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In this paper, a new frame rate up-conversion (FRC) using Backward Multiple Reference Frame Motion (BMRF) compensated interpolation (MCI) algorithm is proposed. The BMRF algorithm has been compared with two most common MCI methods, Temporal Linear (TLIN) and Bidirectional MCI (BID) used in FRC, in the aspect of PSNR, perceptual quality, and computational complexity. The simulation results clearly showed that BMRF presented a trade-off solution between computation and interpolation quality. By using simple weighted frame interpolation between multiple frames, it reduced computation rapidly while keeping similar interpolation quality when compared to the BID method. The simplicity of the BMRF algorithm made it suitable for either software or hardware implementation essential for real-time FRC applications. [ABSTRACT FROM AUTHOR]

Because of the inherent computational complexity, mining the complete frequent item-set in dense datasets remains to be a challenging task. Mining Maximal Frequent Item-set (MFI) is an alternative to address the problem. Set-Enumeration Tree (SET) is a common data structure used in several MFI mining algorithms. For this kind of algorithm, the process of mining MFI's can also be viewed as the process of searching in set-enumeration tree. To reduce the search space, in this paper, a new algorithm, Index-MaxMiner, for mining MFI is proposed by employing a hybrid search strategy blending breadth-first and depth-first. Firstly, the index array is proposed, and based on bitmap, an algorithm for computing index array is presented. By adding subsume index to frequent items, Index-MaxMiner discovers the candidate MFI's using breadth-first search at one time, which avoids first-level nodes that would not participate in the answer set and reduces drastically the number of candidate itemsets. Then, for candidate MFI's, depth-first search strategy is used to generate all MFI's. Thus, the jumping search in SET is implemented, and the search space is reduced greatly. The experimental results show that the proposed algorithm is efficient especially for dense datasets. [ABSTRACT FROM AUTHOR]

The work presented in this paper is motivated by the practical need for content extraction, and the available data source and evaluation benchmark from the ACE program. The Chinese entity detection and tracking task is of particular interest to us. A novel solution is proposed to alleviate the language-independent and language-dependent problems special in this task. Mention detection takes advantages of machine learning approaches and character-based models. It manipulates different types of entities being mentioned and different constitution units (i.e., extents and heads) separately. Mentions referring to the same entity are linked together by integrating most-specific-first and closest-first rule based pairwise clustering algorithms. Types of mentions and entities are determined by head-driven classification approaches. The implemented system achieves 66.1 of ACE value, which has been one of the top-tier results. [ABSTRACT FROM AUTHOR]

In this paper we produce a finite algebra which generates a variety with a PSPACE-complete membership problem. We produce another finite algebra with a γ function that grows exponentially. The results are obtained via a modification of a construction of the algebra A(T) that was introduced by McKenzie in 1996. [ABSTRACT FROM AUTHOR]

In many pattern recognition problems, it is desirable to reduce the number of input features by extracting important features related to the problems. By focusing on only the problem-relevant features, the dimension of features can be greatly reduced and thereby can result in a better generalization performance with less computational complexity. In this paper, we propose a feature extraction method for handling classification problems. The proposed algorithm is used to search for a set of linear combinations of the original features, whose mutual information with the output class can be maximized. The mutual information between the extracted features and the output class is calculated by using the probability density estimation based on the Parzen window method. A greedy algorithm using the gradient descent method is used to determine the new features. The computational load is proportional to the square of the number of samples. The proposed method was applied to several classification problems, which showed better or comparable performances than the conventional feature extraction methods. [ABSTRACT FROM AUTHOR]

This paper addresses a novel sentence reduction algorithm based on a decision tree model in which semantic information is used to enhance the accuracy of sentence reduction. The proposed algorithm is able to deal with the changeable order problem in sentence reduction. Furthermore, the use of decision list to solve the fragment problem in sentence reduction based decision tree model is also discussed. Our experimental results show an improvement when compared with earlier methods. [ABSTRACT FROM AUTHOR]

