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In this paper, we aim to address the cell phase identification problem, and two important aspects, the feature extraction methods and the classifier design, are discussed. In our study, we first propose extracting high frequency information of different orientations using Steerable filters. Next, we employ a multi-layer neural network using the back-propagation algorithm to replace K-Nearest Neighbor (KNN) classifier which has been implemented in the Cellular Image Quantitator (CELLIQ) system [3]. Experimental results provide a comparison between the proposed steerable filter features and existing regular features which have been used in published papers [3, 5]. From the comparison, it can be concluded that Steerable filter features can effectively represent the cells in different phases and improve the classification accuracy. Neural network also has a better performance than KNN currently deployed in CELLIQ system [3]. [ABSTRACT FROM AUTHOR]

Many interaction data still exist only in the biomedical literature and they require much effort to construct well-structured data. Discovering useful knowledge from large collections of papers is becoming more important for efficient biological and biomedical researches as genomic research advances. In this paper, we present a relation kernel-based interaction extraction method to extract knowledge efficiently. We extract protein interactions of from text documents with relation kernel and Yeast was used as an example target organism. Kernel for relation extraction is constructed with predefined interaction corpus and set of interaction patterns. The proposed method only exploits shallow parsed documents. Experimental results show that the proposed kernel method achieves a recall rate of 79.0% and precision rate of 80.8% for protein interaction extraction from biomedical document without full parsing efforts. [ABSTRACT FROM AUTHOR]

In self and non-self discrimination (SNSD) model, it is very important to generate a desirable detector set since it decides the performance and scale of the SNSD model based task. By using the famous principle of negative selection in natural immune system, a novel generating algorithm of detector, multiple-point bit mutation method, is proposed in this paper. It utilizes random multiple-point mutation to look for non-self detectors in a large range in the whole space of detectors, such that we can obtain a required detector set in a reasonable computation time. This paper describes the work procedure of the proposed detector generating algorithm. We tested the algorithm by using many datasets and compared it with the Exhaustive Detector Generating Algorithm in details. The experimental results show that the proposed algorithm outperforms the Exhaustive Detector Generating Algorithm both in computational complexities and detection performance. [ABSTRACT FROM AUTHOR]

This paper addresses the important issues of error control for video transmission over 3G, considering the fact that wireless video delivery faces the huge challenge of the high error rate and time variability in wireless channel. This paper proposes a real world statistics based event-trigger bit error rate (BER) model, which can describe and handle the time-varying wireless channel error characteristics better. Moreover, a recurrent neural network is employed to decide the state transfer as a mechanism. Simulation results and comparisons demonstrate effectiveness and efficiency of the proposed method in term of visual performance and transmission efficiency over a variety of wireless channel conditions. [ABSTRACT FROM AUTHOR]

The recommendation G.728 depends on the Levinson-Durbin (L-D) algorithm to update its gain filter coefficients. In this paper, it is contrasted with BP neural network method. Because quantizer has not existed at optimizing gain filter, the quantization SNR can not be used to evaluate its performance. This paper proposes a scheme to estimate SNR so that gain predictor can be separately optimized with quantizer. Using BP neural network filter, the calculation quantity is only 6.7 percent of L-D method's and its average segment SNR is about 0.156dB higher than G.728. It is also used to evaluate the case that excitation vector is 16 or 20 samples, respectively, the BP neural network algorithm has similarly good result. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid neural network model based on the Generalized Learning Vector Quantization(GLVQ) learning algorithm and applies this proposed method to the BIT system of More-Electric Aircraft Electrical Power System (MEAEPS). This paper first discusses the feasibility of application unsupervised neural networks to the BIT system and the representative Generalized LVQ (GLVQ) neural network is selected due to its good performance in clustering analysis. Next, we adopt a new form of loss factor to modify the original GLVQ algorithm in order to make it more suitable for our application. Since unsupervised networks cannot distinguish the similar classes, we add a LVQ layer to the GLVQ network to construct a hybrid neural network model. Finally, the proposed method has been applied to the intelligent BIT system of the MEAEPS, and the results show that the proposed method is promising to improve the performance of the BIT system. [ABSTRACT FROM AUTHOR]

