Pregrado - Ciencias de la Computación

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Introduction to the SAM-S M* and MAM-S M* families
(Scopus, 2005) Cuadros Vargas, Ernesto; Romero, Francelin
In this paper, two new families of constructive Self-Organizing Maps (SOMs), SAM-SOM* and MAM-SOM*, are proposed. These families are specially useful for information retrieval from large databases, high-dimensional spaces and complex distance functions which usually consume a long time. They are generated by incorporating Spatial Access Method (SAM) and Metric Access Method (MAM) into SOM with the maximum insertion rate, i.e. the case when a new unit is created for each pattern presented to the network. In this specific case, the network presents interesting advantages and acquires new properties which are quite different of traditional SOM. In a constructive SOM, if new units are rarely inserted into network, the training algorithm would probably need a long time to converge. On the other hand, if new units are inserted frequently, the training algorithm would not have enough time to adapt these units to the data distribution. Besides, training time is increased because the search for the winning neuron is traditionally performed sequentially. The use of SAM and MAM combined with SOM open the possibility of training constructive SOM with as much units as existing patterns with less time and interesting advantages compared with both models: Kohonen network SOM and SAM-SOM model (SOM using SAM). Advantages and drawbacks of these new families are also discussed. These new families are useful to improve both SOM and SAM techniques.
Using large databases and self-organizing maps without tears
(Scopus, 2006) Bedregal, Carlos; Cuadros Vargas, Ernesto
Nowadays the need to process lots of complex multimedia databases is more frequent. Recent investigations such as MAM-SOM* and SAM-SOM* families propose the combination of Self-Organizing Maps (SOM) with Access Methods for a faster similarity information retrieval. In this investigation we present experimental results using recent Access Methods such as Slim-Tree and Omni-Sequential that show the improvement acquired by these techniques and their properties in contrast with a traditional SOM network, observing up to 90% of performance improvement. © 2006 IEEE.
A biologically motivated computational architecture inspired in the human immunological system to quantify abnormal behaviors to detect presence of intruders
(Scopus, 2006) Florez Choque, Omar; Cuadros Vargas, Ernesto
In this article is presented a detection model of intruders by using an architecture based in agents that imitates the principal aspects of the Immunological System, such as detection and elimination of antigens in the human body. This model is based on the hypothesis of an intruder which is a strange element in the system, whereby can exist mechanisms able to detect their presence. We will use recognizer agents of intruders (Lymphocytes-B) for such goal and macrophage agents (Lymphocytes-T) for alerting and reacting actions. The core of the system is based in recognizing abnormal patterns of conduct by agents (Lymphocytes-B), which will recognize anomalies in the behavior of the user, through a catalogue of Metrics that will allow us quantify the conduct of the user according to measures of behaviors and then we will apply Statistic and Data Minig technics to classify the conducts of the user in intruder or normal behavior. Our experiments suggest that both methods are complementary for this purpose. This approach was very flexible and customized in the practice for the needs of any particular system. © 2006 International Federation for Information Processing.
DBM*-Tree: An efficient metric acces method
(Scopus, 2007) Ocsa, Alexander; Cuadros Vargas, Ernesto
In this paper we propose a new dynamic Metric Access Method (MAM) called DBM*-Tree, which uses precomputed distances to reduce the construction cost avoiding repeated calculus of distance. Making use of the pre-calculated distances cost of similarity queries are also reduced by taking various local representative objects in order to increment the pruning of irrelevant elements during the query. We also propose a new algorithm to select the suitable subtree in the insertion operation, which is an evolution of the previous methods. Empiric tests on real and synthetic data have shown evidence that DBM*-Tree requires 25 % less average distance computing than Density Based Metric Tree (DBM-Tree) which is one of the most efficient and recent MAM found in the literature. © Copyright 2007 ACM.
DB-GNG: A constructive self-organizing map based on density
(Scopus, 2007) Ocsa, Alexander; Bedregal, Carlos; Cuadros Vargas, Ernesto
Nowadays applications require efficient and fast techniques due to the growing volume of data and its increasing complexity. Recent studies promote the use of Access Methods (AMs) with Self-Organizing Maps (SOMs) for a faster similarity information retrieval. This paper proposes a new constructive SOM based on density, which is also useful for clustering. Our algorithm creates new units based on density of data, producing a better representation of the data space with a less computational cost for a comparable accuracy. It also uses AMs to reduce considerably the Number of Distance Calculations during the training process, outperforming existing constructive SOMs by as much as 89%. ©2007 IEEE.
