encoders and denoising encoders and how it can be seen as a link between deterministic and non-deterministic auto-encoders. We find empirically that this penalty helps to carve a representation that better captures the local directions of variation dictated by the data, corresponding to a lower-dimensional non-linear manifold, while being more invariant to the vast majority of directions orthogonal to the manifold. Finally, we show that by using the learned features to initialize a MLP, we achieve state of the art classification error on a range of datasets, surpassing other methods of pre-training.


New parameterized algorithms for edge dominating set

Abstract: An edge dominating set of a graph G=(V,E) is a subset M of edges in the graph such that each edge in E-M is incident with at least one edge in M. In an instance of the parameterized edge dominating set problem we are given a graph G=(V,E) and an integer k and we are asked to decide whether G has an edge dominating set of size at most k. In this paper we show that the parameterized edge dominating set problem can be solved in O^*(2.3147^k) time and polynomial space. We show that this problem can be reduced to a quadratic kernel with O(k^3) edges.


Data Mining : A prediction of performer or underperformer using classification

Abstract: Now a day's students have a large set of data having precious information hidden. Data mining technique can help to find this hidden information. In this paper, data mining techniques name Byes classification method is used on these data to help an institution. Institutions can find those students who are consistently perform well. This study will help to institution reduce the drop put ratio to a significant level and improve the performance level of the institution.


A Data Mining view on Class Room Teaching Language

Abstract: From ancient period in India, educational institution embarked to use class room teaching. Where a teacher explains the material and students understand and learn the lesson. There is no absolute scale for measuring knowledge but examination score is one scale which shows the performance indicator of students. So it is important that appropriate material is taught but it is vital that while teaching which language is chosen, class notes must be prepared and attendance. This study analyses the impact of language on the presence of students in class room. The main idea is to find out the support, confidence and interestingness level for appropriate language and attendance in the classroom. For this purpose association rule is used.


A Meshless Method for Variational Nonrigid 2-D Shape Registration

Abstract: We present a method for nonrigid registration of 2-D geometric shapes. Our contribution is twofold. First, we extend the classic chamfer-matching energy to a variational functional. Secondly, we introduce a meshless deformation model that can handle significant high-curvature deformations. We represent 2-D shapes implicitly using distance transforms, and registration error is defined based on the shape contours' mutual distances. In addition, we model global shape deformation as an approximation blended from local deformation fields using partition-of-unity. The global deformation field is regularized by penalizing inconsistencies between local fields. The representation can be made adaptive to shape's contour, leading to registration that is both flexible and efficient. Finally, registration is achieved by minimizing a variational chamfer-energy functional combined with the consistency regularizer. We demonstrate the effectiveness of our method on a number of experiments.


Pattern matching in Lempel-Ziv compressed strings: fast, simple, and deterministic

Abstract: Countless variants of the Lempel-Ziv compression are widely used in many real-life applications. This paper is concerned with a natural modification of the classical pattern matching problem inspired by the popularity of such compression methods: given an uncompressed pattern s[1..m] and a Lempel-Ziv representation of a string t[1..N], does s occur in t? Farach and Thorup gave a randomized O(nlog^2(N/n)+m) time solution for this problem, where n is the size of the compressed representation of t. We improve their result by developing a faster and fully deterministic O(nlog(N/n)+m) time algorithm with the same space complexity. Note that for highly compressible texts, log(N/n) might be of order n, so for such inputs the improvement is very significant. A (tiny) fragment of our method can be used to give an asymptotically optimal solution for the substring hashing problem considered by Farach and Muthukrishnan.


MiCi: A Novel Micro-Level Temporal Channel Imploration for Mobile Hosts

Abstract: The exponential increase of multimedia services by the mobile users requires seamless connectivity with cost effective Quality of Service QoS provisioning. For providing such on-demand QoS, the network needs to utilize the radio channels among the Mobile Hosts (MHs) effectively. We use vector genetic algorithm VGA for temporal imploration of sharable channel(s) from the neighbouring cells to fulfill the needs of a cell. We propose a new micro-level temporal channel imploration mechanism MiCi, which promptly allocates available borrowing channel s of the neighbouring cell(s) to the needy cell. The novelty of MiCi is scalability, high availability, and on demand allocation of the channels to the desired cells. The performance of our model has been tested by simulation against a standard FCA scheme as well as a Greedy Borrowing Heuristic. In all the test cases MiCi shows promising results in comparison to both the schemes.


