Departamento de Ciencias de la Computación

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2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI)
(Institute of Electrical and Electronics Engineers Inc., 2018) Túpac Valdivia, Yván Jesús
En las ediciones anteriores de LA-CCI (es decir, 2016 - Colombia, 2015 - Brasil, y en 2014 - Argentina), contó con el apoyo de las respectivas sociedades nacionales y varias sociedades hermanas, especialmente las prestigiosas IEEE e IEEE-CIS. Sin embargo, como resultado natural del crecimiento del evento, el equipo organizador y el comité de dirección están muy felices de anunciar que en 2017, por segunda vez, IEEE será un patrocinador completo de nuestra Conferencia.
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.
A Comparison of Machine Learning Classifiers for Water-Body Segmentation Task in the PeruSAT-1 Imagery
(Springer Science and Business Media Deutschland GmbH, 2021) Huauya, R.; Moreno, F.; Peña, J.; Dianderas, E.; Mauricio, A.; Díaz, J,
"Water-body segmentation is a high-relevance task inside satellite image analysis due to its relationship with environmental monitoring and assessment. Thereon, several authors have proposed different approaches which achieve a wide range of results depending on their datasets and settings. This study is a brief review of classical segmentation techniques in multispectral images using the Peruvian satellite PeruSAT-1 imagery. The areas of interest are medium-sized highland zones with water bodies around in Peruvian south. We aim to analyze classical segmentation methods to prevent future natural disasters, like alluviums or droughts, under low-cost data constraints. We consider accuracy, robustness, conditions, and visual effects in our analysis"
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.
A multi-modal visual emotion recognition method to instantiate an ontology
(SciTePress, 2021) A. Heredia, Juan Pablo; Cardinale, Yudith; Dongo, Irvin; Díaz-Amado, Jose
"Human emotion recognition from visual expressions is an important research area in computer vision and machine learning owing to its significant scientific and commercial potential. Since visual expressions can be captured from different modalities (e.g., face expressions, body posture, hands pose), multi-modal methods are becoming popular for analyzing human reactions. In contexts in which human emotion detection is performed to associate emotions to certain events or objects to support decision making or for further analysis, it is useful to keep this information in semantic repositories, which offers a wide range of possibilities for implementing smart applications. We propose a multi-modal method for human emotion recognition and an ontology-based approach to store the classification results in EMONTO, an extensible ontology to model emotions. The multi-modal method analyzes facial expressions, body gestures, and features from the body and the environment to determine an emotional state; this processes each modality with a specialized deep learning model and applying a fusion method. Our fusion method, called EmbraceNet+, consists of a branched architecture that integrates the EmbraceNet fusion method with other ones. We experimentally evaluate our multi-modal method on an adaptationof the EMOTIC dataset. Results show that our method outperforms the single-modal methods."
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.
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.
AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design
(Institute of Electrical and Electronics Engineers Inc., 2021) Li, Jianning; Pimentel, Pedro; Szengel, Angelika; Ehlke, Moritz; Lamecker, Hans; Zachow, Stefan; Estacio, Laura; Doenitz, Christian; Ramm, Heiko; Shi, Haochen; Chen, Xiaojun; Matzkin, Franco; Newcombe, Virginia; Ferrante, Enzo; Jin, Yuan; Ellis, David G.; Aizenberg, Michele R.; Kodym, Oldrich; Spanel, Michal; Herout, Adam; Mainprize, James G; Fishman, Zachary; Hardisty, Michael R.; Bayat, Amirhossein; Shit, Suprosanna; Wang, Bomin; Liu, Zhi; Eder, Matthias; Pepe, Antonio; Gsaxner, Christina; Alves, Victor; Zefferer, Ulrike; von Campe, Gord; Pistracher, Karin; Schafer, Ute; Schmalstieg, Dieter; Menze, Bjoern H.; Glocker, Ben; Egger, Jan
The aim of this paper is to provide a comprehensive overview of the MICCAI 2020 AutoImplant Challenge. The approaches and publications submitted and accepted within the challenge will be summarized and reported, highlighting common algorithmic trends and algorithmic diversity. Furthermore, the evaluation results will be presented, compared and discussed in regard to the challenge aim: seeking for low cost, fast and fully automated solutions for cranial implant design. Based on feedback from collaborating neurosurgeons, this paper concludes by stating open issues and post-challenge requirements for intra-operative use. The codes can be found at https://github.com/Jianningli/tmi. © 1982-2012 IEEE
Combinatorial Laplacian image cloning
(Scopus, 2011) Cuadros Vargas, Alex Jesús; Nonato, Luis; Pascucci, Valerio
Seamless image cloning has become one of the most important editing operation for photomontage. Recent coordinate-based methods have lessened considerably the computational cost of image cloning, thus enabling interactive applications. However, those techniques still bear severe limitations as to concavities and dynamic shape deformation. In this paper we present novel methodology for image cloning that turns out to be highly efficient in terms of computational times while still being more flexible than existing techniques. Our approach builds on combinatorial Laplacian and fast Cholesky factorization to ensure interactive image manipulation, handling holes, concavities, and dynamic deformations during the cloning process. The provided experimental results show that the proposed technique outperforms existing methods in requisites such as accuracy and flexibility. © 2011 IEEE.