This paper presents a power system stabilizer design that uses a generalized minimum variance-inverse dynamic neuro controller, which is the combination of the inverse dynamic neural model, the generalized minimum variance, and the neuro compensator. An inverse dynamic neural model represents the inverse dynamics of the system. The inverse dynamic neural model is trained to provide control input into the system, which makes the plant output reach the target value at the next sampling time. Once the inverse dynamic neural model is trained, it does not require retuning for cases with other types of disturbances. In this paper, a generalized minimum variance control scheme is adapted to prevent unstable system performance caused by non-minimum phase characteristics. In addition, a neural compensator is designed to compensate for modeling errors. The proposed control scheme is tested in a multimachine power system. [ABSTRACT FROM AUTHOR]

The distribution system problems, such as planning, loss minimization, and energy restoration, usually involve the phase balancing or network reconfiguration procedures. The determination of an optimal phase balance is, in general, a combinatorial optimization problem. This paper proposes optimal reconfiguration of the phase balancing using the neural network, to switch on and off the different switches, allowing the three phases supply by the transformer to the end-users to be balanced. This paper presents the application examples of the proposed method using the real and simulated test data. [ABSTRACT FROM AUTHOR]

Electric Power Load Forecasting is important for the economic and secure operation of power system, and highly accurate forecasting result leads to substantial savings in operating cost and increased reliability of power supply. Conventional load forecasting techniques, including time series methods and stochastic methods, are widely used by electric power companies for forecasting load profiles. However, their accuracy is limited under some conditions. In this paper, neural networks have been successfully applied to load forecasting. Forecasting model with Neural Networks is set up based on the analysis of the characteristics of electric power load, and it works well even with rapidly changing weather conditions. This paper also proposes a novel method to improve the generalization ability of the Neural Networks, and this leads to further increasing accuracy of load forecasting. [ABSTRACT FROM AUTHOR]

In this paper, we propose a grasping control method for robot hand using fuzzy theory and partially- linearized neural network. The robot hand has Double-Octagon Tactile Sensor (D.O.T.S), which has been proposed in our previous papers, to detect grasping force between the grasped object and the robot fingers. Because the measured forces are fluctuant due to the measuring error and vibration of the hand, the tactile information is ambiguous. In order to quickly control the grasping force to prevent the grasped object sliding out off the robot fingers, we apply the possibility theory to deal with the ambiguous problem of the tactile information, and use the partially- linearized neural network (P.L.N.N) to construct a fuzzy neural network. The method proposed in this paper is verified by applying it to practical grasping control of breakable objects, such as eggs, fruits, etc. [ABSTRACT FROM AUTHOR]

This paper considers the problem of PD control of overhead crane in the presence of uncertainty associated with crane dynamics. By using radial basis function neural networks, these uncertainties can be compensated effectively. This new neural control can resolve the two problems for overhead crane control: 1) decrease steady-state error of normal PD control. 2) guarantee stability via neural compensation. By Lyapunov method and input-to-state stability technique, we prove that these robust controllers with neural compensators are stable. Real-time experiments are presented to show the applicability of the approach presented in this paper. [ABSTRACT FROM AUTHOR]

A novel least squares support vector machines based on Mexican hat wavelet kernel is presented in the paper. The wavelet kernel which is admissible support vector kernel is characterized by its local analysis and approximate orthogonality, and we can well obtain estimates for regression by applying a least squares wavelet support vector machines (LS-WSVM). To test the validity of the proposed method, this paper demonstrates that LS-WSVM can be used effectively for the identification and adaptive control of nonlinear dynamical systems. Simulation results reveal that the identification and adaptive control schemes suggested based on LS-WSVM gives considerably better performance and show faster and stable learning in comparison to neural networks or fuzzy logic systems. LS-WSVM provides an attractive approach to study the properties of complex nonlinear system modeling and adaptive control. [ABSTRACT FROM AUTHOR]