Preprocessing for Treewidth: A Combinatorial Analysis through Kernelization

Abstract: The notion of treewidth plays an important role in theoretical and practical studies of graph problems. It has been recognized that, especially in practical environments, when computing the treewidth of a graph it is invaluable to first apply an array of preprocessing rules that simplify and shrink it. This work seeks to prove rigorous performance guarantees for such preprocessing rules, both known and new ones, by studying them in the framework of kernelization from parameterized complexity. It is known that the NP-complete problem of determining whether a given graph G has treewidth at most k admits no polynomial-time preprocessing algorithm that reduces any input instance to size polynomial in k, unless NP is in coNP/poly and the polynomial hierarchy collapses to its third level. In this paper we therefore consider structural graph measures larger than treewidth, and determine whether efficient preprocessing can shrink the instance size to a polynomial in such a parameter value. We prove that given an instance (G,k) of treewidth we can efficiently reduce its size to O(fvs(G)^4) vertices, where fvs(G) is the size of a minimum feedback vertex set in G. We can also prove a size reduction to O(vc(G)^3) vertices, where vc(G) is the size of a minimum vertex cover. Phrased in the language of parameterized complexity, we show that Treewidth has a polynomial kernel when parameterized by the size of a given feedback vertex set, and also by the size of a vertex cover. In contrast we show that Treewidth parameterized by the vertex-deletion distance to a single clique, and Weighted Treewidth parameterized by the size of a vertex cover, do not admit polynomial kernelizations unless NP is in coNP/poly.


Data Reduction for Graph Coloring Problems

Abstract: This paper studies the kernelization complexity of graph coloring problems with respect to certain structural parameterizations of the input instances. We are interested in how well polynomial-time data reduction can provably shrink instances of coloring problems, in terms of the chosen parameter. It is well known that deciding 3-colorability is already NP-complete, hence parameterizing by the requested number of colors is not fruitful. Instead, we pick up on a research thread initiated by Cai (DAM, 2003) who studied coloring problems parameterized by the modification distance of the input graph to a graph class on which coloring is polynomial-time solvable; for example parameterizing by the number k of vertex-deletions needed to make the graph chordal. We obtain various upper and lower bounds for kernels of such parameterizations of q-Coloring, complementing Cai's study of the time complexity with respect to these parameters. Our results show that the existence of polynomial kernels for q-Coloring parameterized by the vertex-deletion distance to a graph class F is strongly related to the existence of a function f(q) which bounds the number of vertices which are needed to preserve the NO-answer to an instance of q-List-Coloring on F.


Satisfiability of Acyclic and Almost Acyclic CNF Formulas

Abstract: We show that the Satisfiability (SAT) problem for CNF formulas with {\beta}-acyclic hypergraphs can be solved in polynomial time by using a special type of Davis-Putnam resolution in which each resolvent is a subset of a parent clause. We extend this class to CNF formulas for which this type of Davis-Putnam resolution still applies and show that testing membership in this class is NP-complete. We compare the class of {\beta}-acyclic formulas and this superclass with a number of known polynomial formula classes. We then study the parameterized complexity of SAT for "almost" {\beta}-acyclic instances, using as parameter the formula's distance from being {\beta}-acyclic. As distance we use the size of a smallest strong backdoor set and the {\beta}-hypertree width. As a by-product we obtain the W[1]-hardness of SAT parameterized by the (undirected) clique-width of the incidence graph, which disproves a conjecture by Fischer, Makowsky, and Ravve.


Algorithms and Complexity Results for Persuasive Argumentation

Abstract: The study of arguments as abstract entities and their interaction as introduced by Dung (Artificial Intelligence 177, 1995) has become one of the most active research branches within Artificial Intelligence and Reasoning. A main issue for abstract argumentation systems is the selection of acceptable sets of arguments. Value-based argumentation, as introduced by Bench-Capon (J. Logic Comput. 13, 2003), extends Dung's framework. It takes into account the relative strength of arguments with respect to some ranking representing an audience: an argument is subjectively accepted if it is accepted with respect to some audience, it is objectively accepted if it is accepted with respect to all audiences. Deciding whether an argument is subjectively or objectively accepted, respectively, are computationally intractable problems. In fact, the problems remain intractable under structural restrictions that render the main computational problems for non-value-based argumentation systems tractable. In this paper we identify nontrivial classes of value-based argumentation systems for which the acceptance problems are polynomial-time tractable. The classes are defined by means of structural restrictions in terms of the underlying graphical structure of the value-based system. Furthermore we show that the acceptance problems are intractable for two classes of value-based systems that where conjectured to be tractable by Dunne (Artificial Intelligence 171, 2007).


