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There is a famous saying "The consumer is God". So exactly analyzing consumers' purchasing behavior is the key factor to success for assuring the profit of the whole e-supply chain. Firstly, this paper proposes and compares three kinds of research modes to analyze consumers' purchasing behavior, and then points out that the research mode driven by data is the most scientific one. Secondly, under the guidance of the idea of the research mode driven by data, this paper takes Self-Organizing Feature Map Neural Network (SOFM NN) as a tool to analyze consumers' purchasing behavior in e-supply chain of book. Lastly, based on the result of analyzing consumers' purchasing behavior, this paper gives some corresponding cyber-marketing strategies that can help to increase the loyalty of consumers and maximize the profit of the whole e-supply chain. Because consumer's purchasing behavior analysis based on SOFM NN is a comparatively novel method, the result of research in this paper is just for reference. Keywords: SOFM; behavior; consumer; neural network. [ABSTRACT FROM AUTHOR]

Over the past twenty-five years or so Saddleback search has been used as an paradigm of how methods of formal program construction can quickly lead to a simple and effective algorithm for searching an ordered table. In this paper we revisit the problem and show that saddleback search is not in fact the best algorithm when one dimension of the table is much smaller than the other. The paper is structured in the form of a classroom discussion involving a teacher and four very clever students. [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 summarizes a new concept to determine principal curves for nonlinear principal component analysis (PCA). The concept is explained within the framework of the Hastie and Stuetzle algorithm and utilizes spline functions. The paper proposes a new algorithm and shows that it provides an efficient method to extract underlying information from measured data. The new method is geometrically simple and computationally expedient, as the number of unknown parameters increases linearly with the analyzed variable set. The utility of the algorithm is exemplified in two examples. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed a fast affine transform method for real-time machine vision applications. Inspection of parts by machine vision requires accurate, fast, reliable, and consistent operations, where the transform of visual images plays an important role. Image transform is generally expensive in computation for real-time applications. For example, a transform including rotation and scaling would require four multiplications and four additions per pixel, which is going to be a great burden to process a large image. Our proposed method reduces the complexity substantially by removing four multiplications per pixel, which exploits the relationship between two neighboring pixels. In addition, this paper shows that the affine transform can be performed by fixed point operations with marginal error. Two interpolation methods are also tried on top of the proposed method in order to test the feasibility of fixed point operations. Experimental results indicated that the proposed algorithm was about six times faster than conventional ones without any interpolation and five times faster with bilinear interpolation. [ABSTRACT FROM AUTHOR]

The Although a variety of standardization and Learning Management System (LMS) were produced to develop and effectively manage web contents in response to active diffusion of internet application, practical changes to assist online learners are not yet to be found. The core purpose of the LMS system proposed in this paper is to help learners pursue proactive and self-oriented education by allowing learners to proactively configure their own content, that is, learners no longer have to be restricted by prescribed sequence of lectures. In this paper, I would like to introduce a LMS that can support self-leading education by providing various types of learners at Virtual University with delicately organized educational contents for maximum efficiency. The system allows a learner to select a lecture or a chapter which has been presorted to meet his educational needs and intellectual ability. [ABSTRACT FROM AUTHOR]

The face images are varying environment database from external illumination. Therefore we propose parallel cascade face detector. We define context image illumination and distinguish context using unsupervised learning. Many unsupervised method is available to distinguish varying illuminant images. This approach can be distribution face image and we can make the classifier for face image context. Therefore, in this paper, we parallel classifier that is strutted cascade classifier of two methods. In first classifier, we use sub-sampling feature extraction and in second classifier we use wavelet transformation method. We achieved very encouraging experimental results. Our method is enhancement varying illumination environment. [ABSTRACT FROM AUTHOR]