Presented in this paper is neural network based adaptive control for a class of affine nonlinear systems in the strict-feedback form with unknown nonlinearities. A popular recursive design methodology - backstepping is employed to systematically construct feedback control laws and associated Lyapunov functions. The significance of this paper is to make best use of available signals, avoid unnecessary parameterization, and minimize the node number of neural networks as on-line approximators. The design assures that all the signals in the closed loop are semi-globally uniformly, ultimately bounded and the outputs of the system converges to a tunable small neighborhood of the desired trajectory. Novel parameter tuning algorithms are obtained on a more practical basis. [ABSTRACT FROM AUTHOR]

In this paper, the topic of fractional order digital differentiators (FODD) is designed using neural networks approximation method. First, FODD amplitude response is given in the form of sum of exponential basis functions. Then, the exponential basis function neural network is used to approximate FODD amplitude response. Finally, some examples compared with others' method are given to illustrate the advantages of this paper approach. [ABSTRACT FROM AUTHOR]

A new approach to object detection using image icons based on Unit-linking PCNN (Pulse Coupled Neural Network) is introduced in this paper. Unit-linking PCNN, which has been developed from PCNN exhibiting synchronous pulse bursts in cat and monkey visual cortexes, is a kind of time-space-coding SNN (Spiking Neural Network). We have used Unit-linking PCNN to produce the global image icons with translation and rotation invariance. Unit-linking PCNN image icon (namely global image icons) is the 1-dimentional time series, and is a kind of image feature extracted from the time information that Unit-linking PCNN code the 2-dimentional image into. Its translation and rotation invariance is a good property in object detection. In addition to translation, rotation invariance, the object detection approach in this paper is also independent of scale variation. [ABSTRACT FROM AUTHOR]

This paper presents an evolutionary cellular automata based neural systems (Evolutionary CANS) for visual servoing of RV-M2 robot manipulator. The architecture of CANS consist of a two-dimensional (2-D) array of basic neurons. Each neuron of CANS has local connections only with contiguous neuron and acts as a form of pulse according to the dynamics of the chaotic neuron model. CANS are generated from initial cells according to the cellular automata (CA) rule. Therefore neural architecture is determined by both initial pattern of cells and production rule of CA. Production rules of CA are evolved based on a DNA coding. DNA coding has the redundancy and overlapping of gene and is apt for representation of the rule. In this paper we show the general expression of CA rule and propose translating method from DNA code to CA rule. In addition, we present visual servoing application using evolutionary CANS. [ABSTRACT FROM AUTHOR]

This paper first introduces the system requirement and the system flow of the auto-plotting system. As the data points needed by the auto-plotting system coming from the remote speech signals, to reach high recognition accuracy, the Hidden Markov Model (HMM) approach was chosen as the speech recognition approach. Then the paper is detailed on the speaker dependent (SD), speaker independent (SI) and speaker adaptive (SA) speech recognition methods. We proposed the n-speech models SD system as the recognition system to gain the highest recognition performance in varying speech environments. However the system required that searching for the optimal model from the database should finish in 5 minutes, so the paper finally describes how the Self-Organizing Map (SOM) was used to pre clustering to the n-speech models, to decrease the time for speech recognition and results evaluation and decrease matching time, Experiments show the n-speech models SD system can select the best-matching model in the limited time and improve the average speech recognition accuracy to 97.2. It ideally suits the system requirements. [ABSTRACT FROM AUTHOR]

Linear Discriminant Analysis (LDA) is a very powerful method in pattern recognition. But it is difficult to realize online processing for data stream. In this paper, a new adaptive LDA method is proposed. We decompose the online LDA problem into two adaptive PCA problems and develop a fixed point adaptive PCA to implement adaptive LDA. Online updating of in-class scatter matrix Sw(t) and covariance matrix Cx(t) are derived in this paper. Simulation results show that the proposed method has no learning rate, fast convergence and less time-consuming. [ABSTRACT FROM AUTHOR]