Curved Gabor Filters for Fingerprint Image Enhancement

Abstract: Gabor filters play an important role in many application areas for the enhancement of various types of images and the extraction of Gabor features. For the purpose of enhancing curved structures in noisy images, we introduce curved Gabor filters which locally adapt their shape to the direction of flow. These curved Gabor filters enable the choice of filter parameters which increase the smoothing power without creating artifacts in the enhanced image. In this paper, curved Gabor filters are applied to the curved ridge and valley structure of low-quality fingerprint images. First, we combine two orientation field estimation methods in order to obtain a more robust estimation for very noisy images. Next, curved regions are constructed by following the respective local orientation and they are used for estimating the local ridge frequency. Lastly, curved Gabor filters are defined based on curved regions and they are applied for the enhancement of low-quality fingerprint images. Experimental results on the FVC2004 databases show improvements of this approach in comparison to state-of-the-art enhancement methods.


Quantitative Synthesis for Concurrent Programs

Abstract: We present an algorithmic method for the quantitative, performance-aware synthesis of concurrent programs. The input consists of a nondeterministic partial program and of a parametric performance model. The nondeterminism allows the programmer to omit which (if any) synchronization construct is used at a particular program location. The performance model, specified as a weighted automaton, can capture system architectures by assigning different costs to actions such as locking, context switching, and memory and cache accesses. The quantitative synthesis problem is to automatically resolve the nondeterminism of the partial program so that both correctness is guaranteed and performance is optimal. As is standard for shared memory concurrency, correctness is formalized "specification free", in particular as race freedom or deadlock freedom. For worst-case (average-case) performance, we show that the problem can be reduced to 2-player graph games (with probabilistic transitions) with quantitative objectives. While we show, using game-theoretic methods, that the synthesis problem is NEXP-complete, we present an algorithmic method and an implementation that works efficiently for concurrent programs and performance models of practical interest. We have implemented a prototype tool and used it to synthesize finite-state concurrent programs that exhibit different programming patterns, for several performance models representing different architectures.


Seeking Meaning in a Space Made out of Strokes, Radicals, Characters and Compounds

Abstract: Chinese characters can be compared to a molecular structure: a character is analogous to a molecule, radicals are like atoms, calligraphic strokes correspond to elementary particles, and when characters form compounds, they are like molecular structures. In chemistry the conjunction of all of these structural levels produces what we perceive as matter. In language, the conjunction of strokes, radicals, characters, and compounds produces meaning. But when does meaning arise? We all know that radicals are, in some sense, the basic semantic components of Chinese script, but what about strokes? Considering the fact that many characters are made by adding individual strokes to (combinations of) radicals, we can legitimately ask the question whether strokes carry meaning, or not. In this talk I will present my project of extending traditional NLP techniques to radicals and strokes, aiming to obtain a deeper understanding of the way ideographic languages model the world.


Random input helps searching predecessors

Abstract: We solve the dynamic Predecessor Problem with high probability (whp) in constant time, using only $n^{1+\delta}$ bits of memory, for any constant $\delta > 0$. The input keys are random wrt a wider class of the well studied and practically important class of $(f_1, f_2)$-smooth distributions introduced in \cite{and:mat}. It achieves O(1) whp amortized time. Its worst-case time is $O(\sqrt{\frac{\log n}{\log \log n}})$. Also, we prove whp $O(\log \log \log n)$ time using only $n^{1+ \frac{1}{\log \log n}}= n^{1+o(1)}$ bits. Finally, we show whp $O(\log \log n)$ time using O(n) space.


The maximum disjoint paths problem on multi-relations social networks

Abstract: Motivated by applications to social network analysis (SNA), we study the problem of finding the maximum number of disjoint uni-color paths in an edge-colored graph. We show the NP-hardness and the approximability of the problem, and both approximation and exact algorithms are proposed. Since short paths are much more significant in SNA, we also study the length-bounded version of the problem, in which the lengths of paths are required to be upper bounded by a fixed integer $l$. It is shown that the problem can be solved in polynomial time for $l=3$ and is NP-hard for $l\geq 4$. We also show that the problem can be approximated with ratio $(l-1)/2+\epsilon$ in polynomial time for any $\epsilon >0$. Particularly, for $l=4$, we develop an efficient 2-approximation algorithm.