This paper considers the capacities of the elements in a set E efficiently so that the total cost for the increment of capacity can be decrease to maximum extent while the final expansion capacity of a given family F of subsets of E is with a given limit bound. The paper supposes the cost w is a fuzzy variable. Network bottleneck capacity expansion problem with fuzzy cost is originally formulated as Expected value model according to some criteria. For solving the fuzzy model efficiently, network bottleneck capacity algorithm, fuzzy simulation, neural network(NN) and genetic algorithm(GA) are integrated to produce a hybrid intelligent algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates the global exponential stability of Takagi-Sugeno Fuzzy cellular neural networks with time-varying delays. Using the reduction to absurdity, a less conservative delay-independent stability criterion is derived to guarantee the exponential stability of Takagi-Sugeno Fuzzy cellular neural networks with time-varying delays. Since our model is more general than some existing works, the results presented in this paper are the improvement and extension of the existed ones. [ABSTRACT FROM AUTHOR]

Gene libraries are a biological mechanism for generating combinatorial diversity in the immune system. However, they also bias the antibody creation process, so that they can be viewed as a way of guiding lifetime learning mechanisms. In this paper we examine the implications of this view, by examining coverage, avoidance of self, clustering and diversity. We show how gene libraries may improve both computational expense and performance, and present an analysis which suggests how they might do it. We suggest that gene libraries: provide combinatorial efficiency; improve coverage; reduce the cost of negative selection; and allow targeting of fixed antigen populations. Keywords: gene libraries, artificial immune systems, antibodies, diversity, Baldwin effect, lifetime learning. [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]

We consider the problem of estimating the location of a moving target in a 2-D plane. In this paper, we focus attention on selecting an appropriate 3rd sensor, given two sensors, with a view to minimize the estimation error. Only the selected sensors need to measure distance to the target and communicate the same to the central "tracker". This minimizes bandwidth and energy consumed in measurement and communication while achieving near minimum estimation error. In this paper, we have proposed that the 3rd sensor be selected based on three measures viz. (a) collinearity, (b) deviation from the ideal direction in which the sensor should be selected, and (c) proximity of the sensor from the target. We assume that the measurements are subject to multiplicative error. Further, we use least square error estimation technique to estimate the target location. Simulation results show that using the proposed algorithm it is possible to achieve near minimum error in target location. [ABSTRACT FROM AUTHOR]

In this paper, keyword map-based relevance feedback is applied to interactive Blog search.There exists vast amount of information in the Web, from which users usually gather information without definite information needs. In particular, when exploring the Blog space, the range of user's interests is expected to be broader than usual Web browsing process. The relevance feedback techniques have been studied in the field of document retrieval, aiming to generate appropriate queries for users' information needs.Although this approach is effective when the assumption that a user has a concrete criteria on the relevance of retrieved documents holds, it could not always hold when searching Blog, which consists of vast number of short articles about various topics. Compared with the previous work on keyword map-based relevance feedback, the algorithm proposed in this paper can consider multiple topics, in which a user is interested on the keyword map. [ABSTRACT FROM AUTHOR]

Web-based information portals provide a point of access onto an integrated and structured body of information about some domain. Knowledge portals are information portals, which make an important contribution to enabling enterprise knowledge management by providing users with a consolidated, personalized user interface that allows efficient access to various types of information. Portlets are mainly ways to present contents in knowledge portals. They are a group of components, which can be involved by a portal container. However, there are lacks no interaction between those portlets. This paper discusses information integration aspects within knowledge portals and presents an approach for communicating the user context (revealing the user's information need) among portlets, utilizing ontologies technologies. [ABSTRACT FROM AUTHOR]

In this paper, we investigate how the seemingly chaotic behavior of stock markets could be well represented using neural network, TS fuzzy system and hierarchical TS fuzzy techniques. To demonstrate the different techniques, we considered Nasdaq-100 index of Nasdaq Stock MarketSM and the S&P CNX NIFTY stock index. We analyzed 7 year's Nasdaq 100 main index values and 4 year's NIFTY index values. The parameters of the different techniques are optimized by the particle swarm optimization algorithm. This paper briefly explains how the different learning paradigms could be formulated using various methods and then investigates whether they can provide the required level of performance, which are sufficiently good and robust so as to provide a reliable forecast model for stock market indices. Experiment results reveal that all the models considered could represent the stock indices behavior very accurately. [ABSTRACT FROM AUTHOR]