Locally linear embedding (LLE) is one of the methods intended for dimensionality reduction, which relates to the number K of nearest-neighbors points to be initially chosen. So, in this paper, we want that the parameter K has little influence on the dimension reduction, that is to say, the parameter K can be widely chosen while not influence the effect of dimension reduction. Therefore, we propose a method of improved LLE, which uses new distance computing for weight of K nearest-neighbors points in LLE. Thus, even when the number K is little, the improved LLE can get good results of dimension reduction, while the traditional LLE needs a larger number of K to get the same results. When the number K of the nearest neighbors gets larger, test in this paper has proved that the improved LLE can still get correct results. [ABSTRACT FROM AUTHOR]

Constructive fuzzy neural networks (i.e., CFNN) proposed in [1] cannot be used for non-numerical data. In order to use CFNN to deal with non-numerical complicated data, rough set theory is adopted to improve the CFNN in this paper. First of all, we use rough set theory to extract core set of non-numerical attributes and decrease number of dimension of samples by reducing redundancy. Secondly, we can pre-classify the samples according to non-numerical attributes. Thirdly, we use CFNN to classify the samples according to numerical attributes. The proposed method not only increases classification accuracy but also speeds up classification process. Finally, the classification of wireless communication signals is given as an example to illustrate the validation of the proposed method in this paper. [ABSTRACT FROM AUTHOR]

For a well-trained radial basis function neural network, this paper proposes a novel input stochastic sensitivity definition and gives its computational formula assuming the inputs are modelled by normal distribution random variables. Based on this formula, one can calculate the magnitude of sensitivity for each input (i.e. feature), which indicates the degree of importance of input to the output of neural network. When there are redundant inputs in the training set, one always wants to remove those redundant features to avoid a large network. This paper shows that removing redundant features or selecting significant features can be completed by choosing features with sensitivity over a predefined threshold. Numerical experiment shows that the new approach to feature selection performs well. [ABSTRACT FROM AUTHOR]

Blind identification of mixing matrix approach and the corresponding algorithm are proposed in this paper. Usually, many conventional Blind Source Separation (BSS) methods separate the source signals by estimating separated matrix. Different from this way, we present a new BSS approach in this paper, which achieves BSS by directly identifying the mixing matrix, especially for underdetermined case. Some experiments are conducted to check the validity of the theory and availability of the algorithm in this paper. [ABSTRACT FROM AUTHOR]

Comparative study of extreme learning machine (ELM) and support vector machine (SVM) is investigated in this paper. A cross validation method for determining the appropriate number of neurons in the hidden layer is also proposed in this paper. ELM proposed by Huang, et al [3] is a novel machine-learning algorithm for single hidden-layer feedforward neural network (SLFN), which randomly chooses the input weights and hidden-layer bias, and analytically determines the output weights optimally instead of tuning them. This algorithm tends to produce good generalization ability and obtain least experience risk simultaneously with solid foundations. Benchmark tests of a real Tennessee Eastman Process (TEP) are carried out to validate its superiority. Compared with SVM, this proposed algorithm is much faster and has better generalization performance than SVM in the case studied in this paper. [ABSTRACT FROM AUTHOR]

This paper presents an SVM classification algorithm with predesigned error correction ability by incorporating the error control coding schemes used in digital communications into the classification algorithm. The algorithm is applied to face recognition problems in the paper. Simulation experiments are conducted for different SVM-based classification algorithms using both PCA and Fisherface features as input vectors respectively to represent the images with dimensional reduction, and performance analysis is made among different approaches. Experiment results show that the error correction SVM classifier of the paper outperforms other commonly used SVM-based classifiers both in recognition rate and error tolerance. [ABSTRACT FROM AUTHOR]

In this paper, a clustering algorithm based on Gradient Based Fuzzy C-Means with a Mercer Kernel, called GBFCM (MK), is proposed. The kernel method adopted in this paper implicitly performs nonlinear mapping of the input data into a high-dimensional feature space. The proposed GBFCM(MK) algorithm is capable of dealing with nonlinear separation boundaries among clusters. Experiments on a synthetic data set and several real MPEG data sets show that the proposed algorithm gives better classification accuracies than both the conventional k-means algorithm and the GBFCM. [ABSTRACT FROM AUTHOR]

