Search

The tenth year of the annual International Conference on Research in Computational Biology (RECOMB) provides an opportunity to reflect on its history. RECOMB has been held across the world, including 6 different countries spanning 3 continents (Table 1). Over its 10 year history, RECOMB has published 373 papers and 170 individuals have served on its various committees. While there are many new faces in RECOMB each year, a significant number of researchers have participated over many years forming the core of the RECOMB community. [ABSTRACT FROM AUTHOR]

This paper presents a novel physically-based model for virtual Chinese brush. Compared with previous works, the main advantage of our method lies in the use of physically based modeling methods that describe the behavior of the real brush's deformation in terms of the interaction of the external and internal forces with the virtual writing paper. Instead of simulating the brush using bristles, we use points to simulate the whole brush bundle, which can drastically decrease the complexity inherent in the conventional bristle-level approach. A spring network is derived to calculate the physical deflection of brush according to the force exerted on it. With this model, we can get a more effective simulation of real brush painting. [ABSTRACT FROM AUTHOR]

Several features of today's grid are based on centralized or hierarchical services. However, as the grid size increasing, some of their functions especially resource discovery should be decentralized to avoid performance bottlenecks and guarantee scalability. A novel grid resource discovery method based on adaptive k-Nearest Neighbors clustering is presented in this paper. A class is formed by a collection of nodes with some similarities in their characteristics, each class is managed by a leader and consists of members that serve as workers. Resource requests are ideally forwarded to an appropriate class leader that would then direct it to one of its workers. This method can handle resource requests by searching a small subset out of a large number of nodes by resource clustering which can improve the resource query efficiency; on the other hand, it also achieves well scalability by managing grid resources with adaptive mechanism. It is shown from a series of experiments that the method presented in this paper achieves more scalability and efficient lookup performance than other existing methods. [ABSTRACT FROM AUTHOR]

In this paper we propose a two-pass classification method and apply it to face recognitions. The method is obtained by integrating together two approaches, the hyper-ellipsoid neural networks (HENN's) and the SVM's with error correcting codes. This method realizes a classification operation in two passes: the first one is to get an intermediate classification result for an input sample by using the HENN's, and the second pass is followed by using the SVM's to re-classify the sample based on both the input data and the intermediate result. Simulations conducted in the paper for applications to face recognition showed that the two-pass method can maintain the advantages of both the HENN's and the SVM's while remedying their disadvantages. Compared with the HENN's and the SVM's, a significant improvement of recognition performance over them has been achieved by the new method. [ABSTRACT FROM AUTHOR]

In this paper, we first discuss the level of compliance for timetabling research to two important research qualities, namely measurability and reproducibility, analyzing what we believe are the most important contributions in the literature. Secondly, we discuss some practices that, in our opinion, could contribute to the improvement on the two aforementioned qualities for future papers in timetabling research. For the sake of brevity, we restrict our scope to university timetabling problems (exams, courses, or events), and thus we leave out other equally important timetabling problems, such as high-school, employee, and transportation timetabling. [ABSTRACT FROM AUTHOR]

A software product-line is a set of products built from a core set of software components. Although software engineers develop software product-lines for various application types, they are most commonly used for embedded systems development, where the variability of hardware features requires variability in the supporting firmware. Feature models are used to represent the variability in these software product-lines. Various feature modeling approaches have been proposed, including feature diagrams, domain specific languages, constraint languages, and the semantic web language OWL. This paper explores a conceptual graph approach to feature modeling in an effort to produce feature models that have a more natural, and more easily expressed mapping to the problem domain. It demonstrates the approach using a standard Graph Product-line problem that has been discussed in various software product-line papers. A conceptual graph feature model is developed for the graph product-line and it is compared to other feature models for this product-line. [ABSTRACT FROM AUTHOR]

