Focalization and phase models for classical extensions of non-associative Lambek calculus

Abstract: Lambek's non-associative syntactic calculus (NL) excels in its resource consciousness: the usual structural rules for weakening, contraction, exchange and even associativity are all dropped. Recently, there have been proposals for conservative extensions dispensing with NL's intuitionistic bias towards sequents with single conclusions: De Groote and Lamarche's classical non-associative Lambek calculus (CNL) and the Lambek-Grishin calculus (LG) of Moortgat and associates. We demonstrate Andreoli's focalization property for said proposals: a normalization result for Cut-free sequent derivations identifying to a large extent those differing only by trivial rule permutations. In doing so, we proceed from a `uniform' sequent presentation, deriving CNL from LG through the addition of structural rules. The normalization proof proceeds by the construction of syntactic phase models wherein every `truth' has a focused proof, similar to work of Okada and of Herbelin and Lee.


Why Does Flow Director Cause Packet Reordering?

Abstract: Intel Ethernet Flow Director is an advanced network interface card (NIC) technology. It provides the benefits of parallel receive processing in multiprocessing environments and can automatically steer incoming network data to the same core on which its application process resides. However, our analysis and experiments show that Flow Director cannot guarantee in-order packet delivery in multiprocessing environments. Packet reordering causes various negative impacts. E.g., TCP performs poorly with severe packet reordering. In this paper, we use a simplified model to analyze why Flow Director can cause packet reordering. Our experiments verify our analysis.


A Transport-Friendly NIC for Multicore/Multiprocessor Systems

Abstract: Receive side scaling (RSS) is a network interface card (NIC) technology. It provides the benefits of parallel receive processing in multiprocessing environments. However, existing RSS-enabled NICs lack a critical data steering mechanism that would automatically steer incoming network data to the same core on which its application process resides. This absence causes inefficient cache usage if an application is not running on the core on which RSS has scheduled the received traffic to be processed. In Linux systems, it cannot even ensure that packets in a TCP flow are processed by a single core, even if the interrupts for the flow are pinned to a specific core. This results in degraded performance. In this paper, we develop such a data steering mechanism in the NIC for multicore or multiprocessor systems. This data steering mechanism is mainly targeted at TCP, but it can be extended to other transport layer protocols. We term a NIC with such a data steering mechanism "A Transport Friendly NIC" (A-TFN). Experimental results have proven the effectiveness of A-TFN in accelerating TCP/IP performance.


Selective Memoization

Abstract: This paper presents language techniques for applying memoization selectively. The techniques provide programmer control over equality, space usage, and identification of precise dependences so that memoization can be applied according to the needs of an application. Two key properties of the approach are that it accepts and efficient implementation and yields programs whose performance can be analyzed using standard analysis techniques. We describe our approach in the context of a functional language called MFL and an implementation as a Standard ML library. The MFL language employs a modal type system to enable the programmer to express programs that reveal their true data dependences when executed. We prove that the MFL language is sound by showing that that MFL programs yield the same result as they would with respect to a standard, non-memoizing semantics. The SML implementation cannot support the modal type system of MFL statically but instead employs run-time checks to ensure correct usage of primitives.


From Boolean Functional Equations to Control Software

Abstract: Many software as well digital hardware automatic synthesis methods define the set of implementations meeting the given system specifications with a boolean relation K. In such a context a fundamental step in the software (hardware) synthesis process is finding effective solutions to the functional equation defined by K. This entails finding a (set of) boolean function(s) F (typically represented using OBDDs, Ordered Binary Decision Diagrams) such that: 1) for all x for which K is satisfiable, K(x, F(x)) = 1 holds; 2) the implementation of F is efficient with respect to given implementation parameters such as code size or execution time. While this problem has been widely studied in digital hardware synthesis, little has been done in a software synthesis context. Unfortunately the approaches developed for hardware synthesis cannot be directly used in a software context. This motivates investigation of effective methods to solve the above problem when F has to be implemented with software. In this paper we present an algorithm that, from an OBDD representation for K, generates a C code implementation for F that has the same size as the OBDD for F and a WCET (Worst Case Execution Time) at most O(nr), being n = |x| the number of arguments of functions in F and r the number of functions in F.


A Consistent Semantics of Self-Adjusting Computation

Abstract: This paper presents a semantics of self-adjusting computation and proves that the semantics are correct and consistent. The semantics integrate change propagation with the classic idea of memoization to enable reuse of computations under mutation to memory. During evaluation, reuse of a computation via memoization triggers a change propagation that adjusts the reused computation to reflect the mutated memory. Since the semantics integrate memoization and change-propagation, it involves both non-determinism (due to memoization) and mutation (due to change propagation). Our consistency theorem states that the non-determinism is not harmful: any two evaluations of the same program starting at the same state yield the same result. Our correctness theorem states that mutation is not harmful: self-adjusting programs are consistent with purely functional programming. We formalize the semantics and their meta-theory in the LF logical framework and machine check our proofs using Twelf.


Learning unbelievable marginal probabilities

Abstract: Loopy belief propagation performs approximate inference on graphical models with loops. One might hope to compensate for the approximation by adjusting model parameters. Learning algorithms for this purpose have been explored previously, and the claim has been made that every set of locally consistent marginals can arise from belief propagation run on a graphical model. On the contrary, here we show that many probability distributions have marginals that cannot be reached by belief propagation using any set of model parameters or any learning algorithm. We call such marginals `unbelievable.' This problem occurs whenever the Hessian of the Bethe free energy is not positive-definite at the target marginals. All learning algorithms for belief propagation necessarily fail in these cases, producing beliefs or sets of beliefs that may even be worse than the pre-learning approximation. We then show that averaging inaccurate beliefs, each obtained from belief propagation using model parameters perturbed about some learned mean values, can achieve the unbelievable marginals.


Submodular Functions Are Noise Stable

Abstract: We show that all non-negative submodular functions have high {\em noise-stability}. As a consequence, we obtain a polynomial-time learning algorithm for this class with respect to any product distribution on $\{-1,1\}^n$ (for any constant accuracy parameter $\epsilon$). Our algorithm also succeeds in the agnostic setting. Previous work on learning submodular functions required either query access or strong assumptions about the types of submodular functions to be learned (and did not hold in the agnostic setting).


Extreme-Value Theorems for Optimal Multidimensional Pricing

Abstract: We provide a near-optimal, computationally efficient algorithm for the unit-demand pricing problem, where a seller wants to price n items to optimize revenue against a unit-demand buyer whose values for the items are independently drawn from known distributions. For any chosen accuracy eps>0 and item values bounded in [0,1], our algorithm achieves revenue that is optimal up to an additive error of at most eps, in polynomial time. For values sampled from Monotone Hazard Rate (MHR) distributions, we achieve a (1-eps)-fraction of the optimal revenue in polynomial time, while for values sampled from regular distributions the same revenue guarantees are achieved in quasi-polynomial time. Our algorithm for bounded distributions applies probabilistic techniques to understand the statistical properties of revenue distributions, obtaining a reduction in the search space of the algorithm via dynamic programming. Adapting this approach to MHR and regular distributions requires the proof of novel extreme value theorems for such distributions. As a byproduct, our techniques establish structural properties of approximately-optimal and near-optimal solutions. We show that, for values independently distributed according to MHR distributions, pricing all items at the same price achieves a constant fraction of the optimal revenue. Moreover, for all eps >0, g(1/eps) distinct prices suffice to obtain a (1-eps)-fraction of the optimal revenue, where g(1/eps) is quadratic in 1/eps and independent of n. Similarly, for all eps>0 and n>0, at most g(1/(eps log n)) distinct prices suffice if the values are independently distributed according to regular distributions, where g() is a polynomial function. Finally, when the values are i.i.d. from some MHR distribution, we show that, if n is a sufficiently large function of 1/eps, a single price suffices to achieve a (1-eps)-fraction of the optimal revenue.


The Impact of Mutation Rate on the Computation Time of Evolutionary Dynamic Optimization

Abstract: Mutation has traditionally been regarded as an important operator in evolutionary algorithms. In particular, there have been many experimental studies which showed the effectiveness of adapting mutation rates for various static optimization problems. Given the perceived effectiveness of adaptive and self-adaptive mutation for static optimization problems, there have been speculations that adaptive and self-adaptive mutation can benefit dynamic optimization problems even more since adaptation and self-adaptation are capable of following a dynamic environment. However, few theoretical results are available in analyzing rigorously evolutionary algorithms for dynamic optimization problems. It is unclear when adaptive and self-adaptive mutation rates are likely to be useful for evolutionary algorithms in solving dynamic optimization problems. This paper provides the first rigorous analysis of adaptive mutation and its impact on the computation times of evolutionary algorithms in solving certain dynamic optimization problems. More specifically, for both individual-based and population-based EAs, we have shown that any time-variable mutation rate scheme will not significantly outperform a fixed mutation rate on some dynamic optimization problem instances. The proofs also offer some insights into conditions under which any time-variable mutation scheme is unlikely to be useful and into the relationships between the problem characteristics and algorithmic features (e.g., different mutation schemes).


The Complexity of Reasoning about Spatial Congruence

Abstract: In the recent literature of Artificial Intelligence, an intensive research effort has been spent, for various algebras of qualitative relations used in the representation of temporal and spatial knowledge, on the problem of classifying the computational complexity of reasoning problems for subsets of algebras. The main purpose of these researches is to describe a restricted set of maximal tractable subalgebras, ideally in an exhaustive fashion with respect to the hosting algebras. In this paper we introduce a novel algebra for reasoning about Spatial Congruence, show that the satisfiability problem in the spatial algebra MC-4 is NP-complete, and present a complete classification of tractability in the algebra, based on the individuation of three maximal tractable subclasses, one containing the basic relations. The three algebras are formed by 14, 10 and 9 relations out of 16 which form the full algebra.


