Catálogo de publicaciones - libros

Alert verification is a process which compares the information referred by an alert with the configuration and topology information of its target system in order to determine if the alert is relevant to its target system. It can reduce false positive alerts and irrelevant alerts. The paper presents an alert verification approach based on multi-level fuzzy comprehensive evaluation. It is effective in achieving false alert and irrelevant alerts reduction, which have been proved by our experiments. The algorithm can deal with the uncertainties better than other alert verification approaches. The relevance score vectors obtained from the algorithm facilitate the formulation of fine and flexible security policies, and further alert processing.

When automatically constructing large programs using program transformations, the number of possible transformations grows very fast. In this paper, we introduce and test a new way of combining several program transformations into one transformation, inspired by the combinatorial concept of Covering Arrays (CA).

We have equipped the ADATE automatic programming system with this new CA transformation and conducted a series of 18 experiments which show that the CA transformation is a highly useful supplement to the existing ADATE transformations.

Texture analysis is an important technique in many image processing areas, such as scene segmentation, object recognition, and shape&depth perception. However, most methods are restricted to issue of computational complexity and supervised problems. Accordingly, we propose a efficient method of segmenting texture that uses unsupervised learning schemes to discover a texture cluster without a pre-knowledge. This method applies 2D Gaussian filters to the clustered region iteratively, and the thresholding value for segmenting is automatically determined by analyzing histogram of the clustered inner-region. It can be acquired by the boundary tracing in the clustered region. In order to show the performance of the proposed method, we have attempted to build a various texture images, and the segmenting quality was measured according to the goodness based on the segmented shape of region. Our experimental results showed that the performance of the proposed method is very successful.

Generally speaking, dependency analysis based Bayesian network learning algorithms are of higher efficiency. J. Cheng’s algorithm is a representative of this kinds of algorithms, while its efficiency could be improved further. This paper presents an efficient Bayesian network learning algorithm, which is an improvement to J. Cheng’s algorithm that uses Mutual Information (MI) and Conditional Mutual Information (CMI) as Conditional Independence (CI) tests. Through redefining the equations for calculating MI and CMI, our algorithm could decrease a large number of basic operations such as logarithms, divisions etc. and reduce the times of access to datasets to the minimum. Moreover, to efficiently calculate CMI, an efficient method for finding an approximate minimum cut-set is proposed in our algorithm. Experimental results show that under the same accuracy, our algorithm is much more efficient than J. Cheng’s algorithm.

We address the problem of building an integrated meta-level framework for time deliberation and parameter control for a system solving a set of hard problems. The trade-off is between the solution qualities achieved for individual problems and the global outcome under the given time-quality constraints. Each problem is modeled as an optimization algorithm whose quality-time performance varies with different control parameter settings. We use the proposed meta-level strategy for generating a deliberation schedule and adaptive cooling mechanism for anytime simulated annealing (ASA) solving task sets. Results on task sets comprising of the traveling salesman problem (TSP) instances demonstrate the efficacy of the proposed control strategies.

Recently, actor-critic methods have drawn much interests in the area of reinforcement learning, and several algorithms have been studied along the line of the actor-critic strategy. This paper studies an actor-critic type algorithm utilizing the RLS(recursive least-squares) method, which is one of the most efficient techniques for adaptive signal processing, together with natural policy gradient. In the actor part of the studied algorithm, we follow the strategy of performing parameter update via the natural gradient method, while in its update for the critic part, the recursive least-squares method is employed in order to make the parameter estimation for the value functions more efficient. The studied algorithm was applied to locomotion of a two-linked robot arm, and showed better performance compared to the conventional stochastic gradient ascent algorithm.

We introduce a novel system of interconnected Self- Organizing Maps that can be used to build feedforward and recurrent networks of maps. Prime application of interconnected maps is in modelling systems that operate with multimodal data as for example in visual and auditory cortices and multimodal association areas in cortex. A detailed example of animal categorization in which the feedworward network of self-organizing maps is employed is presented. In the example we operate with 18-dimensional data projected up on the 19-dimensional hyper-sphere so that the “dot-product” learning law can be used. One potential benefit of the multimodal map is that it allows a rich structure of parallel unimodal processing with many maps involved, followed by convergence into multimodal maps. More complex stimuli can therefore be processed without a growing map size.

We present a new method that improves the performance of the speech recognition system with the speaker-independent (SI) phonetic recognizer. The performance of the speech recognition system with the SI phonetic recognizer is worse than that of the speaker dependent system due to the mismatch between the training utterances and a set of SI models. A new training method that iteratively estimates the phonetic templates and transformation vectors is presented to reduce the mismatch using speaker adaptation techniques. The stochastic matching methods are used to estimate the transformation vectors for speaker adaptation. The experiment performed over actual telephone line shows that a reduction of about 40% in the error rates could be achieved as compared to the conventional method.

Quality of Service (QoS) requirements in networks with uncertain parameters has become a very important research issue in the areas of Internet, mobile networks and distributed systems. This is also a challenging and hard problem for the next generation Internet and mobile networks. It attracts the interests of many people. In this paper we propose a methodology to choose optimized fuzzy controller parameters using the genetic algorithms. Specifically, differentiated service scheme with feedback preference information (FPI) is studied in more detail to illustrate the implement of the new approach. Simulation shows that the approach is efficient, promising and applicable in ad hoc networks. The performance of this scheduler is studied using NS2 and evaluated in terms of quantitative metrics such as packet delivery ratio, average end-to-end delay. Simulation shows that the approach is efficient, promising and applicable in Diff-Serv networks.

In this paper, based on the concept of Discretized Generalized Pursuit Algorithm (DGPA), the discretized generalized pursuit hierarchical structure learning algorithm is constructed which is applied to the hierarchical structure learning automata oprating in the P-model stationary random environment. The efficacy of our algorithm is demonstrated by the numerical simulation, in which the hierarchical structure learning automata is applied to the problem of the mobile robots going through an unknown maze (the maze passage problem of mobile robots).