In view of the character of saturation linearity of output functions of neurons of the cellular neural networks, the method decomposing the state space to sub-regions is adopted to study almost sure exponential stability on delayed cellular neural networks which are in the noised environment. When perturbed terms in the model of the neural network satisfy Lipschitz condition, some algebraic criteria are obtained. The results obtained in this paper show that if an equilibrium of the neural network is the interior point of a sub-region, and an appropriate matrix related to this equilibrium has some stable degree to stabilize the perturbation, then the equilibrium of the delayed cellular neural network can still remain the property of exponential stability. All results in the paper is only to compute eigenvalues of matrices. [ABSTRACT FROM AUTHOR]

In this paper, the global exponential stability of a class of recurrent cellular neural networks with reaction-diffusion and variable time delays was studied. When neural networks contain unbounded activation functions, it may happen that equilibrium point does not exist at all. In this paper, without assuming the boundedness, monotonicity and differentiability of the active functions, the algebraic criteria ensuring existence, uniqueness and global exponential stability of the equilibrium point of neural networks are obtained. [ABSTRACT FROM AUTHOR]

In this paper, we discuss delayed Cohen-Grossberg neural networks with time-varying and distributed delays and investigate their global asymptotical stability of the equilibrium point. The model proposed in this paper is universal. A set of sufficient conditions ensuring global convergence and globally exponential convergence for the Cohen-Grossberg neural networks with time-varying and distributed delays are given. Most of the existing models and global stability results for Cohen-Grossberg neural networks, Hopfield neural networks and cellular neural networks can be obtained from the theorems given in this paper. [ABSTRACT FROM AUTHOR]

In this paper, the global uniform asymptotical stability is studied for delayed neutral-type neural networks by constructing appropriate Lyapunov functional and using the linear matrix inequality (LMI) approach. The main condition given in this paper is dependent on the size of the measure of the space, which is usually less conservative than space-independent ones. Finally, a numerical example is provided to demonstrate the effectiveness and applicability of the proposed criteria. [ABSTRACT FROM AUTHOR]

In this paper we studied a new dynamic evolution model on phase encoding in population of neuronal oscillators under condition of different phase, and investigated neural information processing in cerebral cortex and dynamic evolution under action of different stimulation signal. It is obtained that evolution of the averaging number density along with time in space of three dimensions is described in different cluster of neuronal oscillators firing action potential at different phase space by means of method of numerical analysis. The results of numerical analysis show that the dynamic model proposed in this paper can be used to describe mechanism of neurodynamics on attention and memory. [ABSTRACT FROM AUTHOR]

Fuzzy integrals have attracted the attention of many researchers as a solution for expressing the interactions between classifiers in multiple-classifier fusion. In a classifier fusion system based on fuzzy integrals, the fuzzy measures will have a major impact on a system's performance. Much work has been carried out by numerous authors on how to determine the fuzzy measures to improve results. Our paper presents some new characteristics of multiple-classifier fusion based on fuzzy integrals. This paper discusses the conditions under which the fusion system must give the incorrect classification and that the fusion system can give the correct classification even if all classifiers have given an incorrect classification. It will be helpful for improving classifier fusion systems and designing classifiers in application. [ABSTRACT FROM AUTHOR]

An artificial neural network was used for sample preprocessing in this paper. Firstly, the data points were classified into three types as follows: the high load type, the medium load type and the low load type. Then, the artificial neural network was adopted to forecast the load type of the predict point. Finally, a support vector machine forecasting model was created on the basis of data points whose load type is the same as the predict point. Comparisons were made between different methods. The results show that the model established in this paper is better than other methods in forecasting accuracy and computing speed. [ABSTRACT FROM AUTHOR]