This paper introduces a new paradigm to realize various types of cryptographic primitives such as authenticated key exchange and key encapsulation in the standard model under three standard assumptions: the decisional Diffie-Hellman (DDH) assumption, target collision resistant (TCR) hash functions and pseudo-random functions (PRFs). We propose the first (PKI-based) two-pass authenticated key exchange (AKE) protocol that is comparably as efficient as the existing most efficient protocols like MQV and that is secure in the standard model (under these standard assumptions), while the existing efficient two-pass AKE protocols such as HMQV, NAXOS and CMQV are secure in the random oracle model. Our protocol is shown to be secure in the (currently) strongest security definition, the extended Canetti-Krawczyk (eCK) security definition introduced by LaMacchia, Lauter and Mityagin. This paper also proposes a CCA-secure key encapsulation mechanism (KEM) under these assumptions, which is almost as efficient as the Kurosawa-Desmedt KEM. This scheme is also secure in a stronger security notion, the chosen public-key and ciphertext attack (CPCA) security. The proposed schemes in this paper are redundancy-free (or validity-check-free) and the implication is that combining them with redundancy-free symmetric encryption (DEM) will yield redundancy-free (e.g., MAC-free) CCA-secure hybrid encryption. [ABSTRACT FROM AUTHOR]

The split-radix FFT computes a size-n complex DFT, when n is a large power of 2, using just $4n\lg n-6n+8$ arithmetic operations on real numbers. This operation count was first announced in 1968, stood unchallenged for more than thirty years, and was widely believed to be best possible. Recently James Van Buskirk posted software demonstrating that the split-radix FFT is not optimal. Van Buskirk's software computes a size-n complex DFT using only $(34/9+o(1))n\lg n$ arithmetic operations on real numbers. There are now three papers attempting to explain the improvement from 4 to 34/9: Johnson and Frigo, IEEE Transactions on Signal Processing, 2007; Lundy and Van Buskirk, Computing, 2007; and this paper. This paper presents the "tangent FFT," a straightforward in-place cache-friendly DFT algorithm having exactly the same operation counts as Van Buskirk's algorithm. This paper expresses the tangent FFT as a sequence of standard polynomial operations, and pinpoints how the tangent FFT saves time compared to the split-radix FFT. This description is helpful not only for understanding and analyzing Van Buskirk's improvement but also for minimizing the memory-access costs of the FFT. [ABSTRACT FROM AUTHOR]

This paper presents an approach for solving the driving load forecasting problem based on Cascade Neural Networks with node-decoupled extended Kalman Filtering (CNN-NDEKF). Because of the inherent advantages, hybrid electric vehicles (HEV) are being given more and more attention. The power control strategy of HEVs is the key technology which determines the HEV's efficiency and pollutive emission level. Since the extent of improvement involved with HEV power control strategies greatly depends on the future driving load forecasting, in this paper, we attempt to achieve driving load forecasting using CNN-NDEKF. Instead of forecasting the entire load sequence, we define 5 load levels by a fuzzy logic method and then we forecast the load level. Simulation study is given to illustrate the feasibility of the driving load forecasting approach. [ABSTRACT FROM AUTHOR]

Aiming at improving the convergence performance of conventional BP neural network, this paper presents an improved PSO algorithm instead of gradient descent method to optimize the weights and thresholds of BP network. The strategy of the algorithm is that in each iteration loop, on every dimension d of particle swarm containing n particles, choose the particle whose velocity decreases most quickly to mutate its velocity according to some probability. Simulation results show that the new algorithm is very effective. It is successful to apply the algorithm to gas turbine fault diagnosis. [ABSTRACT FROM AUTHOR]

In this paper, we study the Cohen-Grossberg neural networks with mixed time-varying delays. By applying the Lyapunov functional method and combining with the inequality 3abc ≤ a3 + b3 + c3 (a,b,c > 0) technique, a series of new and useful criteria on the existence of equilibrium point and its global asymptotical stability are established. The results obtained in this paper extend and generalize the corresponding results existing in previous literature. [ABSTRACT FROM AUTHOR]

This paper studies a general class of neural networks with time-varying delays and the neuron activations belong to the set of discontinuous monotone increasing functions. The discontinuities in the activations are an ideal model of the situation where the gain of the neuron amplifiers is very high. Because the delay in combination with high-gain nonlinearities is a particularly harmful source of potential instability, in the paper, conditions which ensure the global convergence of the neural network are derived. [ABSTRACT FROM AUTHOR]

Electromagnetic (EM)-based neural network (NN) approaches have recently gained recognition as unconventional and useful methods for radio frequency (RF) components modeling. In this paper, several improvement techniques including a new data preprocessing technique and an improved training algorithm are presented. Comprehensive cases are compared in this paper. The experimental results indicate that with these techniques, the modified model has better performance. [ABSTRACT FROM AUTHOR]