The paper presents a permutation-based algorithm for predicting RNA secondary structure. It is practicable, and can be used to predict real RNA molecules. The conception of permutation is introduced, which is the start point of our algorithm. Individual is represented as a permutation of stem list. Crossover operator, mutation operator, and selection strategy are designed to be compatible with such an individual representation. At the end of the paper, a comparison between our result and that from RNAstructure is outlined. It is proved that our algorithm has achieved comparable or better result than RNAstructure. [ABSTRACT FROM AUTHOR]

A wavelet-based fuzzy multiscale edge detection scheme (WFMED) is presented in this paper. The dyadic wavelet transform is employed to produce the multiscale representation of the image, fuzzy logic is applied in wavelet domain and it can synthesize the information of image across scales effectively, an optimal result of edge detection can be acquired. WFMED method is used to extract the edge of pulp fibre image; the paper compares the performance of WFMED to the Canny edge detector and to Mallat's algorithm. The results show the superiority of WFMED to these other methods. [ABSTRACT FROM AUTHOR]

The matching similarity measures that can be used in image target recognition are surveyed and a novel similarity measure is proposed in this paper. Two basic factors that affect the image matching performance and the merits and faults of two common types of image matching algorithm are firstly analyzed. Then, based on the systematic study of similarity measures, image matching projection similarity measure is defined by simplify the classical normalized correlation measure. An example of the application of the proposed matching similarity measure in image target recognition and position is given at the end of this paper; the experimental results show its feasibility and effectivity. [ABSTRACT FROM AUTHOR]

This paper is concerned with dynamic modeling, prediction and analysis of cell cytotoxicity. A real-time cell electronic sensing (RT-CES) system has been used for label-free, dynamic measurements of cell responses to toxicant. Cells were grown onto the surfaces of the microelectronic sensors. Changes in cell number expressed as cell index (CI) have been recorded on-line as time series. The CI data are used for dynamic modeling in this paper. The developed models are verified using data that do not participate in the modeling. Optimal multi-step ahead predictions are calculated and compared with the actual CI. A new framework for dynamic cytotoxicity system analysis is established. Through the analysis of the system impulse response, we have observed that there are considerably similarities between the impulse response curves and the raw dynamic data, but there are also some striking differences between the two, particularly in terms of the initial and final cell killing effects. It is shown that dynamic modeling has great potential in modeling cell dynamics in the presence of toxicant and predicting the response of the cells. [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]

How face pose (rotating from right to left) influence the fusion authentication accuracy in face and fingerprint identity authentication system? This paper firstly tries to answer this question. The maximum rotating degree that fusion system can bear is given out by experiment. Furthermore, theoretical analysis deals with how face pose influence the fusion performance is proposed in this paper. Experiment results show that faces with big rotated degree can not be helpful but harmful to fusion system. And the maximum rotated angle of face that fusion system can bear is 20 degree. On the other hand, theoretical analysis proved that the mathematical inherence of influence of face pose on fusion system is not only the reduction of variance but also the decrease of distance between the genuine and imposter classes. [ABSTRACT FROM AUTHOR]

For the purpose of extending the Web that is able to understand and process information by machine, Semantic Web shared knowledge in the ontology form. For exquisite query processing, this paper proposes a method to use semantic relations in the ontology as relevance feedback information to query expansion. We made experiment on pharmacy domain. And in order to verify the effectiveness of the semantic relation in the ontology, we compared a keyword based document retrieval system that gives weights by using the frequency information compared with an ontology based document retrieval system that uses relevant information existed in the ontology to a relevant feedback. From the evaluation of the retrieval performance, we knew that search engine used the concepts and relations in ontology for improving precision effectively. Also it used them for the basis of the inference for improvement the retrieval performance. [ABSTRACT FROM AUTHOR]