Platforms for Building and Deploying Applications for Cloud Computing

Abstract: Cloud computing is rapidly emerging as a new paradigm for delivering IT services as utlity-oriented services on subscription-basis. The rapid development of applications and their deployment in Cloud computing environments in efficient manner is a complex task. In this article, we give a brief introduction to Cloud computing technology and Platform as a Service, we examine the offerings in this category, and provide the basis for helping readers to understand basic application platform opportunities in Cloud by technologies such as Microsoft Azure, Sales Force, Google App, and Aneka for Cloud computing. We demonstrate that Manjrasoft Aneka is a Cloud Application Platform (CAP) leveraging these concepts and allowing an easy development of Cloud ready applications on a Private/Public/Hybrid Cloud. Aneka CAP offers facilities for quickly developing Cloud applications and a modular platform where additional services can be easily integrated to extend the system capabilities, thus being at pace with the rapidly evolution of Cloud computing.


EMBANKS: Towards Disk Based Algorithms For Keyword-Search In Structured Databases

Abstract: In recent years, there has been a lot of interest in the field of keyword querying relational databases. A variety of systems such as DBXplorer [ACD02], Discover [HP02] and ObjectRank [BHP04] have been proposed. Another such system is BANKS, which enables data and schema browsing together with keyword-based search for relational databases. It models tuples as nodes in a graph, connected by links induced by foreign key and other relationships. The size of the database graph that BANKS uses is proportional to the sum of the number of nodes and edges in the graph. Systems such as SPIN, which search on Personal Information Networks and use BANKS as the backend, maintain a lot of information about the users' data. Since these systems run on the user workstation which have other demands of memory, such a heavy use of memory is unreasonable and if possible, should be avoided. In order to alleviate this problem, we introduce EMBANKS (acronym for External Memory BANKS), a framework for an optimized disk-based BANKS system. The complexity of this framework poses many questions, some of which we try to answer in this thesis. We demonstrate that the cluster representation proposed in EMBANKS enables in-memory processing of very large database graphs. We also present detailed experiments that show that EMBANKS can significantly reduce database load time and query execution times when compared to the original BANKS algorithms.


Testing of random matrices

Abstract: Let $n$ be a positive integer and $X = [x_{ij}]_{1 \leq i, j \leq n}$ be an $n \times n$\linebreak \noindent sized matrix of independent random variables having joint uniform distribution $$\hbox{Pr} {x_{ij} = k \hbox{for} 1 \leq k \leq n} = \frac{1}{n} \quad (1 \leq i, j \leq n) \koz. $$ A realization $\mathcal{M} = [m_{ij}]$ of $X$ is called \textit{good}, if its each row and each column contains a permutation of the numbers $1, 2,..., n$. We present and analyse four typical algorithms which decide whether a given realization is good.


Enforcing efficient equilibria in network design games via subsidies

Abstract: The efficient design of networks has been an important engineering task that involves challenging combinatorial optimization problems. Typically, a network designer has to select among several alternatives which links to establish so that the resulting network satisfies a given set of connectivity requirements and the cost of establishing the network links is as low as possible. The Minimum Spanning Tree problem, which is well-understood, is a nice example. In this paper, we consider the natural scenario in which the connectivity requirements are posed by selfish users who have agreed to share the cost of the network to be established according to a well-defined rule. The design proposed by the network designer should now be consistent not only with the connectivity requirements but also with the selfishness of the users. Essentially, the users are players in a so-called network design game and the network designer has to propose a design that is an equilibrium for this game. As it is usually the case when selfishness comes into play, such equilibria may be suboptimal. In this paper, we consider the following question: can the network designer enforce particular designs as equilibria or guarantee that efficient designs are consistent with users' selfishness by appropriately subsidizing some of the network links? In an attempt to understand this question, we formulate corresponding optimization problems and present positive and negative results.


Super-d-complexity of finite words

Abstract: In this paper we introduce and study a new complexity measure for finite words. For positive integer $d$ special scattered subwords, called super-$d$-subwords, in which the gaps are of length at least $(d-1)$, are defined. We give methods to compute super-$d$-complexity (the total number of different super-$d$-subwords) in the case of rainbow words (with pairwise different letters) by recursive algorithms, by mahematical formulas and by graph algorithms. In the case of general words, with letters from a given alphabet without any restriction, the problem of the maximum value of the super-$d$-complexity of all words of length $n$ is presented.


On scattered subword complexity

Abstract: Special scattered subwords, in which the gaps are of length from a given set, are defined. The scattered subword complexity, which is the number of such scattered subwords, is computed for rainbow words.


