Fast object recognition using dynamic programming from combination of salient line groups

This paper presents a new method of grouping and matching line segments to recognize objects. We propose a dynamic programming-based formulation extracting salient line patterns by defining a robust and stable geometric representation that is based on perceptual organizations. As the endpoint proximity, we detect several junctions from image lines. We then search for junction groups by using the collinear constraint between the junctions. Junction groups similar to the model are searched in the scene, based on a local comparison. A DP-based search algorithm reduces the time complexity for the search of the model lines in the scene. The system is able to find reasonable line groups in a short time.     

Cellular matrix model for parallel combinatorial optimization algorithms in Euclidean plane

We propose a parallel computation model, called cellular matrix model (CMM), to address large-size Euclidean graph matching problems in the plane. The parallel computation takes place by partitioning the plane into a regular grid of cells, each cell being affected to a single processor. Each processor operates on local data, starting from its cell location and extending its search to the neighborhood cells in a spiral search way. In order to deal with large-size problems, memory size and processor number are fixed as O(N), where N is the problem size. Then one key point is that closest point searching in the plane is performed in O(1) expected time for uniform or bounded distribution, for each processor independently. We define a generic loop that models the parallel projection between graphs and their matching, as executed by the many cells at a given level of computation granularity. To illustrate its efficacy and versatility, we apply the CMM, on GPU platforms, to two problems in image processing: superpixel segmentation and stereo matching energy minimization. Firstly, we propose an extended version of the well-known SLIC superpixel segmentation algorithm, which we call SPASM algorithm, by using a parallel 2D self-organizing map instead of k-means algorithm. Secondly, we investigate the idea of distributed variable neighborhood search, and propose a parallel search heuristic, called distributed local search (DLS), for global energy minimization of stereo matching problem. We evaluate the approach with regards to the state-of-the-art graph cut and belief propagation algorithms. For each problem, we argue that the parallel GPU implementation provides new competitive quality/time trade-offs, with substantial acceleration factors as the problem size increases.     

E-generalization using grammars

We extend the notion of anti-unification to cover equational theories and present a method based on regular tree grammars to compute a finite representation of E-generalization sets. We present a framework to combine Inductive Logic Programming and E-generalization that includes an extension of Plotkin's lgg theorem to the equational case. We demonstrate the potential power of E-generalization by three example applications: computation of suggestions for auxiliary lemmas in equational inductive proofs, computation of construction laws for given term sequences, and learning of screen editor command sequences.     

A refined search tree technique for Dominating Set on planar graphs

We establish a refined search tree technique for the parameterized DOMINATING SET problem on planar graphs. Here, we are given an undirected graph and we ask for a set of at most k vertices such that every other vertex has at least one neighbor in this set. We describe algorithms with running times O(8^kn) and O(8^kk+n³), where n is the number of vertices in the graph, based on bounded search trees. We describe a set of polynomial time data-reduction rules for a more general “annotated” problem on black/white graphs that asks for a set of k vertices (black or white) that dominate all the black vertices. An intricate argument based on the Euler formula then establishes an efficient branching strategy for reduced inputs to this problem. In addition, we give a family examples showing that the bound of the branching theorem is optimal with respect to our reduction rules. Our final search tree algorithm is easy to implement; its analysis, however, is involved.     

Detecting patterns in finite regular and context-free languages

We consider some natural variations on the following classic pattern-matching problem: given an NFA M over the alphabet Σ and a pattern p over some alphabet Δ, does there exist a word x∈L(M) such that x matches p? We consider the restricted problem where M only accepts a finite language. We also consider the variation where only some factor of x is required to match the pattern p. We show that both of these problems are NP-complete. We also consider the same problems for context-free grammars; in this case the problems become PSPACE-complete.     