The paper presents the application of the wavelet transform of the frequency histogram of connected elements to the detection of very thin cracks in used pallets. First, the paper presents this novel concept and introduces the parameters that define a connected element, showing that the conventional grayscale intensity histogram of a digital image is a particular case of the histogram of connected elements. Then, the discriminant capability of the wavelet transform of this generalized histogram is analyzed. In particular, the information conveyed by the histogram of connected elements is exploited to detect very thin cracks in used pallets. An artificial neural network classifier to discriminate sound wood from defective wood with very thin cracks has been designed. The exhaustive experimental test carried out with numerous boards of used pallets has validated the proposed method, in particular its remarkably low ratio of false alarms. [ABSTRACT FROM AUTHOR]

This paper presents and application of Genetic Programming (GP) for time series forecast. Although this kind of application has been carried out with a wide range of techniques and with very good results, this paper presents a different approach. In most of the experiments done in time series forecasting the objective is, from a consecutive set of samples or time interval, to obtain the value of the sample in the next time step. The aim of this paper is to study the forecasting not only on the next sample, but in general several samples forward. This will allow the building of more complete prediction systems. With this objective, one of the most widely used series for this kind of application has been used, the Mackey-Glass series. [ABSTRACT FROM AUTHOR]

As shown in the bibliography, training an ensemble of networks is an interesting way to improve the performance with respect to a single network. However there are several methods to construct the ensemble. In this paper we present some new results in a comparison of twenty different methods. We have trained ensembles of 3, 9, 20 and 40 networks to show results in a wide spectrum of values. The results show that the improvement in performance above 9 networks in the ensemble depends on the method but it is usually low. Also, the best method for a ensemble of 3 networks is called "Decorrelated" and uses a penalty term in the usual Backpropagation function to decorrelate the network outputs in the ensemble. For the case of 9 and 20 networks the best method is conservative boosting. And finally for 40 networks the best method is Cels. [ABSTRACT FROM AUTHOR]

A novel immunotronic approach to fault detection in hardware based on symbiotic evolution is proposed in this paper. In the immunotronic system, the generation of tolerance conditions corresponds to the generation of antibodies in the biological immune system. In this paper, the principle of antibody diversity, one of the most important concepts in the biological immune system, is employed and it is realized through symbiotic evolution. Symbiotic evolution imitates the generation of antibodies in the biological immune system more than the standard genetic algorithm(SGA) does. It is demonstrated that the suggested method outperforms the previous immunotronic methods with less running time. The suggested method is applied to fault detection in a decade counter (typical example of finite state machines) and MCNC finite state machines and its effectiveness is demonstrated by the computer simulation. Keywords: immunotronic system, hardware fault detection, tolerance conditions, antibody diversity, symbiotic evolution. [ABSTRACT FROM AUTHOR]

Space applications' users have been relying for the past decades on custom developed software tools capable of addressing short term necessities during critical Spacecraft control periods. Advances in computing power and storage solutions have made possible the development of innovative decision support systems. These systems are capable of providing high quality integrated data to both near real time and historical data analysis applications. This paper describes the implementation of a new approach for a distributed and loosely coupled data extraction and transformation solution capable of extracting, transforming and perform loading of relevant real-time and historical Space Weather and Spacecraft data from semi-structured text files into an integrated space-domain decision support system. The described solution takes advantage of XML and Web Service technologies and is currently working under operational environment at the European Space Agency as part of the Space Environment Information System for Mission Control Purposes (SEIS) project. [ABSTRACT FROM AUTHOR]