Graphics recognition for raster-based input discovers primitives such as lines, arrowheads, and circles. This paper focuses on graphics recognition of figures in vector-based PDF documents. The first stage consists of extracting the graphic and text primitives corresponding to figures. An interpreter was constructed to translate PDF content into a set of self-contained graphics and text objects (in Java), freed from the intricacies of the PDF file. The second stage consists of discovering simple graphics entities which we call graphemes, e.g., a pair of primitive graphic objects satisfying certain geometric constraints. The third stage uses machine learning to classify figures using grapheme statistics as attributes. A boosting-based learner (LogitBoost in the Weka toolkit) was able to achieve 100% classification accuracy in hold-out-one training/testing using 16 grapheme types extracted from 36 figures from BioMed Central journal research papers. The approach can readily be adapted to raster graphics recognition. Keywords: Graphics Recognition, PDF, Graphemes, Vector Graphics, Machine Learning, Boosting. [ABSTRACT FROM AUTHOR]

Raster map should be corrected after scanned because of the errors caused by paper map deformation. In the paper, the deficiency of the polynomial fitting method is analyzed. The paper introduces an ANN (Artificial Neural Network) correcting method that utilizes the advantage of its function approximation ability. In the paper, two types of ANNs, BP and GRNN, are designed for the correcting. The comparing experiment is done with the same data by the polynomial fitting and ANN methods, utilizing the MALAB. The experiment results show that the ANN methods, especially the GRNN method, performances far better than the polynomial fitting method does. [ABSTRACT FROM AUTHOR]

Automated timetabling is a research domain that has occupied many researchers over the last 50 years. Several algorithms have proven to be applicable to timetabling but they are nearly all designed to address specific problems. The framework presented in this paper is a step towards a generic semi-automatic timetabling tool. The basis of the framework is an ontology for timetabling that we designed after having investigated different types of timetabling problem. A first step towards solving general problems consists of mapping their data representation to the ontology. That is carried out in such a way that existing data that is already available in databases can further be used throughout the application. In the next step, the tool assists in determining the constraints and objectives of the problem. The semantic components have three sources of information: meta-data about the database, domain knowledge about timetabling problems and external, non-domain-specific knowledge. [ABSTRACT FROM AUTHOR]

In this paper, we present some new results on impossible differential cryptanalysis of reduced AES, which update the best known impossible differential attacks on reduced AES. First, we present some new attacks on 6-round AES (for all the three key length). Second, we extend to 7-round AES, also for all the three key variants. Especially for 128-bit keys, the best known results can attack up to 7 rounds using square attack and collision attack respectively, but their complexity are both marginal either on data or on time (ie. require nearly the entire codebook, or close to key exhaustive search). In this sense, our attack is the first non-marginal one on 7-round AES with 128-bit keys. Thirdly, we extend to 8 rounds for 256-bit keys, which is also non-marginal compared with the best non-related-key attacks so far. Finally, we give an improvement of the 7-round attack for 192-bit keys in R.C.W.Phan's paper, which makes the time complexity reduced greatly. [ABSTRACT FROM AUTHOR]

In EUROCRYPT 2006, Cheon proposed breakthrough algorithms for pairing-related problems such as the q-weak/strong Diffie-Hellman problem. Using that the exponents of an element in an abelian group G of prime order p form the ring Z/pZ structure even if G is a generic group, Cheon's algorithms reduce their complexity by Pohlig-Hellman like method over (Z/pZ)* or its extension. The algorithms are more efficient than solving the relative discrete logarithm problems in certain cases. This paper shows that Cheon's algorithms are faster than the result obtained by the complexity analysis in Cheon's paper, i.e. the algorithms can be done within $O( \sqrt{p/d} + \sqrt{d} )$ group operations, where d is a positive divisor of p − 1 with d ≤ q or a positive divisor of p + 1 with 2d ≤ q, instead of $O( \log p ( \sqrt{p/d} + \sqrt{d} ) )$ group operations shown by Cheon. This paper also shows an improvement of one of the algorithms for q-weak Diffie-Hellman problem. The improvement can be done within $O( \epsilon \sqrt{p/d} )$ group operations, where ε =  min ( 2/(1 − logpd), logp ). Moreover, this paper discusses how to choose the group order so that the algorithms are inefficient and also shows a condition for the group order and the probability that an order satisfies the condition. [ABSTRACT FROM AUTHOR]