Improving human computer interaction through spoken natural language
(Scopus, 2007) Florez Choque, Omar; Cuadros Vargas, Ernesto
The fastest and most straightforward way of communication for mankind is the voice. Therefore, the best way to interact with computers should be the voice too. That is why at the moment men are searching new ways to interact with computers. This interaction is improved if the words spoken by the speaker are organized in Natural Language. In this article, it is proposed a model to recover information from databases through queries in Spanish Natural Language using the voice as the way of communication. This model incorporates a Hybrid Intelligent System based on Genetic Algorithms and a Kohonen Self-Organizing Map (SOM) to recognize the present phonemes in a word through time. This approach allows us to remake up a word with speaker independence. Furthermore, it is proposed the use of a compiler with type 2 grammar according to the Chomsky Hierarchy to support the syntactic and semantic structure in Spanish language. Our experiments suggest that the Spoken Natural Language improves notably the Human-Computer interaction when compared with traditional input methods such as: mouse or keybord. © 2007 IEEE.
Rotation-invariant and scale-invariant steerable pyramid decomposition for texture image retrieval
(Scopus, 2007) Montoya Zegarra, Javier; Leite, Neucimar; da Silva Torres, Ricardo
This paper proposes a new rotation-invariant and scale-invariant representation for texture image retrieval based on Steerable Pyramid Decomposition. By calculating the mean and standard deviation of decomposed image subbands, the texture feature vectors are extracted. To obtain rotation or scale invariance, the feature elements are aligned by considering either the dominant orientation or dominant scale of the input textures. Experiments were conducted on the Brodatz database aiming to compare our approach to the conventional Steerable Pyramid Decomposition, and a recent proposal for texture characterization based on Gabor Wavelets with regard to their retrieval effectiveness. Results demonstrate the superiority of the proposed method in rotated and scaled image datasets. © 2007 IEEE.
Rotation-invariant texture recognition
(Scopus, 2007) Montoya Zegarra, Javier; Paulo Papa, Joao; Leite, Neucimar; da Silva Torres, Ricardo; Falcao, Alexandre
This paper proposes a new texture classification system, which is distinguished by: (1) a new rotation-invariant image descriptor based on Steerable Pyramid Decomposition, and (2) by a novel multi-class recognition method based on Optimum Path Forest. By combining the discriminating power of our image descriptor and classifier, our system uses small size feature vectors to characterize texture images without compromising overall classification rates. State-of-the-art recognition results are further presented on the Brodatz dataset. High classification rates demonstrate the superiority of the proposed method. © Springer-Verlag Berlin Heidelberg 2007.
An improve to human computer interaction, recovering data from databases through spoken natural language
(Scopus, 2007) Florez Choque, Omar; Cuadros Vargas, Ernesto
The fastest and most straightforward way of communication for mankind is the voice. Therefore, the best way to interact with computers should be the voice too. That is why at the moment men are searching new ways to interact with computers. This interaction is improved if the words spoken by the speaker are organized in Natural Language. In this article, it is proposed a model to recover information from databases through queries in Spanish Natural Language using the voice as the way of communication. This model incorporates a Hybrid Intelligent System based on Genetic Algorithms and a Kohonen Self-Organizing Map (SOM) to recognize the present phonemes in a word through time. This approach allows us to remake up a word with speaker independence. Furthermore, it is proposed the use of a compiler with type 2 grammar according to the Chomsky Hierarchy to support the syntactic and semantic structure in Spanish language. Our experiments suggest that the Spoken Natural Language improves notably the Human-Computer interaction when compared with traditional input methods such as: mouse or keybord. © Springer-Verlag Berlin Heidelberg 2007.
Learning how to extract rotation-invariant and scale-invariant features from texture images
(Scopus, 2008) Montoya Zegarra, Javier; Paulo Papa, Joao; Leite, Neucimar; da Silva Torres, Ricardo; Falcao, Alexandre
Learning how to extract texture features from noncontrolled environments characterized by distorted images is a still-open task. By using a new rotation-invariant and scale-invariant image descriptor based on steerable pyramid decomposition, and a novel multiclass recognition method based on optimum-path forest, a new texture recognition system is proposed. By combining the discriminating power of our image descriptor and classifier, our system uses small-size feature vectors to characterize texture images without compromising overall classification rates. State-of-the-art recognition results are further presented on the Brodatz data set. High classification rates demonstrate the superiority of the proposed system.
Combining global with local texture information for image retrieval applications
(Scopus, 2008) Montoya Zegarra, Javier; Beeck, Jan; Jerônimo Leite, Neucimar; da Silva Torres, Ricardo; Falcao, Alexandre
This paper proposes a new texture descriptor to guide the search and retrieval in image databases. It extracts rich information from global and local primitives of textured images. At a higher level, the global macro-features in textured images are characterized by exploiting the multi-resolution properties of the Steerable Pyramid Decomposition. By doing this, the global texture configurations are highlighted. At afiner level, the local arrangements of texture micro-patterns are encoded by the Local Binary Pattern operator. Experiments were carried out on the standard Vistex dataset aiming to compare our desriptors against popular texture extraction methods with regard to their retrieval accuracies. The comparative evaluations allowed us to show the superior descriptive properties of our feature representation methods. © 2008 IEEE.