Given the theoretical hardness of classical planning, the chance of developing practically usable solution algorithms appears slim. However, modern planning systems developed since the 1990s such as Graphplan [14], SATPLAN [75-78], HSP [16] and FF [68] have demonstrated their ability to solve planning tasks of considerable size. Their efficiency, or - put a bit more carefully - their perceived efficiency is somewhat at odds with the theoretical hardness of planning. To understand why we can observe such good planner performance, we must consider the methods with which the performance of planning systems is evaluated. This is the topic of the following Sect. 1.1, which will lead us to a discussion of planning benchmarks in Sect. 1.2 and their theoretical properties in Sect. 1.3. In the penultimate Sect. 1.4, we briefly introduce a set of standard benchmarks. The chapter concludes with an overview of Part I in Sect. 1.5. [ABSTRACT FROM AUTHOR]

Independent Component Analysis (ICA) is a statistical method for expressing an observed set of random vectors as a linear combination of statistically independent components. This paper tackles the task of comparing two ICA algorithms, in terms of their efficiency for compact representation of market securities. A recently developed sequential blind signal extraction algorithm, SmoothICA, is contrasted to a classical implementation of ICA, FastICA. SmoothICA uses an additional 2nd order constraint aiming at identifying temporally smooth components in the data set. This paper demonstrates the superiority of this novel smooth component extraction algorithm in terms of global and local approximation capability, applied to a portfolio of 60 NASDAQ securities, by utilizing common ordering algorithms for financial signals. [ABSTRACT FROM AUTHOR]

This paper proposes a way to implement a time-domain blind separation algorithm for convolutive mixtures of source signals. The approach provides another form of the algorithm by discrete Fourier transform and has the possibility of designing a separating filter in the frequency domain, without bothering about the permutation problem inherent in frequency-domain blind separation approach. This paper also shows a technique to improve separation performance in the frequency domain. The validity of our approach was demonstrated by performing an experiment on separation for convolutive mixtures of two speeches. [ABSTRACT FROM AUTHOR]

This paper presents a new algorithm for solving the permutation ambiguity in convolutive blind source separation. When transformed to the frequency domain, the source separation problem reduces to independent instantaneous separation in each frequency bin, which can be efficiently solved by existing algorithms. But this independency leads to the problem of correct alignment of these single bins which is still not entirely solved. The algorithm proposed in this paper models the frequency-domain separated signals using the generalized Gaussian distribution and utilizes the small deviation of the exponent between neighboring bins for the detection of correct permutations. [ABSTRACT FROM AUTHOR]

This paper describes a new blind source separation method for instantaneous linear mixtures. This new method coined GMCA (Generalized Morphological Component Analysis) relies on morphological diversity. It provides new insights on the use of sparsity for blind source separation in a noisy environment. GMCA takes advantage of the sparse representation of structured data in large overcomplete signal dictionaries to separate sources based on their morphology. In this paper, we define morphological diversity and focus on its ability to be a helpful source of diversity between the signals we wish to separate. We introduce the blind GMCA algorithm and we show that it leads to good results in the overdetermined blind source separation problem from noisy mixtures. Both theoretical and algorithmic comparisons between morphological diversity and independence-based separation techniques are given. The effectiveness of the proposed scheme is confirmed in several numerical experiments. [ABSTRACT FROM AUTHOR]

In this paper I present a Minimum Description Length Estimator for number of sources in an anechoic mixture of sparse signals. The criterion is roughly equal to the sum of negative normalized maximum log-likelihood and the logarithm of number of sources. Numerical evidence supports this approach and compares favorabily to both the Akaike (AIC) and Bayesian (BIC) Information Criteria. [ABSTRACT FROM AUTHOR]

A common problem in independent component analysis after prewhitening is to optimize some contrast on the orthogonal or unitary group. A popular approach is to optimize the contrast only with respect to a single angle (Givens rotation) and to iterate this procedure. In this paper we discuss the choice of the sequence of rotations for such so-called Jacobi-based techniques, in the context of joint block-diagonalization (JBD). Indeed, extensive simulations with synthetic data, reported in the paper, illustrates the sensitiveness of this choice, as standard cyclic sweeps appear to often lead to non-optimal solutions. While not being able to guarantee convergence to an optimal solution, we propose a new schedule which, from empirical testing, considerably increases the chances to achieve global minimization of the criterion. We also point out the interest of initializing JBD with the output of joint diagonalization (JD), corroborating the idea that JD could in fact perform JBD up to permutations, as conjectured in previous works. [ABSTRACT FROM AUTHOR]