Infinite-Horizon Policy-Gradient Estimation

Abstract: Gradient-based approaches to direct policy search in reinforcement learning have received much recent attention as a means to solve problems of partial observability and to avoid some of the problems associated with policy degradation in value-function methods. In this paper we introduce GPOMDP, a simulation-based algorithm for generating a {\em biased} estimate of the gradient of the {\em average reward} in Partially Observable Markov Decision Processes (POMDPs) controlled by parameterized stochastic policies. A similar algorithm was proposed by Kimura, Yamamura, and Kobayashi (1995). The algorithm's chief advantages are that it requires storage of only twice the number of policy parameters, uses one free parameter $\beta\in [0,1)$ (which has a natural interpretation in terms of bias-variance trade-off), and requires no knowledge of the underlying state. We prove convergence of GPOMDP, and show how the correct choice of the parameter $\beta$ is related to the {\em mixing time} of the controlled POMDP. We briefly describe extensions of GPOMDP to controlled Markov chains, continuous state, observation and control spaces, multiple-agents, higher-order derivatives, and a version for training stochastic policies with internal states. In a companion paper (Baxter, Bartlett, & Weaver, 2001) we show how the gradient estimates generated by GPOMDP can be used in both a traditional stochastic gradient algorithm and a conjugate-gradient procedure to find local optima of the average reward


Experiments with Infinite-Horizon, Policy-Gradient Estimation

Abstract: In this paper, we present algorithms that perform gradient ascent of the average reward in a partially observable Markov decision process (POMDP). These algorithms are based on GPOMDP, an algorithm introduced in a companion paper (Baxter and Bartlett, this volume), which computes biased estimates of the performance gradient in POMDPs. The algorithm's chief advantages are that it uses only one free parameter beta, which has a natural interpretation in terms of bias-variance trade-off, it requires no knowledge of the underlying state, and it can be applied to infinite state, control and observation spaces. We show how the gradient estimates produced by GPOMDP can be used to perform gradient ascent, both with a traditional stochastic-gradient algorithm, and with an algorithm based on conjugate-gradients that utilizes gradient information to bracket maxima in line searches. Experimental results are presented illustrating both the theoretical results of (Baxter and Bartlett, this volume) on a toy problem, and practical aspects of the algorithms on a number of more realistic problems.


Reasoning within Fuzzy Description Logics

Abstract: Description Logics (DLs) are suitable, well-known, logics for managing structured knowledge. They allow reasoning about individuals and well defined concepts, i.e., set of individuals with common properties. The experience in using DLs in applications has shown that in many cases we would like to extend their capabilities. In particular, their use in the context of Multimedia Information Retrieval (MIR) leads to the convincement that such DLs should allow the treatment of the inherent imprecision in multimedia object content representation and retrieval. In this paper we will present a fuzzy extension of ALC, combining Zadeh's fuzzy logic with a classical DL. In particular, concepts becomes fuzzy and, thus, reasoning about imprecise concepts is supported. We will define its syntax, its semantics, describe its properties and present a constraint propagation calculus for reasoning in it.


An Analysis of Reduced Error Pruning

Abstract: Top-down induction of decision trees has been observed to suffer from the inadequate functioning of the pruning phase. In particular, it is known that the size of the resulting tree grows linearly with the sample size, even though the accuracy of the tree does not improve. Reduced Error Pruning is an algorithm that has been used as a representative technique in attempts to explain the problems of decision tree learning. In this paper we present analyses of Reduced Error Pruning in three different settings. First we study the basic algorithmic properties of the method, properties that hold independent of the input decision tree and pruning examples. Then we examine a situation that intuitively should lead to the subtree under consideration to be replaced by a leaf node, one in which the class label and attribute values of the pruning examples are independent of each other. This analysis is conducted under two different assumptions. The general analysis shows that the pruning probability of a node fitting pure noise is bounded by a function that decreases exponentially as the size of the tree grows. In a specific analysis we assume that the examples are distributed uniformly to the tree. This assumption lets us approximate the number of subtrees that are pruned because they do not receive any pruning examples. This paper clarifies the different variants of the Reduced Error Pruning algorithm, brings new insight to its algorithmic properties, analyses the algorithm with less imposed assumptions than before, and includes the previously overlooked empty subtrees to the analysis.


GIB: Imperfect Information in a Computationally Challenging Game

Abstract: This paper investigates the problems arising in the construction of a program to play the game of contract bridge. These problems include both the difficulty of solving the game's perfect information variant, and techniques needed to address the fact that bridge is not, in fact, a perfect information game. GIB, the program being described, involves five separate technical advances: partition search, the practical application of Monte Carlo techniques to realistic problems, a focus on achievable sets to solve problems inherent in the Monte Carlo approach, an extension of alpha-beta pruning from total orders to arbitrary distributive lattices, and the use of squeaky wheel optimization to find approximately optimal solutions to cardplay problems. GIB is currently believed to be of approximately expert caliber, and is currently the strongest computer bridge program in the world.


Domain Filtering Consistencies

Abstract: Enforcing local consistencies is one of the main features of constraint reasoning. Which level of local consistency should be used when searching for solutions in a constraint network is a basic question. Arc consistency and partial forms of arc consistency have been widely studied, and have been known for sometime through the forward checking or the MAC search algorithms. Until recently, stronger forms of local consistency remained limited to those that change the structure of the constraint graph, and thus, could not be used in practice, especially on large networks. This paper focuses on the local consistencies that are stronger than arc consistency, without changing the structure of the network, i.e., only removing inconsistent values from the domains. In the last five years, several such local consistencies have been proposed by us or by others. We make an overview of all of them, and highlight some relations between them. We compare them both theoretically and experimentally, considering their pruning efficiency and the time required to enforce them.


Policy Recognition in the Abstract Hidden Markov Model

Abstract: In this paper, we present a method for recognising an agent's behaviour in dynamic, noisy, uncertain domains, and across multiple levels of abstraction. We term this problem on-line plan recognition under uncertainty and view it generally as probabilistic inference on the stochastic process representing the execution of the agent's plan. Our contributions in this paper are twofold. In terms of probabilistic inference, we introduce the Abstract Hidden Markov Model (AHMM), a novel type of stochastic processes, provide its dynamic Bayesian network (DBN) structure and analyse the properties of this network. We then describe an application of the Rao-Blackwellised Particle Filter to the AHMM which allows us to construct an efficient, hybrid inference method for this model. In terms of plan recognition, we propose a novel plan recognition framework based on the AHMM as the plan execution model. The Rao-Blackwellised hybrid inference for AHMM can take advantage of the independence properties inherent in a model of plan execution, leading to an algorithm for online probabilistic plan recognition that scales well with the number of levels in the plan hierarchy. This illustrates that while stochastic models for plan execution can be complex, they exhibit special structures which, if exploited, can lead to efficient plan recognition algorithms. We demonstrate the usefulness of the AHMM framework via a behaviour recognition system in a complex spatial environment using distributed video surveillance data.


Computational Approach to Anaphora Resolution in Spanish Dialogues

Abstract: This paper presents an algorithm for identifying noun-phrase antecedents of pronouns and adjectival anaphors in Spanish dialogues. We believe that anaphora resolution requires numerous sources of information in order to find the correct antecedent of the anaphor. These sources can be of different kinds, e.g., linguistic information, discourse/dialogue structure information, or topic information. For this reason, our algorithm uses various different kinds of information (hybrid information). The algorithm is based on linguistic constraints and preferences and uses an anaphoric accessibility space within which the algorithm finds the noun phrase. We present some experiments related to this algorithm and this space using a corpus of 204 dialogues. The algorithm is implemented in Prolog. According to this study, 95.9% of antecedents were located in the proposed space, a precision of 81.3% was obtained for pronominal anaphora resolution, and 81.5% for adjectival anaphora.


The FF Planning System: Fast Plan Generation Through Heuristic Search

Abstract: We describe and evaluate the algorithmic techniques that are used in the FF planning system. Like the HSP system, FF relies on forward state space search, using a heuristic that estimates goal distances by ignoring delete lists. Unlike HSP's heuristic, our method does not assume facts to be independent. We introduce a novel search strategy that combines hill-climbing with systematic search, and we show how other powerful heuristic information can be extracted and used to prune the search space. FF was the most successful automatic planner at the recent AIPS-2000 planning competition. We review the results of the competition, give data for other benchmark domains, and investigate the reasons for the runtime performance of FF compared to HSP.


Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System

Abstract: Designing the dialogue policy of a spoken dialogue system involves many nontrivial choices. This paper presents a reinforcement learning approach for automatically optimizing a dialogue policy, which addresses the technical challenges in applying reinforcement learning to a working dialogue system with human users. We report on the design, construction and empirical evaluation of NJFun, an experimental spoken dialogue system that provides users with access to information about fun things to do in New Jersey. Our results show that by optimizing its performance via reinforcement learning, NJFun measurably improves system performance.


ATTac-2000: An Adaptive Autonomous Bidding Agent

Abstract: The First Trading Agent Competition (TAC) was held from June 22nd to July 8th, 2000. TAC was designed to create a benchmark problem in the complex domain of e-marketplaces and to motivate researchers to apply unique approaches to a common task. This article describes ATTac-2000, the first-place finisher in TAC. ATTac-2000 uses a principled bidding strategy that includes several elements of adaptivity. In addition to the success at the competition, isolated empirical results are presented indicating the robustness and effectiveness of ATTac-2000's adaptive strategy.