An optimization algorithm for dynamic Bayesian networks (DBN) based on Bayesian optimization algorithm (BOA) is developed for learning and constructing the DBN structure. In this paper, we first introduce some basic theories and concepts of probability model evolutionary algorithm. Then we describe, the basic mode for constructing DBN diagram and the mechanism of DBN structure learning based on BOA. The DBN structure learning based on BOA consists of two parts. The first part is to obtain the structure and parameters of DBN in terms of a good solution, and the second part is to produce new groups according to the obtained DBN structure. In this paper, the DBN learning is achieved by genetics algorithm based on a greedy mechanism. The DBN inference is performed by a forward-simulation algorithm. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Generalization problem is a key problem in NN society, which can be grouped into two classes: the generalization problem with unlimited size of training sample and that with limited size of training sample. The generalization problem with limited size of training sample is considered in this paper. Similar to margin maximization criterion in SVM, we propose a margin maximization training algorithm for BP network to further improve the generalization ability of BP network. Experimental results show that the margin maximization training algorithm proposed in this paper does improve the performance of BP network, and shows a comparable performance with SVM. [ABSTRACT FROM AUTHOR]

Chaotic noise cancellation has potential application in both secret communication and radar target identification. To solve the problem of parameter estimation in chaotic noise, a novel radial basis function neural network (RBF-NN) -based chaotic time series data modeling method is presented in this paper. Together with the spectral analysis technique, the algorithm combines neural network's ability to approximate any nonlinear function. Based on the flexibility of RBF-NN predictor and classical amplitude spectral analysis technique, this paper proposes a new algorithm for parameter estimation in chaotic noise. Analysis of the proposed algorithm's principle and simulation experiments results are given out, which show the effective of the proposed method. We conclude that the study has potential application in various fields as in secret communication for narrow band interference rejection or attenuation and in radar signal processing for weak target detection and identification in sea clutter. [ABSTRACT FROM AUTHOR]

Nowadays, it is still the primary problem to find the inhibitors of retrovirus, protease and integrase in anti-AIDS drug design. However, the research and experimental results about anti-AIDS inhibitors mainly exist in large numbers of scientific literature, not in readable format for computer. In this paper, we introduce an Ontology-based Information Extraction (OIE) approach to extract anti-AIDS inhibitors from literature. Key to the approach is the construction of anti-AIDS inhibitors ontology, which provides a semantic framework for information extraction, and annotation of corpus. Consequently, this paper primarily focuses on the architecture of OIE, on which we construct the anti-AIDS ontology using Protégé tool and annotate corpus. Finally, we employ a demonstrated application scenario to show how to annotate the PubMed articles based on the ontology we have constructed. [ABSTRACT FROM AUTHOR]

Job management is the most complicated and kernel component of grid system. However, due to the dynamic, heterogeneous and dispersed nature of grid environment, grid job submission and scheduling are always intractable issues that needs to be addressed, especially across heterogeneous grids. In this paper, aFlexible Job Scheduling System (FISS) is designed, which exploits and extends JSDL to support the job submission with different QoS requirements across heterogeneous grid platforms. Based on the extended JSDL, multiple types of jobs are supported, including WS, WSRF, GRS. Moreover, it conduces to the interoperation among heterogeneous grid systems. Experiments are carried out and proven that FISS proposed in the paper can improve the efficiency and utilization of grid system while satisfying users' QoS requirements and achieving the interoperability among heterogeneous grids. [ABSTRACT FROM AUTHOR]

De-synchronous is a very useful method to design asynchronous circuit automatically from synchronous description of circuits. This paper introduces an optimal design method based on Control Graph which is an abstract model of the de-synchronous circuit. The main purpose of this optimal design method is to reduce the extra overhead in the area of the de-synchronous circuit. The optimization algorithm takes the performance evaluation function based on the Control Graph of the de-synchronous circuit as its heuristic function. The performance evaluation function presented in this paper is a linear programming problem. In the end of this paper, the optimal method is applied to a set of benchmark circuits. The number of the local controllers in these circuits is markedly reduced by 54%, and the number of C-elements that is required to construct the handshake circuitry between local controllers is also reduced by 76.3%. So the entire area of the circuit is sharply reduced. Because this design method is directed by the performance evaluation function of the circuit, there is no penalty in performance of the de-synchronous circuit. [ABSTRACT FROM AUTHOR]