In the paper we present new Alternating Least Squares (ALS) algorithms for Nonnegative Matrix Factorization (NMF) and their extensions to 3D Nonnegative Tensor Factorization (NTF) that are robust in the presence of noise and have many potential applications, including multi-way Blind Source Separation (BSS), multi-sensory or multi-dimensional data analysis, and nonnegative neural sparse coding. We propose to use local cost functions whose simultaneous or sequential (one by one) minimization leads to a very simple ALS algorithm which works under some sparsity constraints both for an under-determined (a system which has less sensors than sources) and over-determined model. The extensive experimental results confirm the validity and high performance of the developed algorithms, especially with usage of the multi-layer hierarchical NMF. Extension of the proposed algorithm to multidimensional Sparse Component Analysis and Smooth Component Analysis is also proposed. [ABSTRACT FROM AUTHOR]

This paper derives a new algorithm that performs independent component analysis (ICA) by optimizing the contrast function of the RADICAL algorithm. The core idea of the proposed optimization method is to combine the global search of a good initial condition with a gradient-descent algorithm. This new ICA algorithm performs faster than the RADICAL algorithm (based on Jacobi rotations) while still preserving, and even enhancing, the strong robustness properties that result from its contrast. [ABSTRACT FROM AUTHOR]

Resource Allocation in Grid environments to date is generally carried out under the assumption that there is one primary scheduling system scheduling jobs. However, as environments tend towards larger open "utility" Grids it becomes increasingly likely that deployments will involve multiple independent schedulers allocating jobs over the same resources. In this paper we show that, if using current standard scheduling approaches, such multi-scheduler environments may well be prone to serious oscillation problems in resource allocation similar to those commonly found in IP network traffic. Further we demonstrate how common techniques from IP networks - in particularly approaches based on Random Early Detection (RED) buffer management and its subsequent extensions / variations - may provide an effective way to damp or eliminate such oscillations. The paper describes the analogy between multi-scheduler Grid resource allocation and IP network routing and explores the impact of oscillation and RED methods by simulation. [ABSTRACT FROM AUTHOR]

First Come First Served (FCFS) is a policy that is accepted for implementing fairness in a number of application domains such as scheduling in Operating Systems, scheduling web requests, and so on. We also have orthogonal applications of FCFS policies in proving correctness of search algorithms such as Breadth-First Search, the Bellman-Ford FIFO implementation for finding single-source shortest paths, program verification and static analysis. The data structure used to implementing FCFS policies, the queue, suffers from two principal drawbacks, viz., non-trivial verifiability and lack of scalability. In case of large distributed networks, maintaining an explicit queue to enforce FCFS is prohibitively expensive. The question of interest then, is whether queues are required to implement FCFS policies; this paper provides empirical evidence answering this question in the negative. The principal contribution of this paper is the design and analysis of a randomized protocol to implement approximate FCFS policies without queues. From the Software Engineering perspective, the techniques that are developed find direct applications in program verification, model checking, in the implementation of distributed queues and in the design of incremental algorithms for Shortest path problems. [ABSTRACT FROM AUTHOR]

This paper describes FT64 and Multi-FT64, single- and multi-coprocessor systems designed for high performance scientific computing with streams. We give a detailed case study of porting the Mersenne Prime Search problem to FT64 and Multi-FT64 systems. We discuss several special problems associated with streamizing, such as kernel processing granularity, stream organization and workload partitioning for a multi-processor, which are generally applicable to other scientific codes on FT64. Finally, we perform experiments with eight typical scientific applications on FT64. The results show that a 500MHz FT64 achieves over 50% of its peak performance and a 4.2x peak speedup over 1.6GHz Itanium2. An eight processor Multi-FT64 system achieves 6.8x peak speedup over a single FT64. [ABSTRACT FROM AUTHOR]