Efficient Methods for Qualitative Spatial Reasoning

Abstract: The theoretical properties of qualitative spatial reasoning in the RCC8 framework have been analyzed extensively. However, no empirical investigation has been made yet. Our experiments show that the adaption of the algorithms used for qualitative temporal reasoning can solve large RCC8 instances, even if they are in the phase transition region -- provided that one uses the maximal tractable subsets of RCC8 that have been identified by us. In particular, we demonstrate that the orthogonal combination of heuristic methods is successful in solving almost all apparently hard instances in the phase transition region up to a certain size in reasonable time.


Learning Geometrically-Constrained Hidden Markov Models for Robot Navigation: Bridging the Topological-Geometrical Gap

Abstract: Hidden Markov models (HMMs) and partially observable Markov decision processes (POMDPs) provide useful tools for modeling dynamical systems. They are particularly useful for representing the topology of environments such as road networks and office buildings, which are typical for robot navigation and planning. The work presented here describes a formal framework for incorporating readily available odometric information and geometrical constraints into both the models and the algorithm that learns them. By taking advantage of such information, learning HMMs/POMDPs can be made to generate better solutions and require fewer iterations, while being robust in the face of data reduction. Experimental results, obtained from both simulated and real robot data, demonstrate the effectiveness of the approach.


Accelerating Reinforcement Learning through Implicit Imitation

Abstract: Imitation can be viewed as a means of enhancing learning in multiagent environments. It augments an agent's ability to learn useful behaviors by making intelligent use of the knowledge implicit in behaviors demonstrated by cooperative teachers or other more experienced agents. We propose and study a formal model of implicit imitation that can accelerate reinforcement learning dramatically in certain cases. Roughly, by observing a mentor, a reinforcement-learning agent can extract information about its own capabilities in, and the relative value of, unvisited parts of the state space. We study two specific instantiations of this model, one in which the learning agent and the mentor have identical abilities, and one designed to deal with agents and mentors with different action sets. We illustrate the benefits of implicit imitation by integrating it with prioritized sweeping, and demonstrating improved performance and convergence through observation of single and multiple mentors. Though we make some stringent assumptions regarding observability and possible interactions, we briefly comment on extensions of the model that relax these restricitions.


Towards P = NP via k-SAT: A k-SAT Algorithm Using Linear Algebra on Finite Fields

Abstract: The problem of P vs. NP is very serious, and solutions to the problem can help save lives. This article is an attempt at solving the problem using a computer algorithm. It is presented in a fashion that will hopefully allow for easy understanding for many people and scientists from many diverse fields. In technical terms, a novel method for solving k-SAT is explained. This method is primarily based on linear algebra and finite fields. Evidence is given that this method may require rougly O(n^3) time and space for deterministic models. More specifically the algorithm runs in time O(P V(n+V)^2) with mistaking satisfiable Boolean expressions as unsatisfiable with an approximate probablity 1 / \Theta(V(n+V)^2)^P, where n is the number of clauses and V is the number of variables. It's concluded that significant evidence exists that P=NP. There is a forum devoted to this paper at http://482527.ForumRomanum.com. All are invited to correspond here and help with the analysis of the algorithm. Source code for the associated algorithm can be found at https://sourceforge.net/p/la3sat.


Actor-network procedures: Modeling multi-factor authentication, device pairing, social interactions

Abstract: As computation spreads from computers to networks of computers, and migrates into cyberspace, it ceases to be globally programmable, but it remains programmable indirectly: network computations cannot be controlled, but they can be steered by local constraints on network nodes. The tasks of "programming" global behaviors through local constraints belong to the area of security. The "program particles" that assure that a system of local interactions leads towards some desired global goals are called security protocols. As computation spreads beyond cyberspace, into physical and social spaces, new security tasks and problems arise. As networks are extended by physical sensors and controllers, including the humans, and interlaced with social networks, the engineering concepts and techniques of computer security blend with the social processes of security. These new connectors for computational and social software require a new "discipline of programming" of global behaviors through local constraints. Since the new discipline seems to be emerging from a combination of established models of security protocols with older methods of procedural programming, we use the name procedures for these new connectors, that generalize protocols. In the present paper we propose actor-networks as a formal model of computation in heterogenous networks of computers, humans and their devices; and we introduce Procedure Derivation Logic (PDL) as a framework for reasoning about security in actor-networks. On the way, we survey the guiding ideas of Protocol Derivation Logic (also PDL) that evolved through our work in security in last 10 years. Both formalisms are geared towards graphic reasoning and tool support. We illustrate their workings by analysing a popular form of two-factor authentication, and a multi-channel device pairing procedure, devised for this occasion.


Efficient Reinforcement Learning Using Recursive Least-Squares Methods

Abstract: The recursive least-squares (RLS) algorithm is one of the most well-known algorithms used in adaptive filtering, system identification and adaptive control. Its popularity is mainly due to its fast convergence speed, which is considered to be optimal in practice. In this paper, RLS methods are used to solve reinforcement learning problems, where two new reinforcement learning algorithms using linear value function approximators are proposed and analyzed. The two algorithms are called RLS-TD(lambda) and Fast-AHC (Fast Adaptive Heuristic Critic), respectively. RLS-TD(lambda) can be viewed as the extension of RLS-TD(0) from lambda=0 to general lambda within interval [0,1], so it is a multi-step temporal-difference (TD) learning algorithm using RLS methods. The convergence with probability one and the limit of convergence of RLS-TD(lambda) are proved for ergodic Markov chains. Compared to the existing LS-TD(lambda) algorithm, RLS-TD(lambda) has advantages in computation and is more suitable for online learning. The effectiveness of RLS-TD(lambda) is analyzed and verified by learning prediction experiments of Markov chains with a wide range of parameter settings. The Fast-AHC algorithm is derived by applying the proposed RLS-TD(lambda) algorithm in the critic network of the adaptive heuristic critic method. Unlike conventional AHC algorithm, Fast-AHC makes use of RLS methods to improve the learning-prediction efficiency in the critic. Learning control experiments of the cart-pole balancing and the acrobot swing-up problems are conducted to compare the data efficiency of Fast-AHC with conventional AHC. From the experimental results, it is shown that the data efficiency of learning control can also be improved by using RLS methods in the learning-prediction process of the critic. The performance of Fast-AHC is also compared with that of the AHC method using LS-TD(lambda). Furthermore, it is demonstrated in the experiments that different initial values of the variance matrix in RLS-TD(lambda) are required to get better performance not only in learning prediction but also in learning control. The experimental results are analyzed based on the existing theoretical work on the transient phase of forgetting factor RLS methods.


Probabilistic Management of OCR Data using an RDBMS

Abstract: The digitization of scanned forms and documents is changing the data sources that enterprises manage. To integrate these new data sources with enterprise data, the current state-of-the-art approach is to convert the images to ASCII text using optical character recognition (OCR) software and then to store the resulting ASCII text in a relational database. The OCR problem is challenging, and so the output of OCR often contains errors. In turn, queries on the output of OCR may fail to retrieve relevant answers. State-of-the-art OCR programs, e.g., the OCR powering Google Books, use a probabilistic model that captures many alternatives during the OCR process. Only when the results of OCR are stored in the database, do these approaches discard the uncertainty. In this work, we propose to retain the probabilistic models produced by OCR process in a relational database management system. A key technical challenge is that the probabilistic data produced by OCR software is very large (a single book blows up to 2GB from 400kB as ASCII). As a result, a baseline solution that integrates these models with an RDBMS is over 1000x slower versus standard text processing for single table select-project queries. However, many applications may have quality-performance needs that are in between these two extremes of ASCII and the complete model output by the OCR software. Thus, we propose a novel approximation scheme called Staccato that allows a user to trade recall for query performance. Additionally, we provide a formal analysis of our scheme's properties, and describe how we integrate our scheme with standard-RDBMS text indexing.


Cross-Layer Scheduling for Cooperative Multi-Hop Cognitive Radio Networks

Abstract: The paper aims to design cross-layer optimal scheduling algorithms for cooperative multi-hop Cognitive Radio Networks (CRNs), where secondary users (SUs) assist primary user (PU)'s multi-hop transmissions and in return gain authorization to access a share of the spectrum. We build two models for two different types of PUs, corresponding to elastic and inelastic service classes. For CRNs with elastic service, the PU maximizes its throughput while assigning a time-share of the channel to SUs proportional to SUs' assistance. For the inelastic case, the PU is guaranteed a minimum utility. The proposed algorithm for elastic PU model can achieve arbitrarily close to the optimal PU throughput, while the proposed algorithm for inelastic PU model can achieve arbitrarily close to the optimal SU utility. Both algorithms provide deterministic upper-bounds for PU queue backlogs. In addition, we show a tradeoff between throughput/utility and PU's average end-to-end delay upper-bounds for both algorithms. Furthermore, the algorithms work in both backlogged as well as arbitrary arrival rate systems.


Distributed Stochastic Power Control in Ad-hoc Networks: A Nonconvex Case

Abstract: Utility-based power allocation in wireless ad-hoc networks is inherently nonconvex because of the global coupling induced by the co-channel interference. To tackle this challenge, we first show that the globally optimal point lies on the boundary of the feasible region, which is utilized as a basis to transform the utility maximization problem into an equivalent max-min problem with more structure. By using extended duality theory, penalty multipliers are introduced for penalizing the constraint violations, and the minimum weighted utility maximization problem is then decomposed into subproblems for individual users to devise a distributed stochastic power control algorithm, where each user stochastically adjusts its target utility to improve the total utility by simulated annealing. The proposed distributed power control algorithm can guarantee global optimality at the cost of slow convergence due to simulated annealing involved in the global optimization. The geometric cooling scheme and suitable penalty parameters are used to improve the convergence rate. Next, by integrating the stochastic power control approach with the back-pressure algorithm, we develop a joint scheduling and power allocation policy to stabilize the queueing systems. Finally, we generalize the above distributed power control algorithms to multicast communications, and show their global optimality for multicast traffic.