Propositional satisfiability (SAT) problem is fundamental to the theory of NP-completeness. Indeed, using the concept of "polynomial-time reducibility" all NP-complete problems can be polynomially reduced to SAT. Thus, any new technique for satisfiability problems will lead to general approaches for thousands of hard combinatorial problems. In this paper, we introduce the incremental propositional satisfiability problem that consists of maintaining the satisfiability of a propositional formula anytime a conjunction of new clauses is added. More precisely, the goal here is to check whether a solution to a SAT problem continues to be a solution anytime a new set of clauses is added and if not, whether the solution can be modified efficiently to satisfy the old formula and the new clauses. We will study the applicability of systematic and approximation methods for solving incremental SAT problems. The systematic method is based on the branch and bound technique while the approximation methods rely on stochastic local search and genetic algorithms. Experimental tests, conducted on randomly generated SAT instances, demonstrate the efficiency in time of the approximation methods over the branch and bound algorithm. However these approximation methods do not always guarantee the completeness of the solution returned. We show that a method we propose that uses non systematic search in a limited form together with branch and bound has the best compromise, in practice, between time and quality of the solution returned (success ratio). [ABSTRACT FROM AUTHOR]

Eye location is an important step in automatic visual interpretation and face recognition. In this paper, we present a novel eye location algorithm based on Dempster–Shafer's evidential reasoning. Four eye detectors are trained by AdaBoost with different combinations of feature spaces and samples. They detect face region respectively and produce an eye candidate set, then two of the four eye detectors calculate the confidence of every eye candidate. The confidence is converted to belief and plausibility. We use the combining rule to obtain combined belief and plausibility of each eye candidate, which represent the fusion information of the two eye detectors. The centers of eye candidates with great plausibility are considered as actual eye centers. Experimental results on several open face databases demonstrate that our method is precise, robust and has less computational complexity than other newly proposed ones. [ABSTRACT FROM AUTHOR]

Recently kernel methods in support vector machines have widely been used in machine learning algorithms to obtain nonlinear models. Clustering is an unsupervised learning method which divides whole data set into subgroups, and popular clustering algorithms such as c-means are employing kernel methods. Other kernel-based clustering algorithms have been inspired from kernel c-means. However, the formulation of kernel c-means has a high computational complexity. This paper gives an alternative formulation of kernel-based clustering algorithms derived from competitive learning clustering. This new formulation obviously uses sequential updating or on-line learning to avoid high computational complexity. We apply kernel methods to related algorithms: learning vector quantization and self-organizing map. We moreover consider kernel methods for sequential c-means and its fuzzy version by the proposed formulation. [ABSTRACT FROM AUTHOR]

To construct a phylogenetic tree or phylogenetic network for describing the evolutionary history of a set of species is a well-studied problem in computational biology. One previously proposed method to infer a phylogenetic tree/network for a large set of species is by merging a collection of known smaller phylogenetic trees on overlapping sets of species so that no (or as little as possible) branching information is lost. However, little work has been done so far on inferring a phylogenetic tree/network from a specified set of trees when in addition, certain evolutionary relationships among the species are known to be highly unlikely. In this paper, we consider the problem of constructing a phylogenetic tree/network which is consistent with all of the rooted triplets in a given set $\mathcal{C}$ and none of the rooted triplets in another given set $\mathcal{F}$. Although NP-hard in the general case, we provide some efficient exact and approximation algorithms for a number of biologically meaningful variants of the problem. [ABSTRACT FROM AUTHOR]

In this paper, we show a simple lossless compression heuristic for gray scale images. The main advantage of this approach is that it provides a highly parallelizable compressor and decompressor. In fact, it can be applied independently to each block of 8×8 pixels, achieving 80 percent of the compression obtained with LOCO-I (JPEG-LS), the current lossless standard in low-complexity applications. The compressed form of each block employs a header and a fixed length code, and the sequential implementations of the encoder and decoder are 50 to 60 percent faster than LOCO-I. [ABSTRACT FROM AUTHOR]

We present three linear algorithms for as many formulations of the problem of finding motifs with gaps. The three versions of the problem are distinct in that they assume different constraints on the size of the gaps. The outline of the algorithm is always the same, although this is adapted each time to the specific problem, while maintaining a linear time complexity with respect to the input size. The approach we suggest is based on a re-writing of the text that uses a new alphabet made of labels representing words of the original input text. The computational complexity of the algorithm allows the use of it also to find long motifs. The algorithm is in fact general enough that it could be applied to several variants of the problem other than those suggested in this paper. [ABSTRACT FROM AUTHOR]