Primitive words and roots of words

Abstract: In the algebraic theory of codes and formal languages, the set $Q$ of all primitive words over some alphabet $\zi $ has received special interest. With this survey article we give an overview about relevant research to this topic during the last twenty years including own investigations and some new results. In Section 1 after recalling the most important notions from formal language theory we illustrate the connection between coding theory and primitive words by some facts. We define primitive words as words having only a trivial representation as the power of another word. Nonprimitive words (without the empty word) are exactly the periodic words. Every nonempty word is a power of an uniquely determined primitive word which is called the root of the former one. The set of all roots of nonempty words of a language is called the root of the language. The primitive words have interesting combinatorial properties which we consider in Section 2. In Section 3 we investigate the relationship between the set $Q$ of all primitive words over some fixed alphabet and the language classes of the Chomsky Hierarchy and the contextual languages over the same alphabet. The computational complexity of the set $Q$ and of the roots of languages are considered in Section 4. The set of all powers of the same degree of all words from a language is the power of this language. We examine the powers of languages for different sets of exponents, and especially their regularity and context-freeness, in Section 5, and the decidability of appropriate questions in Section 6. Section 7 is dedicated to several generalizations of the notions of periodicity and primitivity of words.


Arc-preserving subsequences of arc-annotated sequences

Abstract: Arc-annotated sequences are useful in representing the structural information of RNA and protein sequences. The longest arc-preserving common subsequence problem has been introduced as a framework for studying the similarity of arc-annotated sequences. In this paper, we consider arc-annotated sequences with various arc structures. We consider the longest arc preserving common subsequence problem. In particular, we show that the decision version of the 1-{\sc fragment LAPCS(crossing,chain)} and the decision version of the 0-{\sc diagonal LAPCS(crossing,chain)} are {\bf NP}-complete for some fixed alphabet $\Sigma$ such that $|\Sigma| = 2$. Also we show that if $|\Sigma| = 1$, then the decision version of the 1-{\sc fragment LAPCS(unlimited, plain)} and the decision version of the 0-{\sc diagonal LAPCS(unlimited, plain)} are {\bf NP}-complete.


Towards a Data Reduction for the Minimum Flip Supertree Problem

Abstract: In computational phylogenetics, the problem of constructing a supertree of a given set of rooted input trees can be formalized in different ways, to cope with contradictory information in the input. We consider the Minimum Flip Supertree problem, where the input trees are transformed into a 0/1/?-matrix, such that each row represents a taxon, and each column represents an inner node of one of the input trees. Our goal is to find a perfect phylogeny for the input matrix requiring a minimum number of 0/1-flips, that is, corrections of 0/1-entries in the matrix. The problem is known to be NP-complete. Here, we present a parameterized data reduction with polynomial running time. The data reduction guarantees that the reduced instance has a solution if and only if the original instance has a solution. We then make our data reduction parameter-independent by using upper bounds. This allows us to preprocess an instance, and to solve the reduced instance with an arbitrary method. Different from an existing data reduction for the consensus tree problem, our reduction allows us to draw conclusions about certain entries in the matrix. We have implemented and evaluated our data reduction. Unfortunately, we find that the Minimum Flip Supertree problem is also hard in practice: The amount of information that can be derived during data reduction diminishes as instances get more "complicated", and running times for "complicated" instances quickly become prohibitive. Still, our method offers another route of attack for this relevant phylogenetic problem.


Tiled QR factorization algorithms

Abstract: This work revisits existing algorithms for the QR factorization of rectangular matrices composed of p-by-q tiles, where p >= q. Within this framework, we study the critical paths and performance of algorithms such as Sameh and Kuck, Modi and Clarke, Greedy, and those found within PLASMA. Although neither Modi and Clarke nor Greedy is optimal, both are shown to be asymptotically optimal for all matrices of size p = q^2 f(q), where f is any function such that \lim_{+\infty} f= 0. This novel and important complexity result applies to all matrices where p and q are proportional, p = \lambda q, with \lambda >= 1, thereby encompassing many important situations in practice (least squares). We provide an extensive set of experiments that show the superiority of the new algorithms for tall matrices.


The 3-satisfiability problem

Abstract: We present a deterministic polynomial-time algorithm that solves the 3-satisfiability problem.


Aperiodic logarithmic signatures

Abstract: In this paper we propose a method to construct logarithmic signatures which are not amalgamated transversal and further do not even have a periodic block. The latter property was crucial for the successful attack on the system MST3 by Blackburn et al. [1]. The idea for our construction is based on the theory in Szab\'o's book about group factorizations [12].