Semantics for Possibilistic Disjunctive Programs

Abstract: In this paper, a possibilistic disjunctive logic programming approach for modeling uncertain, incomplete and inconsistent information is defined. This approach introduces the use of possibilistic disjunctive clauses which are able to capture incomplete information and incomplete states of a knowledge base at the same time. By considering a possibilistic logic program as a possibilistic logic theory, a construction of a possibilistic logic programming semantic based on answer sets and the proof theory of possibilistic logic is defined. It shows that this possibilistic semantics for disjunctive logic programs can be characterized by a fixed-point operator. It is also shown that the suggested possibilistic semantics can be computed by a resolution algorithm and the consideration of optimal refutations from a possibilistic logic theory. In order to manage inconsistent possibilistic logic programs, a preference criterion between inconsistent possibilistic models is defined; in addition, the approach of cuts for restoring consistency of an inconsistent possibilistic knowledge base is adopted. The approach is illustrated in a medical scenario.


An Exponential Lower Bound for the Latest Deterministic Strategy Iteration Algorithms

Abstract: This paper presents a new exponential lower bound for the two most popular deterministic variants of the strategy improvement algorithms for solving parity, mean payoff, discounted payoff and simple stochastic games. The first variant improves every node in each step maximizing the current valuation locally, whereas the second variant computes the globally optimal improvement in each step. We outline families of games on which both variants require exponentially many strategy iterations.


Proceedings of Second International Symposium on Games, Automata, Logics and Formal Verification

Abstract: This volume contains the Proceedings of the Second International Symposium on Games, Automata, Languages, and Formal Verification (GandALF 2011). The conference was held in Minori (Amalfi Coast, Italy), from the 15th to the 17th of June 2011. The aim of the GandALF Symposium is to provide a forum for researchers from different areas and with different background, that share a common interest in game theory, mathematical logic, automata theory, and their applications to the specification, design, and verification of complex systems. This proceedings contain the abstracts of three invited talks and nineteen regular papers that have been selected through a rigorous reviewing process according to originality, quality, and relevance to the topics of the symposium.


Recovering Epipolar Geometry from Images of Smooth Surfaces

Abstract: We present four methods for recovering the epipolar geometry from images of smooth surfaces. In the existing methods for recovering epipolar geometry corresponding feature points are used that cannot be found in such images. The first method is based on finding corresponding characteristic points created by illumination (ICPM - illumination characteristic points' method (PM)). The second method is based on correspondent tangency points created by tangents from epipoles to outline of smooth bodies (OTPM - outline tangent PM). These two methods are exact and give correct results for real images, because positions of the corresponding illumination characteristic points and corresponding outline are known with small errors. But the second method is limited either to special type of scenes or to restricted camera motion. We also consider two more methods which are termed CCPM (curve characteristic PM) and CTPM (curve tangent PM), for searching epipolar geometry for images of smooth bodies based on a set of level curves with constant illumination intensity. The CCPM method is based on searching correspondent points on isophoto curves with the help of correlation of curvatures between these lines. The CTPM method is based on property of the tangential to isophoto curve epipolar line to map into the tangential to correspondent isophoto curves epipolar line. The standard method (SM) based on knowledge of pairs of the almost exact correspondent points. The methods have been implemented and tested by SM on pairs of real images. Unfortunately, the last two methods give us only a finite subset of solutions including "good" solution. Exception is "epipoles in infinity". The main reason is inaccuracy of assumption of constant brightness for smooth bodies. But outline and illumination characteristic points are not influenced by this inaccuracy. So, the first pair of methods gives exact results.


Random Deployment of Data Collectors for Serving Randomly-Located Sensors

Abstract: Recently, wireless communication industries have begun to extend their services to machine-type communication devices as well as to user equipments. Such machine-type communication devices as meters and sensors need intermittent uplink resources to report measured or sensed data to their serving data collector. It is however hard to dedicate limited uplink resources to each of them. Thus, efficient service of a tremendous number of devices with low activities may consider simple random access as a solution. The data collectors receiving the measured data from many sensors simultaneously can successfully decode only signals with signal-to-interference-plus-noise-ratio (SINR) above a certain value. The main design issues for this environment become how many data collectors are needed, how much power sensor nodes transmit with, and how wireless channels affect the performance. This paper provides answers to those questions through a stochastic analysis based on a spatial point process and on simulations.


A Fast Affine Projection Algorithm Based on Matching Pursuit in Adaptive Noise Cancellation for Speech Enhancement

Abstract: In many application of noise cancellation, the changes in signal characteristics could be quite fast. This requires the utilization of adaptive algorithms, which converge rapidly. Least Mean Squares (LMS) adaptive filters have been used in a wide range of signal processing application. The Recursive Least Squares (RLS) algorithm has established itself as the "ultimate" adaptive filtering algorithm in the sense that it is the adaptive filter exhibiting the best convergence behavior. Unfortunately, practical implementations of the algorithm are often associated with high computational complexity and/or poor numerical properties. Recently adaptive filtering was presented that was based on Matching Pursuits, have a nice tradeoff between complexity and the convergence speed. This paper describes a new approach for noise cancellation in speech enhancement using the new adaptive filtering algorithm named fast affine projection algorithm (FAPA). The simulation results demonstrate the good performance of the FAPA in attenuating the noise.


A Family of Adaptive Filter Algorithms in Noise Cancellation for Speech Enhancement

Abstract: In many application of noise cancellation, the changes in signal characteristics could be quite fast. This requires the utilization of adaptive algorithms, which converge rapidly. Least Mean Squares (LMS) and Normalized Least Mean Squares (NLMS) adaptive filters have been used in a wide range of signal processing application because of its simplicity in computation and implementation. The Recursive Least Squares (RLS) algorithm has established itself as the "ultimate" adaptive filtering algorithm in the sense that it is the adaptive filter exhibiting the best convergence behavior. Unfortunately, practical implementations of the algorithm are often associated with high computational complexity and/or poor numerical properties. Recently adaptive filtering was presented, have a nice tradeoff between complexity and the convergence speed. This paper describes a new approach for noise cancellation in speech enhancement using the two new adaptive filtering algorithms named fast affine projection algorithm and fast Euclidean direction search algorithms for attenuating noise in speech signals. The simulation results demonstrate the good performance of the two new algorithms in attenuating the noise.


A Dual Approach for Solving Nonlinear Infinite-Norm Minimization Problems with Applications in Separable Cases

Abstract: In this paper, we focus on nonlinear infinite-norm minimization problems that have many applications, especially in computer science and operations research. We set a reliable Lagrangian dual aproach for solving this kind of problems in general, and based on this method, we propose an algorithm for the mixed linear and nonlinear infinite-norm minimization cases with numerical results.


Studying and Classification of the Most Significant Malicious Software

Abstract: As the cost of information processing and Internet accessibility falls, most organizations are becoming increasingly vulnerable to potential cyber threats which its rate has been dramatically increasing every year in recent times. In this paper, we study, discuss and classify the most significant malicious software: viruses, Trojans, worms, adware and pornware which have made step forward in the science of Virology.


Ice-Creams and Wedge Graphs

Abstract: What is the minimum angle $\alpha >0$ such that given any set of $\alpha$-directional antennas (that is, antennas each of which can communicate along a wedge of angle $\alpha$), one can always assign a direction to each antenna such that the resulting communication graph is connected? Here two antennas are connected by an edge if and only if each lies in the wedge assigned to the other. This problem was recently presented by Carmi, Katz, Lotker, and Ros\'en \cite{CKLR10} who also found the minimum such $\alpha$ namely $\alpha=\frac{\pi}{3}$. In this paper we give a simple proof of this result. Moreover, we obtain a much stronger and optimal result (see Theorem \ref{theorem:main}) saying in particular that one can chose the directions of the antennas so that the communication graph has diameter $\le 4$. Our main tool is a surprisingly basic geometric lemma that is of independent interest. We show that for every compact convex set $S$ in the plane and every $0 < \alpha < \pi$, there exist a point $O$ and two supporting lines to $S$ passing through $O$ and touching $S$ at two \emph{single points} $X$ and $Y$, respectively, such that $|OX|=|OY|$ and the angle between the two lines is $\alpha$.


Python GUI Scripting Interface for Running Atomic Physics Applications

Abstract: We create a Python GUI scripting interface working under Windows in addition to (UNIX/Linux). The GUI has been built around the Python open-source programming language. We use the Python's GUI library that so called Python Mega Widgets (PMW) and based on Tkinter Python module (http://www.freenetpages.co.uk/hp/alan.gauld/tutgui.htm). The new GUI was motivated primarily by the desire of more updated operations, more flexibility incorporating future and current improvements in producing atomic data. Furthermore it will be useful for a variety of applications of atomic physics, plasma physics and astrophysics and will help in calculating various atomic properties.


A Simple Characterization of the Minimal Obstruction Sets for Three-State Perfect Phylogenies

Abstract: Lam, Gusfield, and Sridhar (2009) showed that a set of three-state characters has a perfect phylogeny if and only if every subset of three characters has a perfect phylogeny. They also gave a complete characterization of the sets of three three-state characters that do not have a perfect phylogeny. However, it is not clear from their characterization how to find a subset of three characters that does not have a perfect phylogeny without testing all triples of characters. In this note, we build upon their result by giving a simple characterization of when a set of three-state characters does not have a perfect phylogeny that can be inferred from testing all pairs of characters.