This paper contributes to the analysis and prediction of deviate intentional behaviour of human operators in Human-Machine Systems using Artificial Neural Networks that take uncertainty into account. Such deviate intentional behaviour is a particular violation, called Barrier Removal. The objective of the paper is to propose a predictive Benefit-Cost-Deficit model that allows a multi-reference, multi-factor and multi-criterion evaluation. Human operator evaluations can be uncertain. The uncertainty of their subjective judgements is therefore integrated into the prediction of the Barrier Removal. The proposed approach is validated on a railway application, and the prediction convergence of the uncertainty-integrating model is demonstrated. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology to generate the personalized answer based on a question situation. To construct the question situation, personal learning characteristics should be obtained by principal component analysis and question type vector is used in the process of semantic analysis. After these analyses, the question situation is constructed based on a harmony network. The answer parameters, including answer depth and answer presentation pattern, are calculated by harmony function. According to these parameters, the personalized answer is matched by the adaptive neuro-fuzzy inference (ANFI). The system architecture of personalized answer generation is proposed in this paper and takes a learner's question to demonstrate. [ABSTRACT FROM AUTHOR]

Many methods based on rough sets to deal with incomplete information system have been proposed in recent years. However, they are only suitable for the nominal datasets. So far only a few methods based on rough sets to deal with incomplete information system with continuous-valued attributes have been proposed. In this paper we propose one generalized model of rough sets to reduce continuous-valued attributes and learn some rules in an incomplete information system. The definition of a relative discernible measure is firstly proposed, which is the underlying concept to redefine the concepts of knowledge reduction such as the reduct and core. We extend a number of underlying concepts of knowledge reduction (such as the reduct and core), and finally propose a heuristic algorithm to generate fuzzy reduct from initial data. The main contribution of this paper is that the underlying relationship between the reduct and core of rough sets is proved to be still correct after our extension. The advantage of the proposed method is that instead of preprocessing continuous data by discretization or fuzzification, we can reduce an incomplete information system with continuous-valued attributes directly based on the generalized model of rough sets, Finally a numerical example is given to show the feasibility of our proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a new definition of string stability with bounded input from the input-to-state view. By viewing the interconnection as a kind of input to a subsystem, it specifies sufficient conditions of string stability for a class of directed circular interconnected nonlinear systems, which is based on the input-to-state stability analysis and singular perturbation theory. The proof is first conducted on a system with two subsystems, and then expanded to finite N subsystems. Furthermore, directed graph is used as an illustrative tool in this paper. [ABSTRACT FROM AUTHOR]

This paper proposes the core theoretical foundations on design of the Compound Zeno Behavior in hierarchical hybrid behaviors, including the definitions of different types of behaviors and some theorems. New hierarchical fuzzy behavior-based control architecture of an autonomous mobile robot using the information extracted from its sensors in unknown environments is presented. After that, the paper focuses on the coordination and fusion of the elementary behaviors, which are achieved by means of fuzzy reasoning scheme. Simulation results illustrate the effective performance of the control architecture. [ABSTRACT FROM AUTHOR]

Internet auction is not only an integral part of Electronic Commerce but is also becoming a promising field for applying autonomous agents and multi-agent system (MAS) technologies. And auction, as an efficient resource allocation method, has an important role in MAS problems and as such is receiving increasing attention from scholars. This paper suggests a protocol (VAMA) and strategies for multi-attribute auction. Some useful properties such as strategy-proof are also proven. This paper also includes an analysis of the strategy of the buyer, and gives a decentralized mechanism for VAMA implementation as well as discussing some questions about VAMA. Finally, this protocol is compared with existing ones, improving on the work of Esther David etc. [ABSTRACT FROM AUTHOR]

As reputation mechanism has been widely accepted and adopted to enhance trust in electronic communities, how to cope with the attack and disturbance problems on reputation mechanism, such as collusion, malicious or unfair rating, becomes a key challenge. This paper extends the normal mechanism, which mainly focuses on the trustworthiness of transactions, to a double-layer reputation mechanism, by distinguishing two type reputations: capability reputation and rating reputation. Based on the double-layer reputations, we present the Rating Reputation Feedback (RRF) mechanism to confront above problems. Basic concepts, key issues, instantiated sample and the effectiveness of RRF mechanism are discussed in the paper. [ABSTRACT FROM AUTHOR]