In this paper, we consider Kearns' [4] Statistical Query Model of learning. It is well known [3] that the number of statistical queries, needed for "weakly learning" an unknown target concept (i.e. for gaining significant advantage over random guessing) is polynomially related to the so-called Statistical Query dimension of the concept class. In this paper, we provide a similar characterization for "strong learning" where the learners final hypothesis is required to approximate the unknown target concept up to a small rate of misclassification. The quantity that characterizes strong learnability in the Statistical Query model is a surprisingly close relative of (though not identical to) the Statistical Query dimension. For the purpose of proving the main result, we provide other characterizations of strong learnability which are given in terms of covering numbers and related notions. These results might find some interest in their own right. All characterizations are purely information-theoretical and ignore computational issues. [ABSTRACT FROM AUTHOR]

This paper discusses two advancements in the theory of designing truthful randomized mechanisms. Our first contribution is a new framework for developing truthful randomized mechanisms. The framework enables the construction of mechanisms with polynomially small failure probability. This is in contrast to previous mechanisms that fail with constant probability. Another appealing feature of the new framework is that bidding truthfully is a strongly dominant strategy. The power of the framework is demonstrated by an $O(\sqrt m)$-mechanism for combinatorial auctions that succeeds with probability $1-O(\frac {\log m} {\sqrt m})$. The other major result of this paper is an O(logmloglogm) randomized truthful mechanism for combinatorial auction with subadditive bidders. The best previously-known truthful mechanism for this setting guaranteed an approximation ratio of $O(\sqrt m)$. En route, the new mechanism also provides the best approximation ratio for combinatorial auctions with submodular bidders currently achieved by truthful mechanisms. [ABSTRACT FROM AUTHOR]

The controlled system is an uncertain nonlinear differential-algebraic subsystem (DASs) in a large-scale system. The problem of robust stabilization for such class of uncertain nonlinear DASs is considered in this paper. The robust stabilization controller is proposed based on backstepping approach using two-layer Artificial Neural Networks (ANN) whose weights are updated on-line. The closed-loop error systems are uniformly ultimately bounded (UUB) and the error of convergence can be made arbitrarily small. Finally, using the design scheme proposed in this paper, a governor controller is designed for one synchronous generator in a multi-machine power systems. The simulation results demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper develops a new blind channel identification method in frequency domain. Oversampled signal has the property of spectral redundancy in frequency domain which is corresponding to the cyclostationarity property in time domain. This method exploits the cyclostationarity of oversampled signals to identify possibly non-minimum phase FIR channels. Unlike many existing methods, this method doesn't need EVD or SVD of correlation matrix. Several polynomials are constructed and zeros of channels are identified through seeking for common zeros of those polynomials. It is in the similar spirit of Tong's frequency approach, but this new algorithm is much simpler and computationally more efficient. A sufficient and necessary condition for channel identification is also provided in this paper. This condition is quite similar to Tong's time domain theory but it is derived from a novel point of view. [ABSTRACT FROM AUTHOR]

Most of the algorithms proposed in the literature deal with the problem of digital modulation classification and consider classic probabilistic or decision tree classifiers. In this paper, we compare and analyze the performance of 2 neural network classifiers and 3 support vector machine classifiers (i.e. 1-v-r type, 1-v-1 type and DAG type multi-class classifier). This paper also deals with the modulation classification problems of classifying both analog and digital modulation signals in military and civilian communications applications. A total of 7 statistical signal features are extracted and used to classify 9 modulation signals. It is known that the existing technology is able to classify reliably (accuracy ≥ 90%) only at SNR above 10dB when a large range of modulation types including both digital and analog is being considered. Numerical simulations were conducted to compare performance of classifiers. Results indicated an overall success rate of over 95% at the SNR of 10dB in all classifiers. Especially, it was shown that 3 support vector machine classifiers can achieve the probabilities of correct classification (Pcc) of 96.0%, 97.3% and 97.8% at the SNR of 5dB, respectively. [ABSTRACT FROM AUTHOR]

Currently, there is little quantitative research on macroscopic real estate finance at home and abroad. Seen from the whole system of real estate finance, this paper chooses 14 main indexes to compose an evaluation index system. Based on the evaluation index system, an error -back-propagation BP network model is built to evaluate the growth of real estate finance. Data of real estate financial system from 1997-2005 are used as train and test samples of BP neural network. After training, the BP neural network is used to evaluate and forecast by simulation. Through the good accuracy of evaluation and forecasting, the model is proved to be very efficient. By comparing the growing difference of two adjacent years and analyzing the related macro financial policies in related years, the running effect of related real estate financial policies in related years is gained. So by using the evaluation model of this paper, decision makers can decide to use what kind of macro adjusting and controlling policies to gain anticipated aim of real estate finance in the future. [ABSTRACT FROM AUTHOR]