Performance Analysis of Sequential Method for HandOver in Cognitive Radio Networks

Abstract: This paper has been withdrawn by the author due to a crucial problem in Lemma 3. This equation must be changed.


Secure Deletion on Log-structured File Systems

Abstract: We address the problem of secure data deletion on log-structured file systems. We focus on the YAFFS file system, widely used on Android smartphones. We show that these systems provide no temporal guarantees on data deletion and that deleted data still persists for nearly 44 hours with average phone use and indefinitely if the phone is not used after the deletion. Furthermore, we show that file overwriting and encryption, methods commonly used for secure deletion on block-structured file systems, do not ensure data deletion in log-structured file systems. We propose three mechanisms for secure deletion on log-structured file systems. Purging is a user-level mechanism that guarantees secure deletion at the cost of negligible device wear. Ballooning is a user-level mechanism that runs continuously and gives probabilistic improvements to secure deletion. Zero overwriting is a kernel-level mechanism that guarantees immediate secure deletion without device wear. We implement these mechanisms on Nexus One smartphones and show that they succeed in secure deletion and neither prohibitively reduce the longevity of the flash memory nor noticeably reduce the device's battery lifetime. These techniques provide mobile phone users more confidence that data they delete from their phones are indeed deleted.


Hadoop Performance Models

Abstract: Hadoop MapReduce is now a popular choice for performing large-scale data analytics. This technical report describes a detailed set of mathematical performance models for describing the execution of a MapReduce job on Hadoop. The models describe dataflow and cost information at the fine granularity of phases within the map and reduce tasks of a job execution. The models can be used to estimate the performance of MapReduce jobs as well as to find the optimal configuration settings to use when running the jobs.


Bayesian Combinatorial Auctions: Expanding Single Buyer Mechanisms to Many Buyers

Abstract: For Bayesian combinatorial auctions, we present a general framework for approximately reducing the mechanism design problem for multiple buyers to single buyer sub-problems. Our framework can be applied to any setting which roughly satisfies the following assumptions: (i) buyers' types must be distributed independently (not necessarily identically), (ii) objective function must be linearly separable over the buyers, and (iii) except for the supply constraints, there should be no other inter-buyer constraints. Our framework is general in the sense that it makes no explicit assumption about buyers' valuations, type distributions, and single buyer constraints (e.g., budget, incentive compatibility, etc). We present two generic multi buyer mechanisms which use single buyer mechanisms as black boxes; if an $\alpha$-approximate single buyer mechanism can be constructed for each buyer, and if no buyer requires more than $\frac{1}{k}$ of all units of each item, then our generic multi buyer mechanisms are $\gamma_k\alpha$-approximation of the optimal multi buyer mechanism, where $\gamma_k$ is a constant which is at least $1-\frac{1}{\sqrt{k+3}}$. Observe that $\gamma_k$ is at least 1/2 (for $k=1$) and approaches 1 as $k \to \infty$. As a byproduct of our construction, we present a generalization of prophet inequalities. Furthermore, as applications of our framework, we present multi buyer mechanisms with improved approximation factor for several settings from the literature.


An efficient circle detection scheme in digital images using ant system algorithm

Abstract: Detection of geometric features in digital images is an important exercise in image analysis and computer vision. The Hough Transform techniques for detection of circles require a huge memory space for data processing hence requiring a lot of time in computing the locations of the data space, writing to and searching through the memory space. In this paper we propose a novel and efficient scheme for detecting circles in edge-detected grayscale digital images. We use Ant-system algorithm for this purpose which has not yet found much application in this field. The main feature of this scheme is that it can detect both intersecting as well as non-intersecting circles with a time efficiency that makes it useful in real time applications. We build up an ant system of new type which finds out closed loops in the image and then tests them for circles.


Nearest Prime Simplicial Complex for Object Recognition

Abstract: The structure representation of data distribution plays an important role in understanding the underlying mechanism of generating data. In this paper, we propose nearest prime simplicial complex approaches (NSC) by utilizing persistent homology to capture such structures. Assuming that each class is represented with a prime simplicial complex, we classify unlabeled samples based on the nearest projection distances from the samples to the simplicial complexes. We also extend the extrapolation ability of these complexes with a projection constraint term. Experiments in simulated and practical datasets indicate that compared with several published algorithms, the proposed NSC approaches achieve promising performance without losing the structure representation.


A study of the singularity locus in the joint space of planar parallel manipulators: special focus on cusps and nodes

Abstract: Cusps and nodes on plane sections of the singularity locus in the joint space of parallel manipulators play an important role in nonsingular assembly-mode changing motions. This paper analyses in detail such points, both in the joint space and in the workspace. It is shown that a cusp (resp. a node) defines a point of tangency (resp. a crossing point) in the workspace between the singular curves and the curves associated with the so-called characteristics surfaces. The study is conducted on a planar 3-RPR manipulator for illustrative purposes.


Hypercontractive Inequality for Pseudo-Boolean Functions of Bounded Fourier Width

Abstract: A function $f:\ \{-1,1\}^n\rightarrow \mathbb{R}$ is called pseudo-Boolean. It is well-known that each pseudo-Boolean function $f$ can be written as $f(x)=\sum_{I\in {\cal F}}\hat{f}(I)\chi_I(x),$ where ${\cal F}\subseteq \{I:\ I\subseteq [n]\}$, $[n]=\{1,2,...,n\}$, and $\chi_I(x)=\prod_{i\in I}x_i$ and $\hat{f}(I)$ are non-zero reals. The degree of $f$ is $\max \{|I|:\ I\in {\cal F}\}$ and the width of $f$ is the minimum integer $\rho$ such that every $i\in [n]$ appears in at most $\rho$ sets in $\cal F$. For $i\in [n]$, let $\mathbf{x}_i$ be a random variable taking values 1 or -1 uniformly and independently from all other variables $\mathbf{x}_j$, $j\neq i.$ Let $\mathbf{x}=(\mathbf{x}_1,...,\mathbf{x}_n)$. The $p$-norm of $f$ is $||f||_p=(\mathbb E[|f(\mathbf{x})|^p])^{1/p}$ for any $p\ge 1$. It is well-known that $||f||_q\ge ||f||_p$ whenever $q> p\ge 1$. However, the higher norm can be bounded by the lower norm times a coefficient not directly depending on $f$: if $f$ is of degree $d$ and $q> p>1$ then $ ||f||_q\le (\frac{q-1}{p-1})^{d/2}||f||_p.$ This inequality is called the Hypercontractive Inequality. We show that one can replace $d$ by $\rho$ in the Hypercontractive Inequality for each $q> p\ge 2$ as follows: $ ||f||_q\le ((2r)!\rho^{r-1})^{1/(2r)}||f||_p,$ where $r=\lceil q/2\rceil$. For the case $q=4$ and $p=2$, which is important in many applications, we prove a stronger inequality: $ ||f||_4\le (2\rho+1)^{1/4}||f||_2.$


Complexity Analysis of Vario-eta through Structure

Abstract: Graph-based representations of images have recently acquired an important role for classification purposes within the context of machine learning approaches. The underlying idea is to consider that relevant information of an image is implicitly encoded into the relationships between more basic entities that compose by themselves the whole image. The classification problem is then reformulated in terms of an optimization problem usually solved by a gradient-based search procedure. Vario-eta through structure is an approximate second order stochastic optimization technique that achieves a good trade-off between speed of convergence and the computational effort required. However, the robustness of this technique for large scale problems has not been yet assessed. In this paper we firstly provide a theoretical justification of the assumptions made by this optimization procedure. Secondly, a complexity analysis of the algorithm is performed to prove its suitability for large scale learning problems.


Barbosa, Uniform Polynomial Time Bounds, and Promises

Abstract: This note is a commentary on, and critique of, Andre Luiz Barbosa's paper entitled "P != NP Proof." Despite its provocative title, what the paper is seeking to do is not to prove P \neq NP in the standard sense in which that notation is used in the literature. Rather, Barbosa is (and is aware that he is) arguing that a different meaning should be associated with the notation P \neq NP, and he claims to prove the truth of the statement P \neq NP in his quite different sense of that statement. However, we note that (1) the paper fails even on its own terms, as due to a uniformity problem, the paper's proof does not establish, even in its unusual sense of the notation, that P \neq NP; and (2) what the paper means by the claim P \neq NP in fact implies that P \neq NP holds even under the standard meaning that that notation has in the literature (and so it is exceedingly unlikely that Barbosa's proof can be fixed any time soon).


Open-loop multi-channel inversion of room impulse response

Abstract: This paper considers methods for audio display in a CAVE-type virtual reality theater, a 3 m cube with displays covering all six rigid faces. Headphones are possible since the user's headgear continuously measures ear positions, but loudspeakers are preferable since they enhance the sense of total immersion. The proposed solution consists of open-loop acoustic point control. The transfer function, a matrix of room frequency responses from the loudspeakers to the ears of the user, is inverted using multi-channel inversion methods, to create exactly the desired sound field at the user's ears. The inverse transfer function is constructed from impulse responses simulated by the image source method. This technique is validated by measuring a 2x2 matrix transfer function, simulating a transfer function with the same geometry, and filtering the measured transfer function through the inverse of the simulation. Since accuracy of the image source method decreases with time, inversion performance is improved by windowing the simulated response prior to inversion. Parameters of the simulation and inversion are adjusted to minimize residual reverberant energy; the best-case dereverberation ratio is 10 dB.