Digital signature is one of the most important primitives in public key cryptography. It provides authenticity, integrity and non-repudiation to many kinds of applications. On signer privacy however, it is generally unclear or suspicious of whether a signature scheme itself can guarantee the anonymity of the signer. In this paper, we give some affirmative answers to it. We formally define the signer anonymity for digital signature and propose some schemes of this type. We show that a signer anonymous signature scheme can be very useful by proposing a new anonymous key exchange protocol which allows a client Alice to establish a session key with a server Bob securely while keeping her identity secret from eavesdroppers. In the protocol, the anonymity of Alice is already maintained when Alice sends her signature to Bob in clear, and no additional encapsulation or mechanism is needed for the signature. We also propose a method of using anonymous signature to solve the collusion problem between organizers and reviewers of an anonymous paper review system. [ABSTRACT FROM AUTHOR]

Identity-based encryption (IBE) is one of the most important primitives in cryptography, and various security notions of IBE (e.g., IND-ID-CCA2, NM-ID-CCA2, IND-sID-CPA etc.) have been introduced. The relations among them have been clarified recently. This paper, for the first time, investigates the security of IBE in the universally composable (UC) framework. This paper first defines the UC-security of IBE, i.e., we define the ideal functionality of IBE, $\mathcal{F}_\mathrm{IBE}$. We then show that UC-secure IBE is equivalent to conventionally-secure (IND-ID-CCA2-secure) IBE. Keywords: identity-based encryption, IND-ID-CCA2, universal composition. [ABSTRACT FROM AUTHOR]

Wang et al. have proposed collision attacks for various hash functions. Their approach is to first construct a differential path, and then determine the conditions (sufficient conditions) that maintain the differential path. If a message that satisfies all sufficient conditions is found, a collision can be generated. Therefore, in order to apply the attack of Wang et al., we need techniques for constructing differential paths and for determining sufficient conditions. In this paper, we propose the "SC algorithm", an algorithm that can automatically determine the sufficient conditions. The input of the SC algorithm is a differential path, that is, all message differentials and differentials of the chaining variables. The SC algorithm then outputs the sufficient conditions. The computation time of the SC algorithm is within few seconds. In applying the method of Wang et al. to MD5, there are 3 types of sufficient conditions: conditions for controlling the carry length when differentials appear in the chaining variables, conditions for controlling the output differentials of the Boolean function when the input variables of the function have differentials and conditions for controlling the relationship between the carry effect and left rotation operation. Sufficient conditions for SHA-1, SHA-0 and MD4 consist of only Type 1 and Type 2. Type 3 is unique to MD5. The SC algorithm can construct Type 1 and Type 2 conditions; we use the method of Liang et al. to construct Type 3 conditions. The complexity of the collision attack depends on the number of sufficient conditions needed. The SC algorithm constructs the fewest possible sufficient conditions. To check the feasibility of the SC algorithm, we apply it to the differential path of MD5 given by Wang et al. It is shown to yield 12 fewer conditions than the latest work on MD5. The SC algorithm is applicable to the MD-family and the SHA-family. This paper focuses on the sufficient conditions of MD5, but only as an example. Keywords: Hash Function, Collision Attack, Differential Path, Sufficient Condition. [ABSTRACT FROM AUTHOR]

This paper presents a new coordinate system for elliptic curves that accelerates the elliptic curve addition and doubling over an optimal extension field (OEF). Many coordinate systems for elliptic curves have been proposed to accelerate elliptic curve cryptosystems. This paper is a natural extension of these papers and the new coordinates are much faster when the elliptic curve is defined over an OEF. This paper also shows that the total computational cost is reduced by 28% when the elliptic curve is defined over ${\mathbb F}_{q^m}$, q = 261−1 for m = 5 and the speed of a scalar multiplication on an elliptic curve becomes 41.9 μsec per operation on a 2.82-GHz Athlon 64 FX PC. Keywords: elliptic curve cryptosystem, coordinate system, OEF, fast software implementation. [ABSTRACT FROM AUTHOR]