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.
Computer science curriculum 2013: Reviewing the strawman report from the ACM/IEEE-CS task force
(Scopus, 2012) Sahami, Mehran; Roach, Steve; Cuadros Vargas, Ernesto; Reed, David
Beginning over 40 years ago with the publication of Curriculum 68, the major professional societies in computing - ACM and IEEE-Computer Society - have sponsored various efforts to establish international curricular guidelines for undergraduate programs in computing. As the field has grown and diversified, so too have the recommendations for curricula. There are now guidelines for Computer Engineering, Information Systems, Information Technology, and Software Engineering in addition to Computer Science. These volumes are updated regularly with the aim of keeping computing curricula modern and relevant. In the Fall of 2010, work on the next volume in the series, Computer Science 2013 (CS2013), began. Considerable work on the new volume has already been completed and a first draft of the CS2013 report (known as the Strawman report) will be complete by the beginning of 2012. This panel seeks to update and engage the SIGCSE community in providing feedback on the Strawman report, which will be available shortly prior to the SIGCSE conference. © 2012 Authors.
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.
CrimAnalyzer: Understanding Crime Patterns in Sao Paulo
(IEEE COMPUTER SOC10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314, 2021) Garcia, Germain; Silveira, Jaqueline; Poco, Jorge; Paiva, Afonso; Nery, Marcelo Batista; Silva, Claudio T.; Adorno, Sergio; Nonato, Luis Gustavo
Sao Paulo is the largest city in South America, with crime rates that reflect its size. The number and type of crimes vary considerably around the city, assuming different patterns depending on urban and social characteristics of each particular location. Previous works have mostly focused on the analysis of crimes with the intent of uncovering patterns associated to social factors, seasonality, and urban routine activities. Therefore, those studies and tools are more global in the sense that they are not designed to investigate specific regions of the city such as particular neighborhoods, avenues, or public areas. Tools able to explore specific locations of the city are essential for domain experts to accomplish their analysis in a bottom-up fashion, revealing how urban features related to mobility, passersby behavior, and presence of public infrastructures (e.g., terminals of public transportation and schools) can influence the quantity and type of crimes. In this paper, we present CrimAnalyzer, a visual analytic tool that allows users to study the behavior of crimes in specific regions of a city. The system allows users to identify local hotspots and the pattern of crimes associated to them, while still showing how hotspots and corresponding crime patterns change over time. CrimAnalyzer has been developed from the needs of a team of experts in criminology and deals with three major challenges: i) flexibility to explore local regions and understand their crime patterns, ii) identification of spatial crime hotspots that might not be the most prevalent ones in terms of the number of crimes but that are important enough to be investigated, and iii) understand the dynamic of crime patterns over time. The effectiveness and usefulness of the proposed system are demonstrated by qualitative and quantitative comparisons as well as by case studies run by domain experts involving real data. The experiments show the capability of CrimAnalyzer in identifying crime-related phenomena.
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.
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.
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.
ICE: A visual analytic tool for interactive clustering ensembles generation
(Association for Computing Machinery, 2021) Castro-Ochante, J.; Camara-Chavez, G.; Gomez-Nieto, E.
"Clustering methods are the most used algorithms for unsupervised learning. However, there is no unique optimal approach for all datasets since different clustering algorithms produce different partitions. To overcome this issue of selecting an appropriate technique and its corresponding parameters, cluster ensemble strategies are used for improving accuracy and robustness by a weighted combination of two or more approaches. However, this process is often carried out almost in a blind manner, testing different combinations of methods and assessing if its performance is beneficial for the defined purpose. Thus, the procedure for selecting the best combination tests many clustering ensembles until the desired result is achieved. This paper proposes a novel analytic tool for clustering ensemble generation, based on quantitative metrics and interactive visual resources. Our approach allows the analysts to display different results from state-of-the-art clustering methods and analyze their performance based on specific metrics and visual inspection. Based on their requirements/experience, the analysts can interactively assign weights to the different methods to set their contributions and manage (create, store, compare, and merge), such as for ensembles. Our approach's effectiveness is shown through a set of experiments and case studies, attesting to its usefulness in practical applications. © 2021 ACM."
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.
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.
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.

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