In the standard definition of a P system, a computation step consists of a parallel application of a "maximal" set of nondeterministically chosen rules. Referring to this system as a parallel P system, we consider in this paper a sequential P system, in which each step consists of an application of a single nondeterministically chosen rule. We show the following: 1. For 1-membrane purely catalytic systems (pure CS's), the sequential version is strictly weaker than the parallel version in that the former defines (i.e., generates) exactly the semilinear sets, whereas the latter is known to define nonrecursive sets. 2. For 1-membrane communicating P systems (CPS's), the sequential version can only define a proper subclass of the semilinear sets, whereas the parallel version is known to define nonrecursive sets. 3. Adding a new type of rule of the form: ab → axbyccomedcome to the CPS (a natural generalization of the rule ab → axbyccome in the original model), where x, y ∈ {here, out}, to the sequential 1-membrane CPS makes it equivalent to a vector addition system. 4. Sequential 1-membrane symport/antiport systems (SA's) are equivalent to vector addition systems, contrasting the known result that the parallel versions can define nonrecursive sets. 5. Sequential 1-membrane SA's whose rules have radius 1, (1,1), (1,2) (i.e., of the form (a, out), (a, in), (a, out; b, in), (a, out; bc, in)) generate exactly the semilinear sets. However, if the rules have radius 1, (1,1), (2,1) (i.e., of the form (ab, out; c, in)), the SA's can only generate a proper subclass of the semilinear sets. [ABSTRACT FROM AUTHOR]

The heavy burden of computational complexity and massive storage requirement is the drawback of the standard Hough transform (SHT). To overcome the weakness of SHT, many modified approaches, for example, the probabilistic Hough transform (PHT), have been presented. However, a very important fact, which is that a line has its own width in a real digital image and the width of the line is uniform, was ignored by all of these modified algorithms of Hough transform. This phenomenon influenced the result of line detection. In this paper a new modified algorithm of Hough transform for line detection is proposed. In our algorithm, the fact mentioned above is fully considered and a strip-shaped area corresponding to the accumulate cells of HT is proposed. Experimental results have shown that our approach is efficient and promising, and the effect of detection is far better than the popular modified approaches. [ABSTRACT FROM AUTHOR]

In this paper, a fast low power scheduling algorithm is presented for high-level synthesis with multiple voltages. The resources are assumed to operate at different voltages and their power consumption and delay for each voltage level is known in advance. The proposed methodology achieves maximal power reduction of functional units by identifying the maximal available parallelism of power hungry operators in an initial schedule. The proposed methodology is developed in the framework of a modified stochastic evolution mechanism in order to tame the computational complexity. The proposed scheduling technique is extremely fast and it runs in quadratic complexity in the number of the nodes in the data flow graph of the design. This is the fastest reported time of scheduling algorithms for resource-and-latency constrained scheduling with resources operating at multiple voltages. The algorithm produces results within accuracy of 3%–5% of the linear programming method. [ABSTRACT FROM AUTHOR]

Quantum complexity theory is a powerful tool that provides deep insights into Quantum Information Processing (QIP) and aims to do that also for Quantum Mechanics (QM), in general. This paper is a short review of the main and new motivations, goals, tools, results and challenges of quantum complexity, oriented mainly for pedestrians. [ABSTRACT FROM AUTHOR]

Finding the extreme value of a database or a function plays an important role in computing and communication applications. Unfortunately, classical solutions suffer from computational complexity if the database is unsorted or, equivalently, the function has many local minimum/maximum points. Proposed quantum computing based solutions can guarantee success only in terms of the expected value of the database queries. In this paper we introduce a new technique exploiting the parallel processing capabilities of quantum computing in a different way. We derive quantum existence testing which allows adapting the classical logarithmic search algorithm suitable for structured databases to unstructured ones while maintaining its efficiency. [ABSTRACT FROM AUTHOR]

In this paper, introducing stochastic dynamics into an optimal competitive Hopfield network model (OCHOM), we propose a new algorithm that permits temporary energy increases which helps the OCHOM escape from local minima. The goal of the maximum cut problem, which is an NP-complete problem, is to partition the node set of an undirected graph into two parts in order to maximize the cardinality of the set of edges cut by the partition. The problem has many important applications including the design of VLSI circuits and design of communication networks. Recently, Galán-Marín et al. proposed the OCHOM, which can guarantee convergence to a global/local minimum of the energy function, and performs better than the other competitive neural approaches. However, the OCHOM has no mechanism to escape from local minima. The proposed algorithm introduces stochastic dynamics which helps the OCHOM escape from local minima, and it is applied to the maximum cut problem. A number of instances have been simulated to verify the proposed algorithm. [ABSTRACT FROM AUTHOR]