Parallel Breadth-First Search on Distributed Memory Systems

Abstract: Data-intensive, graph-based computations are pervasive in several scientific applications, and are known to to be quite challenging to implement on distributed memory systems. In this work, we explore the design space of parallel algorithms for Breadth-First Search (BFS), a key subroutine in several graph algorithms. We present two highly-tuned parallel approaches for BFS on large parallel systems: a level-synchronous strategy that relies on a simple vertex-based partitioning of the graph, and a two-dimensional sparse matrix-partitioning-based approach that mitigates parallel communication overhead. For both approaches, we also present hybrid versions with intra-node multithreading. Our novel hybrid two-dimensional algorithm reduces communication times by up to a factor of 3.5, relative to a common vertex based approach. Our experimental study identifies execution regimes in which these approaches will be competitive, and we demonstrate extremely high performance on leading distributed-memory parallel systems. For instance, for a 40,000-core parallel execution on Hopper, an AMD Magny-Cours based system, we achieve a BFS performance rate of 17.8 billion edge visits per second on an undirected graph of 4.3 billion vertices and 68.7 billion edges with skewed degree distribution.


Performance Analysis of AODV under Black Hole Attack through Use of OPNET Simulator

Abstract: Mobile ad hoc networks (MANETs) are dynamic wireless networks without any infrastructure. These networks are weak against many types of attacks. One of these attacks is the black hole. In this attack, a malicious node advertises itself as having freshest or shortest path to specific node to absorb packets to itself. The effect of black hole attack on ad hoc network using AODV as a routing protocol will be examined in this research. Furthermore, we investigate solution for increasing security in these networks. Simulation results using OPNET simulator depict that packet delivery ratio in the presence of malicious nodes, reduces notably.


Polynomial Estimators for High Frequency Moments

Abstract: We present an algorithm for computing $F_p$, the $p$th moment of an $n$-dimensional frequency vector of a data stream, for $2 < p < \log (n) $, to within $1\pm \epsilon$ factors, $\epsilon \in [n^{-1/p},1]$ with high constant probability. Let $m$ be the number of stream records and $M$ be the largest magnitude of a stream update. The algorithm uses space in bits $$ O(p^2\epsilon^{-2}n^{1-2/p}E(p,n) \log (n) \log (nmM)/\min(\log (n),\epsilon^{4/p-2}))$$ where, $E(p,n) = (1-2/p)^{-1}(1-n^{-4(1-2/p})$. Here $E(p,n)$ is $ O(1)$ for $p = 2+\Omega(1)$ and $ O(\log n)$ for $p = 2 + O(1/\log (n)$. This improves upon the space required by current algorithms \cite{iw:stoc05,bgks:soda06,ako:arxiv10,bo:arxiv10} by a factor of at least $\Omega(\epsilon^{-4/p} \min(\log (n), \epsilon^{4/p-2}))$. The update time is $O(\log (n))$. We use a new technique for designing estimators for functions of the form $\psi(\expect{X})$, where, $X$ is a random variable and $\psi$ is a smooth function, based on a low-degree Taylor polynomial expansion of $\psi(\expect{X})$ around an estimate of $\expect{X}$.


ChemXSeer Digital Library Gaussian Search

Abstract: We report on the Gaussian file search system designed as part of the ChemXSeer digital library. Gaussian files are produced by the Gaussian software [4], a software package used for calculating molecular electronic structure and properties. The output files are semi-structured, allowing relatively easy access to the Gaussian attributes and metadata. Our system is currently capable of searching Gaussian documents using a boolean combination of atoms (chemical elements) and attributes. We have also implemented a faceted browsing feature on three important Gaussian attribute types - Basis Set, Job Type and Method Used. The faceted browsing feature enables a user to view and process a smaller, filtered subset of documents.


Boolean Equi-propagation for Optimized SAT Encoding

Abstract: We present an approach to propagation based solving, Boolean equi-propagation, where constraints are modelled as propagators of information about equalities between Boolean literals. Propagation based solving applies this information as a form of partial evaluation resulting in optimized SAT encodings. We demonstrate for a variety of benchmarks that our approach results in smaller CNF encodings and leads to speed-ups in solving times.


Minimum cell connection and separation in line segment arrangements

Abstract: We study the complexity of the following cell connection and separation problems in segment arrangements. Given a set of straight-line segments in the plane and two points $a$ and $b$ in different cells of the induced arrangement: (i) compute the minimum number of segments one needs to remove so that there is a path connecting $a$ to $b$ that does not intersect any of the remaining segments; (ii) compute the minimum number of segments one needs to remove so that the arrangement induced by the remaining segments has a single cell; (iii) compute the minimum number of segments one needs to retain so that any path connecting $a$ to $b$ intersects some of the retained segments. We show that problems (i) and (ii) are NP-hard and discuss some special, tractable cases. Most notably, we provide a linear-time algorithm for a variant of problem (i) where the path connecting $a$ to $b$ must stay inside a given polygon $P$ with a constant number of holes, the segments are contained in $P$, and the endpoints of the segments are on the boundary of $P$. For problem (iii) we provide a cubic-time algorithm.