The abstraction of cryptographic operations by term algebras, called Dolev-Yao models or symbolic cryptography, is essential in almost all tool- supported methods for proving security protocols. Recently significant progress was made - using two conceptually different approaches - in proving that Dolev-Yao models can be sound with respect to actual cryptographic realizations and security definitions. One such approach is grounded on the notion of simulatability, which constitutes a salient technique of Modern Cryptography with a longstanding history for a variety of different tasks. The other approach strives for the so-called mapping soundness - a more recent technique that is tailored to the soundness of specific security properties in Dolev-Yao models, and that can be established using more compact proofs. Typically, both notions of soundness for similar Dolev-Yao models are established separately in independent papers. This paper relates the two approaches for the first time. Our main result is that simulatability soundness entails mapping soundness provided that both approaches use the same cryptographic implementation. Hence, future research may well concentrate on simulatability soundness whenever applicable, and resort to mapping soundness in those cases where simulatability soundness constitutes too strong a notion. [ABSTRACT FROM AUTHOR]

In this paper we introduce a Prover-Verifier model for analyzing the computational complexity of a class of Constraint Satisfaction problems termed Binary Boolean Constraint Satisfaction problems (BBCSPs). BBCSPs represent an extremely general class of constraint satisfaction problems and find applications in a wide variety of domains including constraint programming, puzzle solving and program testing. We establish that each instance of a BBCSP admits a coin-flipping Turing Machine that halts in time polynomial in the size of the input. The prover P in the Prover-Verifier model is endowed with very limited powers; in particular, it has no memory and it can only pose restricted queries to the verifier. The verifier on the other hand is both omniscient in that it is cognizant of all the problem details and insincere in that it does not have to decide a priori on the intended proof. However, the verifier must stay consistent in its responses. Inasmuch as our provers will be memoryless and our verifiers will be asked for extremely simple certificates, our work establishes the existence of a simple, randomized algorithm for BBCSPs. Our model itself serves as a basis for the design of zero-knowledge machine learning algorithms in that the prover ends up learning the proof desired by the verifier. [ABSTRACT FROM AUTHOR]

The aim of this paper is to study communication in networks where nodes fail in a random dependent way. In order to capture fault dependencies, we introduce the neighborhood fault model, where damaging events, called spots, occur randomly and independently with probability p at nodes of a network, and cause faults in the given node and all of its neighbors. Faults at distance at most 2 become dependent in this model and are positively correlated. We investigate the impact of spot probability on feasibility and time of communication in the fault-free part of the network. We show a network which supports fast communication with high probability, if p ≤ 1/clogn. We also show that communication is not feasible with high probability in most classes of networks, for constant spot probabilities. For smaller spot probabilities, high probability communication is supported even by bounded degree networks. It is shown that the torus supports communication with high probability when p decreases faster than 1/n1/2, and does not when p ∈ 1/O(n1/2). Furthermore, a network built of tori is designed, with the same fault-tolerance properties and additionally supporting fast communication. We show, however, that networks of degree bounded by a constant d do not support communication with high probability, if p ∈ 1/O(n1/d). While communication in networks with independent faults was widely studied, this is the first analytic paper which investigates network communication for random dependent faults. [ABSTRACT FROM AUTHOR]

We initiate a study on automata recognizing labelled posets constructed from scattered and countable linear orderings. More precisely, the class of labelled posets considered in this paper is the smallest containing letters, closed under finite parallel operation and sequential product indexed by all countable and scattered linear orderings. The first result of this paper establishes that those labelled posets are precisely the N-free ones. The second result is a Kleene-like theorem, which establishes that the class of languages of labelled posets accepted by branching automata is exactly the class of rational languages. This generalizes both the finite [9] and ω-labelled posets [2,6] cases, and the Kleene-like theorem on words on linear orderings [3]. [ABSTRACT FROM AUTHOR]