Recently, the distribution and using of the digital multimedia contents are easy by developing the internet application program and related technology. However, the digital signal is easily duplicated and the duplicates have the same quality compare with original digital signal. To solve this problem, there is the multimedia fingerprint which is studied for the protection of copyright. Fingerprinting scheme is a technique which supports copyright protection to track redistributors of electronic information using cryptographic techniques. Only regular user can know the inserted fingerprint data in fingerprinting schemes differ from a symmetric/asymmetric scheme and the scheme guarantee an anonymous before re-contributed data. In this paper, we present a new scheme which is the detection of colluded multimedia fingerprint by neural network. This proposed scheme is consists of the anti-collusion code generation and the neural network for the error correction. Anti-collusion code based on BIBD(Balanced Incomplete Block Design) was made 100% collusion code detection rate about the average linear collusion attack, and the Hopfield neural network using (n,k) code designing for the error bits correction confirmed that can correct error within 2bits. [ABSTRACT FROM AUTHOR]

Code reuse in software reuse has several limitations such as difficulties of understanding and retrieval of the reuse code written by other developers. To overcome these problems, it should be possible to reuse the analysis/design information than source code itself. In this paper, I present analogical matching techniques for the reuse of object models and design patterns. We have suggested the design patterns as reusable components and the representation techniques to store them. The contents of the paper are as follows. 1) Analogical matching functions to retrieve analogous design patterns from reusable libraries. 2) The representation of reusable components to be stored in the library in order to support the analogical matching. [ABSTRACT FROM AUTHOR]

According to analysis of both neuro-electrophysiological experimental data and the biophysical properties of neurons, in early research paper we proposed a new biophysical model that reflects the property of energy coding in neuronal activity. On the based of the above research work, in this paper the proposed biophysical model can reproduce the membrane potentials and the depolarizing membrane current by means of neuro-electrophysiological experimental data. Combination with our previous research results, the proposed biophysical model is demonstrated again to be more effective compared with known biophysical models of neurons. [ABSTRACT FROM AUTHOR]

The existence and global asymptotic stability of a large class of Cohen-Grossberg neural networks is discussed in this paper. Previous papers always assume that the amplification function has positive lower and upper bounds, which excludes a large class of functions. In our paper, it is only needed that the amplification function is positive. Also, the model discussed is general, the method used is direct and the conditions needed are weak. [ABSTRACT FROM AUTHOR]

The paper investigates the existence and global stability of periodic solution of non-autonomous neural networks with delay. Then the existence and uniqueness of periodic solutions of the neural networks are discussed in the paper. Moreover, criterion on stability of periodic solutions of the neural networks is obtained by using matrix function inequality, and algorithm for the criterion on the neural networks is provided. Result in the paper generalizes and improves the result in the existing references. In the end, an illustrate example is given to verify our results. [ABSTRACT FROM AUTHOR]

This paper mainly discusses a generic scheme for on-line adaptive critic design for nonlinear system based on neural dynamic programming (NDP), more exactly, an improved action-depended dual heuristic dynamic programming (ADDHP) method. The principal merit of the proposed method is to avoid the model neural network which predicts the state of next time step, and only use current and previous states in the method, as makes the algorithm more suitable for real-time or on-line application for process control. In this paper, convergence proof of the method will also be given to guarantee the control to reach the optimal. At last, simulation result verifies the performance. [ABSTRACT FROM AUTHOR]

This paper discusses a new application of adaptive dynamic programming (ADP). Meanwhile, traffic control as an important factor in social development is a valuable research topic. Considering with advancement of ADP and importance of traffic control, this paper present a new signal control in a single intersection. Simulation results show that the proposed signal control is valid. [ABSTRACT FROM AUTHOR]