Business Mode Selection in Digital Content Markets

Abstract: In this paper, we consider a two-sided digital content market, and study which of the two business modes, i.e., Business-to-Customer (B2C) and Customer-to-Customer (C2C), should be selected and when it should be selected. The considered market is managed by an intermediary, through which content producers can sell their contents to consumers. The intermediary can select B2C or C2C as its business mode, while the content producers and consumers are rational agents that maximize their own utilities. The content producers are differentiated by their content qualities. First, given the intermediary's business mode, we show that there always exists a unique equilibrium at which neither the content producers nor the consumers change their decisions. Moreover, if there are a sufficiently large number of consumers, then the decision process based on the content producers' naive expectation can reach the unique equilibrium. Next, we show that in a market with only one intermediary, C2C should be selected if the intermediary aims at maximizing its profit. Then, by considering a particular scenario where the contents are not highly substitutable, we prove that when the intermediary chooses to maximize the social welfare, C2C should be selected if the content producers can receive sufficient compensation for content sales, and B2C should be selected otherwise.


Temporal Second Difference Traces

Abstract: Q-learning is a reliable but inefficient off-policy temporal-difference method, backing up reward only one step at a time. Replacing traces, using a recency heuristic, are more efficient but less reliable. In this work, we introduce model-free, off-policy temporal difference methods that make better use of experience than Watkins' Q(\lambda). We introduce both Optimistic Q(\lambda) and the temporal second difference trace (TSDT). TSDT is particularly powerful in deterministic domains. TSDT uses neither recency nor frequency heuristics, storing (s,a,r,s',\delta) so that off-policy updates can be performed after apparently suboptimal actions have been taken. There are additional advantages when using state abstraction, as in MAXQ. We demonstrate that TSDT does significantly better than both Q-learning and Watkins' Q(\lambda) in a deterministic cliff-walking domain. Results in a noisy cliff-walking domain are less advantageous for TSDT, but demonstrate the efficacy of Optimistic Q(\lambda), a replacing trace with some of the advantages of TSDT.


Finding Light Spanners in Bounded Pathwidth Graphs

Abstract: Given an edge-weighted graph $G$ and $\epsilon>0$, a $(1+\epsilon)$-spanner is a spanning subgraph $G'$ whose shortest path distances approximate those of $G$ within a $(1+\epsilon)$ factor. If $G$ is from certain minor-closed graph families (at least bounded genus graphs and apex graphs), then we know that light spanners exist. That is, we can compute a $(1+\epsilon)$-spanner $G'$ with total edge weight at most a constant times the weight of a minimum spanning tree. This constant may depend on $\epsilon$ and the graph family, but not on the particular graph $G$ nor on its edge weighting. For weighted graphs from several minor-closed graph families, the existence of light spanners has been essential in the design of approximation schemes for the metric TSP (the traveling salesman problem) and some similar problems. In this paper we make some progress towards the conjecture that light spanners exist for every minor-closed graph family. In particular, we show that they exist for graphs with bounded pathwidth. We do this via the construction of light enough monotone spanning trees in such graphs.


Rounding Semidefinite Programming Hierarchies via Global Correlation

Abstract: We show a new way to round vector solutions of semidefinite programming (SDP) hierarchies into integral solutions, based on a connection between these hierarchies and the spectrum of the input graph. We demonstrate the utility of our method by providing a new SDP-hierarchy based algorithm for constraint satisfaction problems with 2-variable constraints (2-CSP's). More concretely, we show for every 2-CSP instance I a rounding algorithm for r rounds of the Lasserre SDP hierarchy for I that obtains an integral solution that is at most \eps worse than the relaxation's value (normalized to lie in [0,1]), as long as r > k\cdot\rank_{\geq \theta}(\Ins)/\poly(\e) \;, where k is the alphabet size of I, $\theta=\poly(\e/k)$, and $\rank_{\geq \theta}(\Ins)$ denotes the number of eigenvalues larger than $\theta$ in the normalized adjacency matrix of the constraint graph of $\Ins$. In the case that $\Ins$ is a \uniquegames instance, the threshold $\theta$ is only a polynomial in $\e$, and is independent of the alphabet size. Also in this case, we can give a non-trivial bound on the number of rounds for \emph{every} instance. In particular our result yields an SDP-hierarchy based algorithm that matches the performance of the recent subexponential algorithm of Arora, Barak and Steurer (FOCS 2010) in the worst case, but runs faster on a natural family of instances, thus further restricting the set of possible hard instances for Khot's Unique Games Conjecture. Our algorithm actually requires less than the $n^{O(r)}$ constraints specified by the $r^{th}$ level of the Lasserre hierarchy, and in some cases $r$ rounds of our program can be evaluated in time $2^{O(r)}\poly(n)$.