Distortion-free predictive streaming time-series matching

Efficient processing of streaming time-series generated by remote sensors and mobile devices has become an important research area. As in traditional time-series applications, similarity matching on streaming time-series is also an essential research issue. To obtain more accurate similarity search results in many time-series applications, preprocessing is performed on the time-series before they are compared. The preprocessing removes distortions such as offset translation, amplitude scaling, linear trends, and noise inherent in time-series. In this paper, we propose an algorithm for distortion-free predictive streaming time-series matching. Similarity matching on streaming time-series is saliently different from traditional time-series in that it is not feasible to directly apply the traditional algorithms for streaming time-series. Our algorithm is distortion-free in the sense that it performs preprocessing on streaming time-series to remove offset translation and amplitude scaling distortions at the same time. Our algorithm is also predictive, since it performs streaming time-series matching against the predicted most recent subsequences in the near future, and thus improves search performance. To the best of our knowledge, no streaming time-series matching algorithm currently performs preprocessing and predicts future search results simultaneously.     

Improved LP-based algorithms for the closest string problem

In a recent paper by Liu et al. [Exact algorithm and heuristic for the closest string problem, Computers & Operations Research 2011;38:1513-20], a polynomial time heuristic procedure is proposed for the closest string problem (CSP). Such heuristic called LDDA_LSS is a combination of a previously published approximation algorithm and local search strategies. This paper points out that an instant algorithm deriving a feasible solution directly from the continuous relaxation solution of a standard ILP formulation of CSP already strongly outperforms LDDA_LSS both in terms of solution quality and computing time. Two core based procedures are then proposed that further improve the results of the instant algorithm. Based on these results, we conclude that such LP-based approaches for their efficiency and simplicity should be used as a benchmark for future heuristics on CSP.     

Normal eulerian clique-covering and hamiltonicity

A connected graph is hamiltonian if it contains a cycle which goes through all vertices exactly once. Determining if a graph is hamiltonian is known as an NP-complete problem and no satisfactory characterization for these graphs has been found.Since the seminal work of Dirac in 1952 many sufficient conditions were found. In 1974, Goodman and Hedetniemi gave such a condition based on the existence of a clique-covering of the graph. This condition was recently generalized using the notion of eulerian clique-covering. In addition, an algorithm able to find a normal eulerian clique-covering for a large class of graphs was also introduced. A normal clique-covering has additional properties, making the search for such a covering easier than in the general case.In this article, we prove several properties of normal clique-coverings. In particular we prove that there exists an eulerian clique-covering of a graph if and only if there exists a normal one. Using this result, the search for an eulerian clique-covering as a sufficient condition for hamiltonicity can be reduced to the normal case.     

Neighborhood search techniques for solving uncapacitated multilevel lot-sizing problems

In this paper, several neighborhood search techniques for solving uncapacitated multilevel lot-sizing problems are investigated. We introduce three indexes: distance, changing range, and changing level that have great influence on the searching efficacy of neighborhood search techniques. These insights can help develop more efficient heuristic algorithms. As a result, we have developed an iterated neighborhood search (INS) algorithm that is very simple but that demonstrates good performance when tested against 176 benchmark instances under different scales (small, medium, and large), with 25 instances having been updated with new best known solutions in the computing experiments.     

A fast algorithm for order-preserving pattern matching

Given a text T and a pattern P, the order-preserving pattern matching (OPPM) problem is to find all substrings in T which have the same relative orders as P. The OPPM has been studied in the fields of finding some patterns affected by relative orders, not by their absolute values. In this paper, we present a method of deciding the order-isomorphism between two strings even when there are same characters. Then, we show that the bad character rule of the Horspool algorithm for generic pattern matching problems can be applied to the OPPM problem and we present a space-efficient algorithm for computing shift tables for text search. Finally, we combine our bad character rule with the KMP-based algorithm to improve the worst-case running time. We give experimental results to show that our algorithm is about 2 to 6 times faster than the KMP-based algorithm in reasonable cases.     

The group-coordinate relaxation method for solving the generalized eigenvalue problem for large real-symmetric matrices

A method is described for solving the generalized eigenvalue problem for very large matrices which are not sparse and in which the off-diagonal elements are not small compared with the diagonal ones. This method has been found to succeed where others have failed.     