Synthesis from Recursive-Components Libraries

Abstract: Synthesis is the automatic construction of a system from its specification. In classical synthesis algorithms it is always assumed that the system is "constructed from scratch" rather than composed from reusable components. This, of course, rarely happens in real life. In real life, almost every non-trivial commercial software system relies heavily on using libraries of reusable components. Furthermore, other contexts, such as web-service orchestration, can be modeled as synthesis of a system from a library of components. In 2009 we introduced LTL synthesis from libraries of reusable components. Here, we extend the work and study synthesis from component libraries with "call and return"' control flow structure. Such control-flow structure is very common in software systems. We define the problem of Nested-Words Temporal Logic (NWTL) synthesis from recursive component libraries, where NWTL is a specification formalism, richer than LTL, that is suitable for "call and return" computations. We solve the problem, providing a synthesis algorithm, and show the problem is 2EXPTIME-complete, as standard synthesis.


Improving BDD Based Symbolic Model Checking with Isomorphism Exploiting Transition Relations

Abstract: Symbolic model checking by using BDDs has greatly improved the applicability of model checking. Nevertheless, BDD based symbolic model checking can still be very memory and time consuming. One main reason is the complex transition relation of systems. Sometimes, it is even not possible to generate the transition relation, due to its exhaustive memory requirements. To diminish this problem, the use of partitioned transition relations has been proposed. However, there are still systems which can not be verified at all. Furthermore, if the granularity of the partitions is too fine, the time required for verification may increase. In this paper we target the symbolic verification of asynchronous concurrent systems. For such systems we present an approach which uses similarities in the transition relation to get further memory reductions and runtime improvements. By applying our approach, even the verification of systems with an previously intractable transition relation becomes feasible.


Computing the Reveals Relation in Occurrence Nets

Abstract: Petri net unfoldings are a useful tool to tackle state-space explosion in verification and related tasks. Moreover, their structure allows to access directly the relations of causal precedence, concurrency, and conflict between events. Here, we explore the data structure further, to determine the following relation: event a is said to reveal event b iff the occurrence of a implies that b inevitably occurs, too, be it before, after, or concurrently with a. Knowledge of reveals facilitates in particular the analysis of partially observable systems, in the context of diagnosis, testing or verification; it can also be used to generate more concise representations of behaviours via abstractions. The reveals relation was previously introduced in the context of fault diagnosis, where it was shown that the reveals relation was decidable: for a given pair a,b in the unfolding U of a safe Petri net N, a finite prefix P of U is sufficient to decide whether or not a reveals b. In this paper, we first considerably improve the bound on |P|. We then show that there exists an efficient algorithm for computing the relation on a given prefix. We have implemented the algorithm and report on experiments.


Automated Analysis of MUTEX Algorithms with FASE

Abstract: In this paper we study the liveness of several MUTEX solutions by representing them as processes in PAFAS s, a CCS-like process algebra with a specific operator for modelling non-blocking reading behaviours. Verification is carried out using the tool FASE, exploiting a correspondence between violations of the liveness property and a special kind of cycles (called catastrophic cycles) in some transition system. We also compare our approach with others in the literature. The aim of this paper is twofold: on the one hand, we want to demonstrate the applicability of FASE to some concrete, meaningful examples; on the other hand, we want to study the impact of introducing non-blocking behaviours in modelling concurrent systems.


A reduction from parity games to simple stochastic games

Abstract: Games on graphs provide a natural model for reactive non-terminating systems. In such games, the interaction of two players on an arena results in an infinite path that describes a run of the system. Different settings are used to model various open systems in computer science, as for instance turn-based or concurrent moves, and deterministic or stochastic transitions. In this paper, we are interested in turn-based games, and specifically in deterministic parity games and stochastic reachability games (also known as simple stochastic games). We present a simple, direct and efficient reduction from deterministic parity games to simple stochastic games: it yields an arena whose size is linear up to a logarithmic factor in size of the original arena.


Opacity Issues in Games with Imperfect Information

Abstract: We study in depth the class of games with opacity condition, which are two-player games with imperfect information in which one of the players only has imperfect information, and where the winning condition relies on the information he has along the play. Those games are relevant for security aspects of computing systems: a play is opaque whenever the player who has imperfect information never "knows" for sure that the current position is one of the distinguished "secret" positions. We study the problems of deciding the existence of a winning strategy for each player, and we call them the opacity-violate problem and the opacity-guarantee problem. Focusing on the player with perfect information is new in the field of games with imperfect-information because when considering classical winning conditions it amounts to solving the underlying perfect-information game. We establish the EXPTIME-completeness of both above-mentioned problems, showing that our winning condition brings a gap of complexity for the player with perfect information, and we exhibit the relevant opacity-verify problem, which noticeably generalizes approaches considered in the literature for opacity analysis in discrete-event systems. In the case of blindfold games, this problem relates to the two initial ones, yielding the determinacy of blindfold games with opacity condition and the PSPACE-completeness of the three problems.


Type Inference for Bimorphic Recursion

Abstract: This paper proposes bimorphic recursion, which is restricted polymorphic recursion such that every recursive call in the body of a function definition has the same type. Bimorphic recursion allows us to assign two different types to a recursively defined function: one is for its recursive calls and the other is for its calls outside its definition. Bimorphic recursion in this paper can be nested. This paper shows bimorphic recursion has principal types and decidable type inference. Hence bimorphic recursion gives us flexible typing for recursion with decidable type inference. This paper also shows that its typability becomes undecidable because of nesting of recursions when one removes the instantiation property from the bimorphic recursion.


A Decidable Extension of Data Automata

Abstract: Data automata on data words is a decidable model proposed by Boja\'nczyk et al. in 2006. Class automata, introduced recently by Boja\'nczyk and Lasota, is an extension of data automata which unifies different automata models on data words. The nonemptiness of class automata is undecidable, since class automata can simulate two-counter machines. In this paper, a decidable model called class automata with priority class condition, which restricts class automata but strictly extends data automata, is proposed. The decidability of this model is obtained by establishing a correspondence with priority multicounter automata. This correspondence also completes the picture of the links between various class conditions of class automata and various models of counter machines. Moreover, this model is applied to extend a decidability result of Alur, Cern\'y and Weinstein on the algorithmic analysis of array-accessing programs.


Connectivity Games over Dynamic Networks

Abstract: A game-theoretic model for the study of dynamic networks is analyzed. The model is motivated by communication networks that are subject to failure of nodes and where the restoration needs resources. The corresponding two-player game is played between "Destructor" (who can delete nodes) and "Constructor" (who can restore or even create nodes under certain conditions). We also include the feature of information flow by allowing Constructor to change labels of adjacent nodes. As objective for Constructor the network property to be connected is considered, either as a safety condition or as a reachability condition (in the latter case starting from a non-connected network). We show under which conditions the solvability of the corresponding games for Constructor is decidable, and in this case obtain upper and lower complexity bounds, as well as algorithms derived from winning strategies. Due to the asymmetry between the players, safety and reachability objectives are not dual to each other and are treated separately.


Optimal Bounds in Parametric LTL Games

Abstract: We consider graph games of infinite duration with winning conditions in parameterized linear temporal logic, where the temporal operators are equipped with variables for time bounds. In model checking such specifications were introduced as "PLTL" by Alur et al. and (in a different version called "PROMPT-LTL") by Kupferman et al.. We present an algorithm to determine optimal variable valuations that allow a player to win a game. Furthermore, we show how to determine whether a player wins a game with respect to some, infinitely many, or all valuations. All our algorithms run in doubly-exponential time; so, adding bounded temporal operators does not increase the complexity compared to solving plain LTL games.


New results on pushdown module checking with imperfect information

Abstract: Model checking of open pushdown systems (OPD) w.r.t. standard branching temporal logics (pushdown module checking or PMC) has been recently investigated in the literature, both in the context of environments with perfect and imperfect information about the system (in the last case, the environment has only a partial view of the system's control states and stack content). For standard CTL, PMC with imperfect information is known to be undecidable. If the stack content is assumed to be visible, then the problem is decidable and 2EXPTIME-complete (matching the complexity of PMC with perfect information against CTL). The decidability status of PMC with imperfect information against CTL restricted to the case where the depth of the stack content is visible is open. In this paper, we show that with this restriction, PMC with imperfect information against CTL remains undecidable. On the other hand, we individuate an interesting subclass of OPDS with visible stack content depth such that PMC with imperfect information against the existential fragment of CTL is decidable and in 2EXPTIME. Moreover, we show that the program complexity of PMC with imperfect information and visible stack content against CTL is 2EXPTIME-complete (hence, exponentially harder than the program complexity of PMC with perfect information, which is known to be EXPTIME-complete).


Reactive Safety

Abstract: The distinction between safety and liveness properties is a fundamental classification with immediate implications on the feasibility and complexity of various monitoring, model checking, and synthesis problems. In this paper, we revisit the notion of safety for reactive systems, i.e., for systems whose behavior is characterized by the interplay of uncontrolled environment inputs and controlled system outputs. We show that reactive safety is a strictly larger class of properties than standard safety. We provide algorithms for checking if a property, given as a temporal formula or as a word or tree automaton, is a reactive safety property and for translating such properties into safety automata. Based on this construction, the standard verification and synthesis algorithms for safety properties immediately extend to the larger class of reactive safety.


An Optimal Decision Procedure for MPNL over the Integers

Abstract: Interval temporal logics provide a natural framework for qualitative and quantitative temporal reason- ing over interval structures, where the truth of formulae is defined over intervals rather than points. In this paper, we study the complexity of the satisfiability problem for Metric Propositional Neigh- borhood Logic (MPNL). MPNL features two modalities to access intervals "to the left" and "to the right" of the current one, respectively, plus an infinite set of length constraints. MPNL, interpreted over the naturals, has been recently shown to be decidable by a doubly exponential procedure. We improve such a result by proving that MPNL is actually EXPSPACE-complete (even when length constraints are encoded in binary), when interpreted over finite structures, the naturals, and the in- tegers, by developing an EXPSPACE decision procedure for MPNL over the integers, which can be easily tailored to finite linear orders and the naturals (EXPSPACE-hardness was already known).