Performance Evaluation of Statistical Approaches for Text Independent Speaker Recognition Using Source Feature

Abstract: This paper introduces the performance evaluation of statistical approaches for TextIndependent speaker recognition system using source feature. Linear prediction LP residual is used as a representation of excitation information in speech. The speaker-specific information in the excitation of voiced speech is captured using statistical approaches such as Gaussian Mixture Models GMMs and Hidden Markov Models HMMs. The decrease in the error during training and recognizing speakers during testing phase close to 100 percent accuracy demonstrates that the excitation component of speech contains speaker-specific information and is indeed being effectively captured by continuous Ergodic HMM than GMM. The performance of the speaker recognition system is evaluated on GMM and 2 state ergodic HMM with different mixture components and test speech duration. We demonstrate the speaker recognition studies on TIMIT database for both GMM and Ergodic HMM.


Secured Message Transmission in Mobile AD HOC Networks through Identification and Removal of Byzantine Failures

Abstract: The emerging need for mobile ad hoc networks and secured data transmission phase is of crucial importance depending upon the environments like military. In this paper, a new way to improve the reliability of message transmission is presented. In the open collaborative MANET environment, any node can maliciously or selfishly disrupt and deny communication of other nodes. Dynamic changing topology makes it hard to determine the adversary nodes that affect the communication in MANET. An SMT protocol provides a way to secure message transmission by dispersing the message among several paths with minimal redundancy. The multiple routes selected are known as APS -Active Path Set. This paper describes a technique for fault discovery process to identify Byzantine failures which include nodes that drop, modify, or mis-route packets in an attempt to disrupt the routing service. An adaptive probing technique detects a malicious link through binary search and according to the nodes behavior, these links are avoided in the active path by multiplicatively increasing their weights. The proposed scheme provides secure communication even with increased number of adversaries.


Positive Semidefinite Metric Learning Using Boosting-like Algorithms

Abstract: The success of many machine learning and pattern recognition methods relies heavily upon the identification of an appropriate distance metric on the input data. It is often beneficial to learn such a metric from the input training data, instead of using a default one such as the Euclidean distance. In this work, we propose a boosting-based technique, termed BoostMetric, for learning a quadratic Mahalanobis distance metric. Learning a valid Mahalanobis distance metric requires enforcing the constraint that the matrix parameter to the metric remains positive definite. Semidefinite programming is often used to enforce this constraint, but does not scale well and easy to implement. BoostMetric is instead based on the observation that any positive semidefinite matrix can be decomposed into a linear combination of trace-one rank-one matrices. BoostMetric thus uses rank-one positive semidefinite matrices as weak learners within an efficient and scalable boosting-based learning process. The resulting methods are easy to implement, efficient, and can accommodate various types of constraints. We extend traditional boosting algorithms in that its weak learner is a positive semidefinite matrix with trace and rank being one rather than a classifier or regressor. Experiments on various datasets demonstrate that the proposed algorithms compare favorably to those state-of-the-art methods in terms of classification accuracy and running time.


Bayesian approach for near-duplicate image detection

Abstract: In this paper we propose a bayesian approach for near-duplicate image detection, and investigate how different probabilistic models affect the performance obtained. The task of identifying an image whose metadata are missing is often demanded for a myriad of applications: metadata retrieval in cultural institutions, detection of copyright violations, investigation of latent cross-links in archives and libraries, duplicate elimination in storage management, etc. The majority of current solutions are based either on voting algorithms, which are very precise, but expensive; either on the use of visual dictionaries, which are efficient, but less precise. Our approach, uses local descriptors in a novel way, which by a careful application of decision theory, allows a very fine control of the compromise between precision and efficiency. In addition, the method attains a great compromise between those two axes, with more than 99% accuracy with less than 10 database operations.


Multiple-Source Single-Sink Maximum Flow in Directed Planar Graphs in O(diameter*n*log(n)) Time

Abstract: We develop a new technique for computing maximum flow in directed planar graphs with multiple sources and a single sink that significantly deviates from previously known techniques for flow problems. This gives rise to an O(diameter*n*log(n)) algorithm for the problem.