A translation from RSL to CSP
(Scopus, 2008) Parisaca Vargas, Abigail; Tapia Tarifa, Silvia Lizeth; George, Chris
The Raise Specification Language (RSL) is a broad spectrum modeling language which supports a wide range of specification styles. In order to apply verification techniques based on model checking to descriptions of concurrent systems in RSL, we translate RSL specifications into the input language CSPM of the FDR model checker. FDR is a well-established model checker for the process algebra CSP. However, we need to show that the analysis performed in FDR carry over to the original RSL specifications. For this purpose, we define a syntactic and semantic translation between RSL and CSPM, and show that this translation is in fact a strong bisimulation which preserves various properties such as traces and deadlock. Finally, we have built a tool which automates the translation of RSL specifications into CSPM following this approach. © 2008 IEEE.
Graph coloring for enforcing password identification against brute force attacks
(Scopus, 2008) Gutiérrez Cárdenas, Juan; Wilfredo, Bardales Roncalla; Orihuela Ordóñez, Lenin
Password Identification or Weak Authentication is one of the weakest points in accessing a system and a suitable point for recurrent attacks of crackers or sniffers. Breakdowns ranging from dictionary brute force attacks to password guesses have shown the increasing need for new types of identification forms based not only on characters' combination, but also taking into account the inherent advantages of the so-called Graphical Passwords. Using graph coloring for a password based system has always been an interesting proposal, but one of its main drawbacks was to teach the user some basic concepts of Graph Theory and also some Graph Coloring Algorithms. The following research tries to establish the usefulness of using password identification with graph coloring applied to graphical passwords, so that a common user could take advantage of this technique in a simplistic manner.
Wavelet-based fingerprint image retrieval
(Scopus, 2009) Montoya Zegarra, Javier; Leite, Neucimar; da Silva Torres, Ricardo
This paper presents a novel approach for personal identification based on a wavelet-based fingerprint retrieval system which encompasses three image retrieval tasks, namely, feature extraction, similarity measurement, and feature indexing. We propose the use of different types of Wavelets for representing and describing the textural information presented in fingerprint images in a compact way. For that purpose, the feature vectors used to characterize the fingerprints are obtained by computing the mean and the standard deviation of the decomposed images in the wavelet domain. These feature vectors are used both to retrieve the most similar fingerprints, given a query image, and their indexation is used to reduce the search spaces of candidate images. The different types of Wavelets used in our study include: Gabor wavelets, tree-structured wavelet decomposition using both orthogonal and bi-orthogonal filter banks, as well as the steerable wavelets. To evaluate the retrieval accuracy of the proposed approach, a total number of eight different data sets were considered. We also took into account different combinations of the above wavelets with six similarity measures. The results show that the Gabor wavelets combined with the Square Chord similarity measure achieves the best retrieval effectiveness. © 2008 Elsevier B.V. All rights reserved.
Model checking LTL formulae in RAISE with FDR
(Scopus, 2009) Parisaca Vargas, Abigail; Gabriela Garis, Ana; Tapia Tarifa, Silvia Lizeth; George, Chris
TheRaise SpecificationLanguage (RSL) is a modeling languagewhich supports various specification styles. To apply model checking to RSL concurrent descriptions, we translate RSL specifications into the input language CSPM of FDR. FDR is the model checker for the process algebra CSP. First, we define a syntactic and semantic translation from the concurrent applicative subset of RSL to CSPM, and show that this translation is a strong bisimulation which preserves properties such as traces and deadlock. Consequently, results obtained by refinement checks in FDR are sound for the original RSL descriptions. Second, RSL uses Linear Temporal Logic (LTL) to specify desired properties, but FDR does not support LTL. LTL formulas may be translated to CSP test processes in order to check them with FDR.We build a tool which automates the translation of RSL specifications into CSPMand translates LTL formulas to CSP processes, enabling the model checking of LTL formulas over RSL descriptions with FDR. © Springer-Verlag Berlin Heidelberg 2009.