Compression is most important when space is in short supply, so compression algorithms are often implemented in limited memory. Most analyses ignore memory constraints as an implementation detail, however, creating a gap between theory and practice. In this paper we consider the effect of memory limitations on compression algorithms. In the first part of the paper we assume the memory available is fixed and prove nearly tight upper and lower bound on how much is needed to compress a string close to its k-th order entropy. In the second part we assume the memory available grows (slowly) as more and more characters are read. In this setting we show that the rate of growth of the available memory determines the speed at which the compression ratio approaches the entropy. In particular, we establish a relationship between the rate of growth of the sliding window in the LZ77 algorithm and its convergence rate. [ABSTRACT FROM AUTHOR]

During the memorizing process, knowledge is memorized closely related to the emotional state. The emotional factor also effects on the decision making process as well as memorization. Accordingly in this paper Reticular Activating System was designed and the knowledge management strategy considering the emotional switching was proposed. We applied this system to the virtual memory and tested the results. [ABSTRACT FROM AUTHOR]

The central problem in training a radial basis function neural network is the selection of hidden layer neurons, which includes the selection of the center and width of those neurons. In this paper, we propose a new method to construct an adaptive RBF neural network classifier based on artificial immune network algorithm. A multiple granularities immune network (MGIN) algorithm is employed to get the candidate hidden neurons and construct an original RBF network including all candidate neurons, and a removing redundant neurons procedure is used to simplify the classifier finally. Some experimental results show that the network obtained tends to generalize well. [ABSTRACT FROM AUTHOR]

A novel feature extraction method based on cloud model for EEG classification is proposed in the paper. The cloud model can transform numerical data to qualitative concept described by a group of characteristics. It provides a new way for concept induction in machine learning. Classification of single trial EEG recorded in a ‘self-paced key typing' experiment is made through feature extraction based on cloud model and linear regression method for the classification of feature vectors. Results of up to 90% classification accuracy on test data set were obtained. The results show that compared with other methods, the feature extraction and translation method for EEG classification in this paper is simple and effective. [ABSTRACT FROM AUTHOR]

Based on the 'bottom-up' programming approach in Artificial Life, Finite-State Machine is adopted to describe the behavior of the individual bee, and the behavior in different period was realized in this paper. As a result, the interaction of the individual bee each other, individual bee and virtual environment produce the Emergence of swarm's collective behaviors. Finally, we apply the graphical interfaces to realize the simulation. [ABSTRACT FROM AUTHOR]

In mammals, the master circadian pacemaker is considered the suprachiasmatic nucleus (SCN) of the hypothalamus. Individual cellular clocks in SCN, the circadian center, are integrated into a stable and robust pacemaker with a period length of about 24 hours, which are remarkably accurate at timing biological events despite the randomness of their biochemical reactions. In this paper, we study the effect of the Light-Dark cycle and environment noise on the daily rhythms of mammals and give some numerical analysis. The results show that the environment noise makes for phase synchronization but it can not make the oscillators get phase synchronization with period of 24-h. On the contrary, the threshold of the strength of light that makes the oscillators to get the phase synchronization with period of 24-h with environment noise is larger than that in the case without environment noise. [ABSTRACT FROM AUTHOR]

This paper introduces strong bisimulation for continuous-time Markov decision processes (CTMDPs), a stochastic model which allows for a nondeterministic choice between exponential distributions, and shows that bisimulation preserves the validity of CSL . To that end, we interpret the semantics of CSL —a stochastic variant of CSL for continuous-time Markov chains—on CTMDPs and show its measure-theoretic soundness. The main challenge faced in this paper is the proof of logical preservation that is substantially based on measure theory. [ABSTRACT FROM AUTHOR]