This paper presents a model predictive control (MPC) based on a neural network (NN) model for air/fuel ration (AFR) control of automotive engines. The novelty of the paper is that the severe nonlinearity of the engine dynamics are modelled by a NN to a high precision, and adaptation of the NN model can cope with system uncertainty and time varying effects. A single dimensional optimization algorithm is used in the paper to speed up the optimization so that it can be implemented to the engine fast dynamics. Simulations on a widely used mean value engine model (MVEM) demonstrate effectiveness of the developed method. [ABSTRACT FROM AUTHOR]

In this paper, a method for automatically indexing the contents to reduce the effort that used to be required for input paper information and constructing index is sought. Various contents formats for journals, which have different features from those for general documents, are described. The principal elements that we want to represent are titles, authors, and pages for each paper. Thus, the three principal elements are modeled according to the order of their arrangement, and then their features are generalized. The content analysis system is then implemented based on the suggested modeling method. The content analysis system, implemented for verifying the suggested method, gets its input in the form containing more than 300 dpi gray scale image and analyze structural features of the contents. It classifies titles, authors and pages using efficient projection method. The definition of each item is classified according to regions, and then is extracted automatically as index information. It also helps to recognize characters region by region. The experimental result is obtained by applying to some of the suggested 6 models, and the system shows 97.3% success rate for various journals. [ABSTRACT FROM AUTHOR]

Logiweb is a system for electronic publication and archival of machine checked mathematics of high typographic quality. It can verify the formal correctness of pages, i.e. mathematical papers expressed suitably. The present paper is an example of such a Logiweb page and the present paper is formally correct in the sense that it has been verified by Logiweb. The paper may of course contain informal errors like any other paper. Logiweb is neutral with respect to choice of logic and choice of notation and can support any kind of formal reasoning. Logiweb uses the World Wide Web to publish Logiweb pages and Logiweb pages can be viewed by ordinary Web browsers. Logiweb pages can reference definitions, lemmas, and proofs on previously referenced Logiweb pages across the Internet. When Logiweb verifies a Logiweb page, it takes all transitively referenced pages into account. [ABSTRACT FROM AUTHOR]

In this paper a new problem-solving environment (PSE) for geometric processing of surfaces is introduced. The PSE has been designed to be responsive to the needs of our collaboration with an industrial partner, the Spanish company CANDEMAT S.A., devoted to build moulds and dies for the automotive industry. The PSE has been implemented in Matlab and is aimed to support the full range of activities carried out by our partner in the field of geometric processing of surfaces for the automotive industry. Firstly, the paper describes the architecture of the system and some implementation details. Then, some examples of its application to critical problems in the automotive industry - such as the computation of the intersection curves of surfaces, the generation of tool-path trajectories for NC machining and the visualization of geometric entities stored in industrial files of several formats - are briefly described. The PSE has shown to provide our partner with accurate, reliable solutions to these and other problems and to serve as a communication channel for exchange of geometrical data as well as a platform for trial and research support. [ABSTRACT FROM AUTHOR]

We introduce and analyse a simple probabilistic model of genome evolution. It is based on three fundamental evolutionary events: gene duplication, loss and innovation, and it is called DLI model. The focus of the paper is around the size distribution of gene families. The formulas for equilibrium gene family sizes are derived showing that they follow a logarithmic distribution. We consider also a disjoint union of DLI models and we present the result of this study. Some empirical results for microbial genomes are presented. [ABSTRACT FROM AUTHOR]

This paper presents an application of relation algebra to lexical databases. The semantics of knowledge representation formalisms and query languages can be provided either via a set-theoretic semantics or via an algebraic structure. With respect to formalisms based on n-ary relations (such as relational databases or power context families), a variety of algebras is applicable. In standard relational databases and in formal concept analysis (FCA) research, the algebra of choice is usually some form of Cylindric Set Algebra (CSA) or Peircean Algebraic Logic (PAL). A completely different choice of algebra is a binary Relation Algebra (RA). In this paper, it is shown how RA can be used for modelling FCA applications with respect to lexical databases. [ABSTRACT FROM AUTHOR]

Amine is a Java open source multi-layer platform dedicated to the development of intelligent systems and multi-agents systems. This paper and companion papers [2, 3] provide an overview of Amine platform and illustrate its use in the development of dynamic programming applications, natural language processing applications, multi-agents systems and ontology-based applications. [ABSTRACT FROM AUTHOR]

