Informatica

The rapid development of network technologies has made the web a huge information source with its own characteristics. In most cases, traditional database-based technologies are no longer suitable for web information processing and management. For effectively processing and managing web information, it is necessary to reveal intrinsic relationships/structures among concerned web information objects such as web pages. In this work, a set of web pages that have their intrinsic relationships is called a web page community. This paper proposes a matrix-based model to describe relationships among concerned web pages. Based on this model, intrinsic relationships among pages could be revealed, and in turn a web page community could be constructed. The issues that are related to the application of the model are deeply investigated and studied. The concepts of community and intrinsic relationships, as well as the proposed matrix-based model, are then extended to other application areas such as biological data processing. Some application cases of the model in a broad range of areas are presented, demonstrating the potentials of this matrix-based model.

In this paper, the information theory interpreted as the neural network systems of the brain is considered for information conveying and storing. Using the probability theory and specific properties of the neural systems, some foundations are presented. The neural network model proposed and computational experiments allow us to draw a conclusion that such an approach can be applied in storing, coding, and transmission of information.

In this paper, the relative multidimensional scaling method is investigated. This method is designated to visualize large multidimensional data. The method encompasses application of multidimensional scaling (MDS) to the so-called basic vector set and further mapping of the remaining vectors from the analyzed data set. In the original algorithm of relative MDS, the visualization process is divided into three steps: the set of basis vectors is constructed using the k-means clustering method; this set is projected onto the plane using the MDS algorithm; the set of remaining data is visualized using the relative mapping algorithm. We propose a modification, which differs from the original algorithm in the strategy of selecting the basis vectors. The experimental investigation has shown that the modification exceeds the original algorithm in the visualization quality and computational expenses. The conditions, where the relative MDS efficiency exceeds that of standard MDS, are estimated.

In this article we present the general architecture of a hybrid neuro-symbolic system for the selection and stepwise elimination of predictor variables and non-relevant individuals for the construction of a model. Our purpose is to design tools for extracting the relevant variables and the relevant individuals for an automatic training from data. The objective is to reduce the complexity of storage, therefore the complexity of calculation, and to gradually improve the performance of ordering, that is to say to arrive at a good quality training.

In this paper we propose a modified framework of support vector machines, called Oblique Support Vector Machines(OSVMs), to improve the capability of classification. The principle of OSVMs is joining an orthogonal vector into weight vector in order to rotate the support hyperplanes. By this way, not only the regularized risk function is revised, but the constrained functions are also modified. Under this modification, the separating hyperplane and the margin of separation are constructed more precise. Moreover, in order to apply to large-scale data problem, an iterative learning algorithm is proposed. In this iterative learning algorithm, three different schemes for training can be found in this literature, including pattern-mode learning, semi-batch mode learning and batch mode learning. Besides, smooth technique is adopted in order to convert the constrained nonlinear programming problem into unconstrained optimum problem. Consequently, experimental results and comparisons are given to demonstrate that the performance of OSVMs is better than that of SVMs and SSVMs.

A key exchange (or agreement) protocol is designed to allow two entities establishing a session key to encrypt the communication data over an open network. In 1990, Gunther proposed an identity-based key exchange protocol based on the difficulty of computing a discrete logarithm problem. Afterwards, several improved protocols were proposed to reduce the number of communication steps and the communicational cost required by Gunther's protocol. This paper presents an efficient identity-based key exchange protocol based on the difficulty of computing a discrete logarithm problem. As compared with the previously proposed protocols, it has better performance in terms of the computational cost and the communication steps. The proposed key exchange protocol provides implicit key authentication as well as the desired security attributes of an authenticated key exchange protocol.

The key agreement protocol based on infinite non-commutative group presentation and representation levels is proposed.

In this paper we consider branching time temporal logics of knowledge and belief. These logics involve the discrete time linear temporal logic operators “next” and “until” with the branching temporal logic operator “on all paths”. The latter operator is interpreted with respect to a version of the bundle semantics. In addition the temporal logic of knowledge (belief) contains an indexed set of unary modal operators “agent i knows” (“agent i believes”) and it contains the modality of common knowledge (belief). For these logics we present sequent calculi with a restricted cut rule. Thus, we get proof systems where proof-search becomes decidable. The soundness and completeness for these calculi are proved.

The objective of this research is to construct parallel models that simulate the behavior of artificial neural networks. The type of network that is simulated in this project is the counterpropagation network and the parallel platform used to simulate that network is the message passing interface (MPI). In the next sections the counterpropagation algorithm is presented in its serial as well as its parallel version. For the latter case, simulation results are given for the session parallelization as well as the training set parallelization approach. Regarding possible parallelization of the network structure, there are two different approaches that are presented; one that is based to the concept of the intercommunicator and one that uses remote access operations for the update of the weight tables and the estimation of the mean error for each training stage.

Deniable authenticated protocol is a new cryptographic authentication protocol that enables a designated receiver to identify the source of a given message without being able to prove the identity of the sender to a third party. Therefore, it can be applied to some particular situations in electronic commerce. In this paper, we formally define the security model for the non-interactive ID-based deniable authentication protocol and present a new efficient ID-based deniable authentication protocol based on RSA assumption. What's more, we also use the techniques from provable security to analyze the security of our proposed protocol.