Although several highly accurate blind source separation algorithms have already been proposed in the literature, these algorithms must store and process the whole data set which may be tremendous in some situations. This makes the blind source separation infeasible and not realisable on VLSI level, due to a large memory requirement and costly computation. This paper concerns the algorithms for solving the problem of tremendous data sets and high computational complexity, so that the algorithms could be run on-line and implementable on VLSI level with acceptable accuracy. Our approach is to partition the observed signals into several parts and to extract the partitioned observations with a simple activation function performing only the "shift-and-add" micro-operation. No division, multiplication and exponential operations are needed. Moreover, obtaining an optimal initial de-mixing weight matrix for speeding up the separating time will be also presented. The proposed algorithm is tested on some benchmarks available online. The experimental results show that our solution provides comparable efficiency with other approaches, but lower space and time complexity. [ABSTRACT FROM AUTHOR]

In this paper we present the first distributed algorithm on general graphs for the Minimum Degree Spanning Tree problem. The problem is NP-hard in sequential. Our algorithm give a Spanning Tree of a degree at most 1 from the optimal. The resulting distributed algorithm is asynchronous, it works for named asynchronous arbitrary networks and achieves O(|V|) time complexity and O(|V||E|) message complexity. [ABSTRACT FROM AUTHOR]

In this paper, some results concerning the k-truck problem are produced. Firstly, the algorithms and their complexity concerning the off-line k-truck problem are discussed. Following that, a lower bound of competitive ratio (1+θ)·k/(θ·k+2) for the on-line k-truck problem is given, where θ is the ratio of cost of the loaded truck and the empty truck on the same distance, and a relevant lower bound for the on-line k-taxi problem followed naturally. Thirdly, based on the Position Maintaining Strategy (PMS), some new results which are slightly better than those of [11] for general cases are obtained. For example, a c-competitive or (c/θ+1/θ+1)-competitive algorithm for the on-line k-truck problem depending on the value of θ, where c is the competitive ratio of some algorithm to a relevant k-server problem, is developed. The Partial-Greedy Algorithm (PG) is used as well to solve this problem on a line with n nodes and is proved to be a (1+(n-k)/θ)-competitive algorithm for this case. Finally, the concepts of the on-line k-truck problem are extended to obtain a new variant: Deeper On-line k-Truck Problem (DTP). We claim that results of PMS for the STP (Standard Truck Problem) hold for the DTP. [ABSTRACT FROM AUTHOR]

Evolutionary Computation (EC) has attracted increasing attention in recent years, as powerful computational techniques, for solving many complex real-world problems. The Operations Research (OR)/Optimization community is divided on the acceptability of these techniques. One group accepts these techniques as potential heuristics for solving complex problems and the other rejects them on the basis of their weak mathematical foundations. In this paper, we discuss the reasons for using EC in optimization. A brief review of Evolutionary Algorithms (EAs) and their applications is provided. We also investigate the use of EAs for solving a two-stage transportation problem by designing a new algorithm. The computational results are analyzed and compared with conventional optimization techniques. [ABSTRACT FROM AUTHOR]

This paper describes a single DSP implementation of a realtime 3D sound synthesis algorithm dedicatedly for mobile applications. This approach is distinctive in that the audible frequency band is divided into three subbands in such a way that, given a head-related transfer function (HRTF), the portion of the functional behavior in each subband can be realized efficiently by a distinct digital filtering structure. The whole digital filters are implemented on a commercially available 16-bit fixed-point DSP, taking account of three major design factors of memory size, calculation accuracy, and computational complexity. As a result, the realtime 3D sound synthesis is achieved by employing a single DSP with the low computational labor of 51.3 MIPS and the small memory space of 4.6 k words. [ABSTRACT FROM AUTHOR]

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA. [ABSTRACT FROM AUTHOR]

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA. [ABSTRACT FROM AUTHOR]