This paper considers the quantum query complexity of ε-biased oracles that return the correct value with probability only 1/2 + ε. In particular, we show a quantum algorithm to compute N-bit OR functions with $O(\sqrt{N}/{\varepsilon})$ queries to ε-biased oracles. This improves the known upper bound of $O(\sqrt{N}/{\varepsilon}^2)$ and matches the known lower bound; we answer the conjecture raised by the paper [1] affirmatively. We also show a quantum algorithm to cope with the situation in which we have no knowledge about the value of ε. This contrasts with the corresponding classical situation, where it is almost hopeless to achieve more than a constant success probability without knowing the value of ε. [ABSTRACT FROM AUTHOR]

It is often believed that the performance of ant system, and in general of ant colony optimization algorithms, depends somehow on the scale of the problem instance at hand. The issue has been recently raised explicitly [1] and the hyper-cube framework has been proposed to eliminate this supposed dependency. In this paper, we show that although the internal state of ant system—that is, the pheromone matrix—depends on the scale of the problem instance under analysis, this does not affect the external behavior of the algorithm. In other words, for an appropriate initialization of the pheromone, the sequence of solutions obtained by ant system does not depend on the scale of the instance. As a second contribution, the paper introduces a straightforward variant of ant system in which also the pheromone matrix is independent of the scale of the problem instance under analysis. [ABSTRACT FROM AUTHOR]

This paper studies the state-of-the-art software optimization methodology for symmetric cryptographic primitives on the new 64-bit x64 processors, AMD Athlon64 (AMD64) and Intel Pentium 4 (EM64T). We fully utilize newly introduced 64-bit registers and instructions for extracting maximal performance of target primitives. Our program of AES with 128-bit key runs in 170 cycles/block on Athlon 64, which is, as far as we know, the fastest implementation of AES on a PC processor. Also we implemented a "bitsliced" AES and Camellia for the first time, both of which achieved very good performance. A bitslice implementation is important from the viewpoint of a countermeasure against cache timing attacks because it does not require lookup tables with a key-dependent address. We also analyze performance of SHA256/512 and Whirlpool hash functions and show that SHA512 can run faster than SHA256 on Athlon 64. This paper exhibits an undocumented fact that 64-bit right shifts and 64-bit rotations are extremely slow on Pentium 4, which often leads to serious and unavoidable performance penalties in programming encryption primitives on this processor. Keywords: Fast Software Encryption, x64 Processors, Bitslice. [ABSTRACT FROM AUTHOR]

MD5 is a well-known and widely-used cryptographic hash function. It has received renewed attention from researchers subsequent to the recent announcement of collisions found by Wang et al. [16]. To date, however, the method used by researchers in this work has been fairly difficult to grasp. In this paper we conduct a study of all attacks on MD5 starting from Wang. We explain the techniques used by her team, give insights on how to improve these techniques, and use these insights to produce an even faster attack on MD5. Additionally, we provide an "MD5 Toolkit" implementing these improvements that we hope will serve as an open-source platform for further research. Our hope is that a better understanding of these attacks will lead to a better understanding of our current collection of hash functions, what their strengths and weaknesses are, and where we should direct future efforts in order to produce even stronger primitives. Keywords: Cryptographic Hash Functions, Differential Cryptanalysis, MD5. [ABSTRACT FROM AUTHOR]

This paper analyses the chaotic characteristics of violent abnormal vibration signals of a large rolling mill, and studies phase space reconstruction techniques of the signals. On this basis, the vibration model of wavelet neural networks and the model of backpropagation neural networks are set up, respectively, through inversion methods. The properties of these two models are tested and compared with each other. The result shows that the wavelet neural networks have an advantage over the backpropagation neural networks in rapid convergence and high accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a model of generalized timetabling problem and a decomposition algorithm which can decompose large problems into independent smaller subproblems—the search for a feasible solution can then be easily parallelized. The timetabling problem consists in fixing a sequence of meetings between teachers and students in a prefixed period of time (typically a week), satisfying a set of constraints of various types [9]. Course timetabling is a multi-dimensional NP-complete problem [4]. In this paper we present the multi-resource timetabling problem (MRTP), our model for the generalized high-school timetabling problem. The MRTP is a search problem—a feasible solution is searched. The main contribution of this paper is the Decomposition Algorithm for MRTP to parallelize the search, so the whole MRTP can be easily distributed and parallelized. Our approach was applied to real-life instances of high-school timetabling problems. Results are discussed at the end of this paper and parallelized search is compared with the centralized one. [ABSTRACT FROM AUTHOR]