Separation of Test-Free Propositional Dynamic Logics over Context-Free Languages

Abstract: For a class L of languages let PDL[L] be an extension of Propositional Dynamic Logic which allows programs to be in a language of L rather than just to be regular. If L contains a non-regular language, PDL[L] can express non-regular properties, in contrast to pure PDL. For regular, visibly pushdown and deterministic context-free languages, the separation of the respective PDLs can be proven by automata-theoretic techniques. However, these techniques introduce non-determinism on the automata side. As non-determinism is also the difference between DCFL and CFL, these techniques seem to be inappropriate to separate PDL[DCFL] from PDL[CFL]. Nevertheless, this separation is shown but for programs without test operators.


On P-transitive graphs and applications

Abstract: We introduce a new class of graphs which we call P-transitive graphs, lying between transitive and 3-transitive graphs. First we show that the analogue of de Jongh-Sambin Theorem is false for wellfounded P-transitive graphs; then we show that the mu-calculus fixpoint hierarchy is infinite for P-transitive graphs. Both results contrast with the case of transitive graphs. We give also an undecidability result for an enriched mu-calculus on P-transitive graphs. Finally, we consider a polynomial time reduction from the model checking problem on arbitrary graphs to the model checking problem on P-transitive graphs. All these results carry over to 3-transitive graphs.


A Game-Theoretic approach to Fault Diagnosis of Hybrid Systems

Abstract: Physical systems can fail. For this reason the problem of identifying and reacting to faults has received a large attention in the control and computer science communities. In this paper we study the fault diagnosis problem for hybrid systems from a game-theoretical point of view. A hybrid system is a system mixing continuous and discrete behaviours that cannot be faithfully modeled neither by using a formalism with continuous dynamics only nor by a formalism including only discrete dynamics. We use the well known framework of hybrid automata for modeling hybrid systems, and we define a Fault Diagnosis Game on them, using two players: the environment and the diagnoser. The environment controls the evolution of the system and chooses whether and when a fault occurs. The diagnoser observes the external behaviour of the system and announces whether a fault has occurred or not. Existence of a winning strategy for the diagnoser implies that faults can be detected correctly, while computing such a winning strategy corresponds to implement a diagnoser for the system. We will show how to determine the existence of a winning strategy, and how to compute it, for some decidable classes of hybrid automata like o-minimal hybrid automata.


Deciding Reachability for 3-Dimensional Multi-Linear Systems

Abstract: This paper deals with the problem of point-to-point reachability in multi-linear systems. These systems consist of a partition of the Euclidean space into a finite number of regions and a constant derivative assigned to each region in the partition, which governs the dynamical behavior of the system within it. The reachability problem for multi-linear systems has been proven to be decidable for the two-dimensional case and undecidable for the dimension three and higher. Multi-linear systems however exhibit certain properties that make them very suitable for topological analysis. We prove that reachability can be decided exactly in the 3-dimensional case when systems satisfy certain conditions. We show with experiments that our approach can be orders of magnitude more efficient than simulation.


Towards Efficient Exact Synthesis for Linear Hybrid Systems

Abstract: We study the problem of automatically computing the controllable region of a Linear Hybrid Automaton, with respect to a safety objective. We describe the techniques that are needed to effectively and efficiently implement a recently-proposed solution procedure, based on polyhedral abstractions of the state space. Supporting experimental results are presented, based on an implementation of the proposed techniques on top of the tool PHAVer.


Exploring Network Economics

Abstract: In this paper, we explore what \emph{network economics} is all about, focusing on the interesting topics brought about by the Internet. Our intent is make this a brief survey, useful as an outline for a course on this topic, with an extended list of references. We try to make it as intuitive and readable as possible. We also deliberately try to be critical at times, and hope our interpretation of the topic will lead to interests for further discussions by those doing research in the same field.


Traffic Performance Analysis of Manet Routing Protocol

Abstract: The primary objective of this research work is to study and investigate the performance measures of Gossip Routing protocol and Energy Efficient and Reliable Adaptive Gossip routing protocols. We use TCP and CBR based traffic models to analyze the performance of above mentioned protocols based on the parameters of Packet Delivery Ratio, Average End-to-End Delay and Throughput. We will investigate the effect of change in the simulation time and Number of nodes for the MANET routing protocols. For Simulation, we have used ns-2 simulator.


DDoS Attack and Defense Scheme in Wireless Ad hoc Networks

Abstract: The wireless ad hoc networks are highly vulnerable to distributed denial of service(DDoS) attacks because of its unique characteristics such as open network architecture, shared wireless medium and stringent resource constraints. These attacks throttle the tcp throughput heavily and reduce the quality of service(QoS) to end systems gradually rather than refusing the clients from the services completely. In this paper, we discussed the DDoS attacks and proposed a defense scheme to improve the performance of the ad hoc networks. Our proposed defense mechanism uses the medium access control (MAC) layer information to detect the attackers. The status values from MAC layer that can be used for detection are Frequency of receiving RTS/CTS packets, Frequency of sensing a busy channel and the number of RTS/DATA retransmissions. Once the attackers are identified, all the packets from those nodes will be blocked. The network resources are made available to the legitimate users. We perform the simulation with Network Simulator NS2 and we proved that our proposed system improves the network performance.


Representation Sharing for Prolog

Abstract: Representation sharing can reduce the memory footprint of a program by sharing one representation between duplicate terms. The most common implementation of representation sharing in functional programming systems is known as hash-consing. In the context of Prolog, representation sharing has been given little attention. Some current techniques that deal with representation sharing are reviewed. The new contributions are: (1) an easy implementation of {\em input sharing} for {\em findall/3}; (2) a description of a {\em sharer} module that introduces representation sharing at runtime. Their realization is shown in the context of the WAM as implemented by hProlog. Both can be adapted to any WAM-like Prolog implementation. The sharer works independently of the garbage collector, but it can be made to cooperate with the garbage collector. Benchmark results show that the sharer has a cost comparable to the heap garbage collector, that its effectiveness is highly application dependent, and that its policy must be tuned to the collector. To appear in Theory and Practice of Logic Programming (TPLP)


Methods of Matrix Multiplication: An Overview of Several Methods and their Implementation

Abstract: In this overview article we present several methods for multiplying matrices and the implementation of these methods in C. Also a little test program is given to compare their running time and the numerical stability. The methods are: naive method, naive method working on arrays, naive method with the \textsc{Kahan} trick, three methods with loop unrolling, winograd method and the scaled variant, original \textsc{Strassen} method and the \textsc{Strassen}-\textsc{Winograd} variant. Please note, that this is the FIRST version. The algorithms are not well tested and the implementation is not optimized. If you like to join the project, please contact me.


Probabilistic Abstractions with Arbitrary Domains

Abstract: Recent work by Hermanns et al. and Kattenbelt et al. has extended counterexample-guided abstraction refinement (CEGAR) to probabilistic programs. These approaches are limited to predicate abstraction. We present a novel technique, based on the abstract reachability tree recently introduced by Gulavani et al., that can use arbitrary abstract domains and widening operators (in the sense of abstract interpretation). We show how suitable widening operators can deduce loop invariants diffcult to find for predicate abstraction, and propose refinement techniques.


Exploiting Dynamically Propositional Logic Structures in SAT

Abstract: The 32-bit hwb (hwb-n32 for short) problem is from equivalence checking that arises in combining two circuits computing the hidden weighted bit function. Since 2002, it remains still unsolvable in every SAT competition. This paper focuses on solving problems such as hwb-n32. Generally speaking, modern solvers can detect only XOR, AND, OR and ITE gates. Other non-clausal formulas (propositional logic structures) cannot be detected. To solve the hwb-n32 problem, we extract dynamically some special propositional logic structures, and then use a variant of DPLL-based solvers to solve the subproblem simplified by the extracted structure information. Using the dynamic extraction technique, we solved efficiently the hwb-n32 problem, even some of which were solved within 3000 seconds.


Phase Selection Heuristics for Satisfiability Solvers

Abstract: In general, a SAT Solver based on conflict-driven DPLL consists of variable selection, phase selection, Boolean Constraint Propagation, conflict analysis, clause learning and its database maintenance. Optimizing any part of these components can enhance the performance of a solver. This paper focuses on optimizing phase selection. Although the ACE (Approximation of the Combined lookahead Evaluation) weight is applied to a lookahead SAT solver such as March, so far, no conflict-driven SAT solver applies successfully the ACE weight, since computing the ACE weight is time-consuming. Here we apply the ACE weight to partial phase selection of conflict-driven SAT solvers. This can be seen as an improvement of the heuristic proposed by Jeroslow-Wang (1990). We incorporate the ACE heuristic and the existing phase selection heuristics in the new solver MPhaseSAT, and select a phase heuristic in a way similar to portfolio methods. Experimental results show that adding the ACE heuristic can improve the conflict-driven solvers. Particularly on application instances, MPhaseSAT with the ACE heuristic is significantly better than MPhaseSAT without the ACE heuristic, and even can solve a few SAT instances that remain unsolvable so far.