Searching a polygonal region by a group of stationary k-searchers

We study the problem of searching for mobile intruders in a polygonal region by stationary searchers having various levels of vision given by the number of flashlights that a searcher carries. We show that (2g−1) 1-searchers (i.e., 2g−1 searchers with one flashlight each) are always sufficient, and sometimes necessary, to search a simple polygonal region having a guard number g, which is the size of a minimum guard set. We also show that g(h+1)-searchers (i.e., g searchers with h+1 flashlights each), and consequently g(h+1) 1-searchers as well, can always search a polygonal region with h?1 holes having a guard number g.     

The capacitated centred clustering problem

The capacitated centred clustering problem (CCCP) consists of defining a set of clusters with limited capacity and maximum proper similarity per cluster. Each cluster is composed of individuals from whom we can compute a centre value and hence, determine a similarity measure. The clusters must cover the demands of their individuals. This problem can be applied to the design of garbage collection zones, defining salesmen areas, etc. In this work, we present two variations (p-CCCP and Generic CCCP) of this problem and their mathematical programming formulations. We first focus our attention on the Generic CCCP, and then we create a new procedure for p  -CCCP. These problems being NP-HARD, we propose a two-phase polynomial heuristic algorithm. The first phase is a constructive phase for which we will propose two variants: the first technique uses known cluster procedures oriented by a log-polynomial geometric tree search, the other one uses unconstrained to constrained clustering. The second phase is a refinement of the variable neighbourhood search (VNS). We also show the results we have obtained for tests from the CCP literature, the design of garbage collection zones, and salesmen areas distribution using the approach implemented for the SISROT^®${\mathrm{SISROT}}^{\circledR }$ system.     

Differential interaction nets

We introduce interaction nets for a fragment of the differential lambda-calculus and exhibit in this framework a new symmetry between the of course and the why not modalities of linear logic, which is completely similar to the symmetry between the tensor and par connectives of linear logic. We use algebraic intuitions for introducing these nets and their reduction rules, and then we develop two correctness criteria (weak typability and acyclicity) and show that they guarantee strong normalization. Finally, we outline the correspondence between this interaction nets formalism and the resource lambda-calculus.     

A time-varying transfer function for balancing the exploration and exploitation ability of a binary PSO

Many real-world problems belong to the family of discrete optimization problems. Most of these problems are NP-hard and difficult to solve efficiently using classical linear and convex optimization methods. In addition, the computational difficulties of these optimization tasks increase rapidly with the increasing number of decision variables. A further difficulty can be also caused by the search space being intrinsically multimodal and non-convex. In such a case, it is more desirable to have an effective optimization method that can cope better with these problem characteristics. Binary particle swarm optimization (BPSO) is a simple and effective discrete optimization method. The original BPSO and its variants have been used to solve a number of classic discrete optimization problems. However, it is reported that the original BPSO and its variants are unable to provide satisfactory results due to the use of inappropriate transfer functions. More specifically, these transfer functions are unable to provide BPSO a good balance between exploration and exploitation in the search space, limiting their performances. To overcome this problem, this paper proposes to employ a time-varying transfer function in the BPSO, namely TV_T-BPSO. To understand the search behaviour of the TV_T-BPSO, we provide a systematic analysis of its exploration and exploitation capability. Our experimental results demonstrate that TV_T-BPSO outperforms existing BPSO variants on both low-dimensional and high-dimensional classical 0–1 knapsack problems, as well as a 200-member truss problem, suggesting that TV_T-BPSO is able to better scale to high dimensional combinatorial problems than the existing BPSO variants and other metaheuristic algorithms.     

On the desirability of mechanizing calculational proofs

Dijkstra argues that calculational proofs are preferable to traditional pictorial and/or verbal proofs. First, due to the calculational proof format, incorrect proofs are less likely. Second, syntactic considerations (letting the “symbols do the work”) have led to an impressive array of techniques for elegant proof construction. However, calculational proofs are not formal and are not flawless. Why not make them formal and check them mechanically?     