Novel approaches for exclusive and continuous fingerprint classification
(Scopus, 2009) Montoya Zegarra, Javier; Paulo Papa, Joao; Leite, Neucimar; da Silva Torres, Ricardo; Falcao, Alexandre
This paper proposes novel exclusive and continuous approaches to guide the search and the retrieval in fingerprint image databases. Both approaches are useful to perform a coarse level classification of fingerprint images before fingerprint authentication tasks. Our approaches are characterized by: (1) texture image descriptors based on pairs of multi-resolution decomposition methods that encode effectively global and local fingerprint information, with similarity measures used for fingerprint matching purposes, and (2) a novel multi-class object recognition method based on the Optimum Path Forest classifier. Experiments were carried out on the standard NIST-4 dataset aiming to study the discriminative and scalability capabilities of our approaches. The high classification rates allow us demonstrate the feasibility and validity of our approaches for characterizing fingerprint images accurately. © 2009 Springer Berlin Heidelberg.
Controlling oil production in smart wells by MPC strategy with reinforcement learning
(Scopus, 2010) Talavera, Alvaro; Túpac Valdivia, Yván Jesús; Vellasco, Marley
This work presents the modeling and development of a methodology based on Model Predictive Control - MPC that uses a machine learning model, based on Reinforcement Learning, as the method for searching the optimal control policy, and a neural network as a proxy, for modeling the nonlinear plant. The neural network model was developed to predict the following variables: average pressure of the reservoir, the daily production of oil, gas, water and water cut in the production well, for three consecutive values, to perform the predictive control. This model is applied as a strategy to control the oil production in an oil reservoir with existing producer and injector wells. The experiments were carried out on a synthetic oil reservoir model that consists in a reservoir with three layers with different permeability and one producer well and one injector well, both completed in the three layers. There are three valves located into the injector well, one for each completion, which are the handling variables of the model. The oil production of the producer well is the controlled variable. The experiments performed have considered various set points and also the impact of disturbances on the production well. The obtained results indicate that the proposed model is capable of controlling oil production even with disturbances in the producing well, for different reference values for oil production and supporting some features of the petroleum reservoir systems such as: strong non-linearity, long delay in the system response, and multivariate characteristic. Copyright 2010, Society of Petroleum Engineers.
Unsupervised WSD by finding the predominant sense using context as a dynamic thesaurus
(Scopus, 2010) Tejada Cálrcamo, Javier; Calvo, Hiram; Gelbukh, Alex; Hara, Kazuo
We present and analyze an unsupervised method for Word Sense Disambiguation (WSD). Our work is based on the method presented by McCarthy et al. in 2004 for finding the predominant sense of each word in the entire corpus. Their maximization algorithm allows weighted terms (similar words) from a distributional thesaurus to accumulate a score for each ambiguous word sense, i.e., the sense with the highest score is chosen based on votes from a weighted list of terms related to the ambiguous word. This list is obtained using the distributional similarity method proposed by Lin Dekang to obtain a thesaurus. In the method of McCarthy et al., every occurrence of the ambiguous word uses the same thesaurus, regardless of the context where the ambiguous word occurs. Our method accounts for the context of a word when determining the sense of an ambiguous word by building the list of distributed similar words based on the syntactic context of the ambiguous word. We obtain a top precision of 77.54% of accuracy versus 67.10% of the original method tested on SemCor. We also analyze the effect of the number of weighted terms in the tasks of finding the Most Frecuent Sense (MFS) and WSD, and experiment with several corpora for building the Word Space Model. © 2010 Springer Science+Business Media, LLC & Science Press, China.
MFSRank: An unsupervised method to extract keyphrases using semantic information
(Scopus, 2011) Enrique López, Roque; Barreda, Dennis; Tejada, Javier; Cuadros Vargas, Ernesto
This paper presents an unsupervised graph-based method to extract keyphrases using semantic information. The proposed method has two stages. In the first one, we have extracted MFS (Maximal Frequent Sequences) and built the nodes of a graph with them. The weight of the connection between two nodes has been established according to common statistical information and semantic relatedness. In the second stage, we have ranked MFS with traditionally PageRank algorithm; but we have included ConceptNet. This external resource adds an extra weight value between two MFS. The experimental results are competitive with traditional approaches developed in this area. MFSRank overcomes the baseline for top 5 keyphrases in precision, recall and F-score measures. © 2011 Springer-Verlag.
A GH-SOM optimization with SOM labelling and dunn index
(Scopus, 2011) Bokan Garay, Alessandro; Ponce Contreras, Guillermo; Patiño Escarcina, Raquel
Clustering is an unsupervised classification method that divides a data set in groups, where the elements of a group have similar characteristics to each other. A well-known clustering method is the Growing Hierarchical Self-Organizing Map (GH-SOM), that improves the results of an ordinary SOM by controlling the number of neurons generated. In this paper it is proposed a optimization of the typical GH-SOM, using a cluster validation index to verify the quality of partitioning. © 2011 IEEE.

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