Scheduling is a key concern for the execution of performance-driven Grid applications. In this paper we comparatively examine different existing approaches for scheduling of scientific workflow applications in a Grid environment. We evaluate three algorithms namely genetic, HEFT, and simple "myopic" and compare incremental workflow partitioning against the full-graph scheduling strategy. We demonstrate experiments using real-world scientific applications covering both balanced (symmetric) and unbalanced (asymmetric) workflows. Our results demonstrate that full-graph scheduling with the HEFT algorithm performs best compared to the other strategies examined in this paper. [ABSTRACT FROM AUTHOR]

The paper presents various transparency issues that have to be considered during development of object-oriented Grid applications based on virtual repositories. Higher-level transparencies, such as location, heterogeneity, fragmentation, replication, redundancy, indexing and service provider transparency assure new information processing culture greatly supporting the development, operation and maintenance of Grid database applications. The paper discusses some requirements for a virtual repository that is a kernel of a Grid database and a general architecture of such systems. The architecture is based on object-oriented updatable database views that serve, in particular, as adapters of local servers and integrators/mediators on the level of a global virtual repository. Finally, some issues of the development of Grid database applications are presented. [ABSTRACT FROM AUTHOR]

Solution methods for very large scale optimization problems are addressed in this paper. Interior point methods are demonstrated to provide unequalled efficiency in this context. They need a small (and predictable) number of iterations to solve a problem. A single iteration of interior point method requires the solution of indefinite system of equations. This system is regularized to guarantee the existence of triangular decomposition. Hence the well-understood parallel computing techniques developed for positive definite matrices can be extended to this class of indefinite matrices. A parallel implementation of an interior point method is described in this paper. It uses object-oriented programming techniques and allows for exploiting different block-structures of matrices. Our implementation outperforms the industry-standard optimizer, shows very good parallel efficiency on massively parallel architecture and solves problems of unprecedented sizes reaching 109 variables. [ABSTRACT FROM AUTHOR]

This paper deals with the possibilities of applying evolutionary methods in computer aided designThe work reported in this paper was partially supported by the State Committee for Scientific Research grant 8T07A01621.. As the representation is one of the main issues in any computer aided design system here an approach using hierarchically organized data is presented. It is based on traditional graph structures extended to be able to represent different types of relations within a designed object. Genetic operators working on such graphs and other elements of an evolutionary system are also presented. The method is illustrated by examples from two different domains. One of them is a chair design system based on proposed method. The other one is derived from the design of skeletal structures of transmission towers. [ABSTRACT FROM AUTHOR]

The paper is devoted to computational grids applications in evolutionary optimization of mechanical structures. The LCG2 and UNICORE grid middleware are used. The optimization is performed by means of the distributed evolutionary algorithm. The fitness function is computed using the finite element method. The numerical example is presented in the paper. [ABSTRACT FROM AUTHOR]

The paper considers the problem of determining an optimal observation schedule for discrimination between competing models of a dynamic process. To this end, an approach originating in optimum experimental design is applied. Its use necessitates solving some maximin problem. Unfortunately, a high computational cost is the main reason for limited practical applications, especially regarding distributed parameter systems. The paper constitutes an attempt to overcome such an impediment via a parallel implementation performed on a Linux cluster. The resulting numerical scheme is validated on a simulation example motivated by problems arising in chemical kinetics. [ABSTRACT FROM AUTHOR]

This paper describes the Bar-Ilan system participating in the Recognising Textual Entailment Challenge. The paper proposes first a general probabilistic setting that formalizes the notion of textual entailment. We then describe a concrete alignment-based model for lexical entailment, which utilizes web co-occurrence statistics in a bag of words representation. Finally, we report the results of the model on the Recognising Textual Entailment challenge dataset along with some analysis. [ABSTRACT FROM AUTHOR]