A Unified Framework for Approximating and Clustering Data

Abstract: Given a set $F$ of $n$ positive functions over a ground set $X$, we consider the problem of computing $x^*$ that minimizes the expression $\sum_{f\in F}f(x)$, over $x\in X$. A typical application is \emph{shape fitting}, where we wish to approximate a set $P$ of $n$ elements (say, points) by a shape $x$ from a (possibly infinite) family $X$ of shapes. Here, each point $p\in P$ corresponds to a function $f$ such that $f(x)$ is the distance from $p$ to $x$, and we seek a shape $x$ that minimizes the sum of distances from each point in $P$. In the $k$-clustering variant, each $x\in X$ is a tuple of $k$ shapes, and $f(x)$ is the distance from $p$ to its closest shape in $x$. Our main result is a unified framework for constructing {\em coresets} and {\em approximate clustering} for such general sets of functions. To achieve our results, we forge a link between the classic and well defined notion of $\varepsilon$-approximations from the theory of PAC Learning and VC dimension, to the relatively new (and not so consistent) paradigm of coresets, which are some kind of "compressed representation" of the input set $F$. Using traditional techniques, a coreset usually implies an LTAS (linear time approximation scheme) for the corresponding optimization problem, which can be computed in parallel, via one pass over the data, and using only polylogarithmic space (i.e, in the streaming model). We show how to generalize the results of our framework for squared distances (as in $k$-mean), distances to the $q$th power, and deterministic constructions.


Consistent Query Answering under Spatial Semantic Constraints

Abstract: Consistent query answering is an inconsistency tolerant approach to obtaining semantically correct answers from a database that may be inconsistent with respect to its integrity constraints. In this work we formalize the notion of consistent query answer for spatial databases and spatial semantic integrity constraints. In order to do this, we first characterize conflicting spatial data, and next, we define admissible instances that restore consistency while staying close to the original instance. In this way we obtain a repair semantics, which is used as an instrumental concept to define and possibly derive consistent query answers. We then concentrate on a class of spatial denial constraints and spatial queries for which there exists an efficient strategy to compute consistent query answers. This study applies inconsistency tolerance in spatial databases, rising research issues that shift the goal from the consistency of a spatial database to the consistency of query answering.


Communaut\'es Web 2.0 d'apprenants de langue avec parcours d'apprentissage : r\^oles, p\'edagogie et rapports au contenu

Abstract: Among the various types of online language learner collective types, we analyze in this contribution Web 2.0 communities featuring an explicit progression. We use three analysis angles (user roles, pedagogical progression and content) in order to provides leads towards expressing the interrelations between the implementation choices regarding the concepts linked to Web 2.0 and the learning experience of the users.


Towards OWL-based Knowledge Representation in Petrology

Abstract: This paper presents our work on development of OWL-driven systems for formal representation and reasoning about terminological knowledge and facts in petrology. The long-term aim of our project is to provide solid foundations for a large-scale integration of various kinds of knowledge, including basic terms, rock classification algorithms, findings and reports. We describe three steps we have taken towards that goal here. First, we develop a semi-automated procedure for transforming a database of igneous rock samples to texts in a controlled natural language (CNL), and then a collection of OWL ontologies. Second, we create an OWL ontology of important petrology terms currently described in natural language thesauri. We describe a prototype of a tool for collecting definitions from domain experts. Third, we present an approach to formalization of current industrial standards for classification of rock samples, which requires linear equations in OWL 2. In conclusion, we discuss a range of opportunities arising from the use of semantic technologies in petrology and outline the future work in this area.


Distributed Wake-Up Scheduling for Energy Saving in Wireless Networks

Abstract: A customary solution to reduce the energy consumption of wireless communication devices is to periodically put the radio into low-power sleep mode. A relevant problem is to schedule the wake-up of nodes in such a way as to ensure proper coordination among devices, respecting delay constraints while still saving energy. In this paper, we introduce a simple algebraic characterization of the problem of periodic wake-up scheduling under both energy consumption and delay constraints. We demonstrate that the general problem of wake-up times coordination is equivalent to integer factorization and discuss the implications on the design of efficient scheduling algorithms. We then propose simple polynomial time heuristic algorithms that can be implemented in a distributed fashion and present a message complexity of the order of the number of links in the network. Numerical results are provided in order to assess the performance of the proposed techniques when applied to wireless sensor networks.


A Linear-Time Approximation of the Earth Mover's Distance

Abstract: Color descriptors are one of the important features used in content-based image retrieval. The Dominant Color Descriptor (DCD) represents a few perceptually dominant colors in an image through color quantization. For image retrieval based on DCD, the earth mover's distance and the optimal color composition distance are proposed to measure the dissimilarity between two images. Although providing good retrieval results, both methods are too time-consuming to be used in a large image database. To solve the problem, we propose a new distance function that calculates an approximate earth mover's distance in linear time. To calculate the dissimilarity in linear time, the proposed approach employs the space-filling curve for multidimensional color space. To improve the accuracy, the proposed approach uses multiple curves and adjusts the color positions. As a result, our approach achieves order-of-magnitude time improvement but incurs small errors. We have performed extensive experiments to show the effectiveness and efficiency of the proposed approach. The results reveal that our approach achieves almost the same results with the EMD in linear time.


A Novel Combined Term Suggestion Service for Domain-Specific Digital Libraries

Abstract: Interactive query expansion can assist users during their query formulation process. We conducted a user study with over 4,000 unique visitors and four different design approaches for a search term suggestion service. As a basis for our evaluation we have implemented services which use three different vocabularies: (1) user search terms, (2) terms from a terminology service and (3) thesaurus terms. Additionally, we have created a new combined service which utilizes thesaurus term and terms from a domain-specific search term re-commender. Our results show that the thesaurus-based method clearly is used more often compared to the other single-method implementations. We interpret this as a strong indicator that term suggestion mechanisms should be domain-specific to be close to the user terminology. Our novel combined approach which interconnects a thesaurus service with additional statistical relations out-performed all other implementations. All our observations show that domain-specific vocabulary can support the user in finding alternative concepts and formulating queries.


Poplar: A Java Extension for Evolvable Component Integration

Abstract: The Java programming language contains many features that aid component-based software development (CBSD), such as interfaces, visibility levels, and strong support for encapsulation. However, component evolution often causes so-called breaking changes, largely because of the rigidity of component interconnections in the form of explicit method calls and field accesses. We present a Java extension, Poplar, which we are currently developing. In Poplar, inter-component dependencies are expressed using declarative queries; concrete linking code, generated using a planning algorithm, replaces these at compile time. Poplar includes a minimal specification language based on typestate-like protocols and labels, and a lightweight effect system, which ensures the absence of unwanted interference between hand-written code and generated code. We give several examples of fully automatic component integration using Poplar, and demonstrate its potential to simplify object-oriented software development greatly through evolvable and statically checkable integration links.


A Neural Network Model for Construction Projects Site Overhead Cost Estimating in Egypt

Abstract: Estimating of the overhead costs of building construction projects is an important task in the management of these projects. The quality of construction management depends heavily on their accurate cost estimation. Construction costs prediction is a very difficult and sophisticated task especially when using manual calculation methods. This paper uses Artificial Neural Network (ANN) approach to develop a parametric cost-estimating model for site overhead cost in Egypt. Fifty-two actual real-life cases of building projects constructed in Egypt during the seven year period 2002-2009 were used as training materials. The neural network architecture is presented for the estimation of the site overhead costs as a percentage from the total project price.


Improving Web Page Readability by Plain Language

Abstract: In today's world anybody who wants to access any information the first choice is to use the web because it is the only source to provide easy and instant access to information. However web readers face many hurdles from web which includes load of web pages, text size, finding related information, spelling and grammar etc. However understanding of web pages written in English language creates great problems for non native readers who have basic knowledge of English. In this paper, we propose a plain language for a local language (Urdu) using English alphabets for web pages in Pakistan. For this purpose we developed two websites, one with a normal English fonts and other in a local language text scheme using English alphabets. We also conducted a questionnaire from 40 different users with a different level of English language fluency in Pakistan to gain the evidence of the practicality of our approach. The result shows that the proposed plain language text scheme using English alphabets improved the reading comprehension for non native English speakers in Pakistan.


Scheduling links for heavy traffic on interfering routes in wireless mesh networks

Abstract: We consider wireless mesh networks and the problem of scheduling the links of a given set of routes under the assumption of a heavy-traffic pattern. We assume some TDMA protocol provides a background of synchronized time slots and seek to schedule the routes' links to maximize the number of packets that get delivered to their destinations per time slot. Our approach is to construct an undirected graph G and to heuristically obtain node multicolorings for G that can be turned into efficient link schedules. In G each node represents a link to be scheduled and the edges are set up to represent every possible interference for any given set of interference assumptions. We present two multicoloring-based heuristics and study their performance through extensive simulations. One of the two heuristics is based on relaxing the notion of a node multicoloring by dynamically exploiting the availability of communication opportunities that would otherwise be wasted. We have found that, as a consequence, its performance is significantly superior to the other's.


Max-Margin Stacking and Sparse Regularization for Linear Classifier Combination and Selection

Abstract: The main principle of stacked generalization (or Stacking) is using a second-level generalizer to combine the outputs of base classifiers in an ensemble. In this paper, we investigate different combination types under the stacking framework; namely weighted sum (WS), class-dependent weighted sum (CWS) and linear stacked generalization (LSG). For learning the weights, we propose using regularized empirical risk minimization with the hinge loss. In addition, we propose using group sparsity for regularization to facilitate classifier selection. We performed experiments using two different ensemble setups with differing diversities on 8 real-world datasets. Results show the power of regularized learning with the hinge loss function. Using sparse regularization, we are able to reduce the number of selected classifiers of the diverse ensemble without sacrificing accuracy. With the non-diverse ensembles, we even gain accuracy on average by using sparse regularization.