A tabu search algorithm for the multi-period inspector scheduling problem

This paper introduces a multi-period inspector scheduling problem (MPISP), which is a new variant of the multi-trip vehicle routing problem with time windows (VRPTW). In the MPISP, each inspector is scheduled to perform a route in a given multi-period planning horizon. At the end of each period, each inspector is not required to return to the depot but has to stay at one of the vertices for recuperation. If the remaining time of the current period is insufficient for an inspector to travel from his/her current vertex A to a certain vertex B, he/she can choose either waiting at vertex A until the start of the next period or traveling to a vertex C that is closer to vertex B. Therefore, the shortest transit time between any vertex pair is affected by the length of the period and the departure time. We first describe an approach of computing the shortest transit time between any pair of vertices with an arbitrary departure time. To solve the MPISP, we then propose several local search operators adapted from classical operators for the VRPTW and integrate them into a tabu search framework. In addition, we present a constrained knapsack model that is able to produce an upper bound for the problem. Finally, we evaluate the effectiveness of our algorithm with extensive experiments based on a set of test instances. Our computational results indicate that our approach generates high-quality solutions.     

Improvements on an authentication scheme for vehicular sensor networks

In a recent paper, Shim (2012) presented a very interesting authentication scheme for vehicular sensor networks. Shim claimed that the scheme is secure against the highest adopted level of attack, namely the chosen-message attack (CID-CMA). Nevertheless, we find that the proof in Shim’s paper does not actually prove that the scheme is secure in this level. Instead, it can only ensure that the scheme is secure in a strictly weaker level of attack, the adaptive chosen-identity and no-message attack (CID-NMA). In this paper, first we show that there exist some security risks in vehicular networks if a scheme, which is only secure against CID-NMA but not CID-CMA, is deployed. Hence, having the proof that the scheme is only CID-NMA is insufficient for the aforementioned application. That is, Shim did not prove that the proposed scheme can resist these kinds of attack. Here, we use a different approach to prove the scheme for security against CID-CMA. We note that this proof is essential to ensure that the scheme can indeed be used for the aforementioned scenario. In addition, we also show that the batch verification of the scheme, proposed in the same paper, may have non-negligible error. Two invalid signatures may give a positive result. We further improve the batch verification part so that the error rate can be reduced to negligible level.     

Optimal language learning from positive data

Gold?s original paper on inductive inference introduced a notion of an optimal learner. Intuitively, a learner identifies a class of objects optimally iff there is no other learner that: requires as little of each presentation of each object in the class in order to identify that object, and, for some presentation of some object in the class, requires less of that presentation in order to identify that object. Beick considered this notion in the context of function learning, and gave an intuitive characterization of an optimal function learner. Jantke and Beick subsequently characterized the classes of functions that are algorithmically, optimally identifiable.Herein, Gold?s notion is considered in the context of language learning. It is shown that a characterization of optimal language learners analogous to Beick?s does not hold. It is also shown that the classes of languages that are algorithmically, optimally identifiable cannot be characterized in a manner analogous to that of Jantke and Beick.Other interesting results concerning optimal language learning include the following. It is shown that strong non-U-shapedness, a property involved in Beick?s characterization of optimal function learners, does not restrict algorithmic language learning power. It is also shown that, for an arbitrary optimal learner F of a class of languages L, F optimally identifies a subclass K of L iff F is class-preserving with respect to K.     

On building minimal automaton for subset matching queries

We address the problem of building an index for a set D of n strings, where each string location is a subset of some finite integer alphabet of size σ, so that we can answer efficiently if a given simple query string (where each string location is a single symbol) p occurs in the set. That is, we need to efficiently find a string d∈D such that p[i]∈d[i] for every i. We show how to build such index in O(nlogσ/Δ(σ)log(n)) average time, where Δ is the average size of the subsets. Our methods have applications e.g. in computational biology (haplotype inference) and music information retrieval.