Artículos - Ciencia de la computación

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    Unsupervised detection of disturbances in 2D radiographs
    (IEEE Computer Society, 2021) Estacio, Laura; Mora, Rensso; Moritz, Ehlke; Lamecker, Hans; Tack, Alexander; Zachow, Stefan; Castro, Eveling
    "We present a method based on a generative model for detection of disturbances such as prosthesis, screws, zippers, and metals in 2D radiographs. The generative model is trained in an unsupervised fashion using clinical radiographs as well as simulated data, none of which contain disturbances. Our approach employs a latent space consistency loss which has the benefit of identifying similarities, and is enforced to reconstruct X-rays without disturbances. In order to detect images with disturbances, an anomaly score is computed also employing the Frechet distance between the input X-ray and the reconstructed one using our generative model. Validation was performed using clinical pelvis radiographs. We achieved an AUC of 0.77 and 0.83 with clinical and synthetic data, respectively. The results demonstrated a good accuracy of our method for detecting outliers as well as the advantage of utilizing synthetic data. © 2021 IEEE"
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    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."
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    Political discourses, ideologies, and online coalitions in the Brazilian Congress on Twitter during 2019
    (SAGE Publications Ltd, 2021) García-Sánchez, E.; Benetti, P.R.; Higa, G.L.; Alvarez, M.C.; Gomez-Nieto, E.
    "The aim of this research is to describe the pattern of interactions of Brazilian legislators on Twitter during 2019 in the construction of political discourses. Based on 20,076 replies during 2019, posted on Twitter by 514 Brazilian legislators, we conducted descriptive analysis of legislators’ Twitter profiles, social network analyses from their interactions, and content analysis of the messages. We found that (1) there are large disparities between legislators in the use of Twitter; (2) the pattern of interactions depicted five clusters defined by political affinities; (3) each cluster had different features regarding their composition and impact; (4) the centrality of the legislators within the network was positively associated with public endorsement on Twitter; and (5) the topics of messages within the clusters reinforce discourses aligned to political ideologies. We argue that the pattern of interactions on Twitter allows to identify online coalitions that reinforce particular discourses within the Brazilian parliamen"
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    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
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    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"
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    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.
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    Urban Perception: Can We Understand Why a Street Is Safe?
    (Springer Science and Business Media Deutschland GmbH, 2021) Moreno-Vera, Felipe; Lavi, Bahram; Poco, Jorge
    The importance of urban perception computing is relatively growing in machine learning, particularly in related areas to Urban Planning and Urban Computing. This field of study focuses on developing systems to analyze and map discriminant characteristics that might direcly impact the city’s perception. In other words, it seeks to identify and extract discriminant components to define the behavior of a city’s perception. This work will perform a street-level analysis to understand safety perception based on the “visual components”. As our result, we present our experimental evaluation regarding the influence and impact of those visual components on the safety criteria and further discuss how to properly choose confidence on safe or unsafe measures concerning the perceptional scores on the city street levels analysis. © 2021, Springer Nature Switzerland AG
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    Understanding safety based on urban perception
    (Springer Science and Business Media Deutschland GmbH, 2021) Moreno-Vera, F.
    "Currently, one important field on machine learning is Urban Perception Computing is to model the way in which humans can interact and understand the environment that surrounds them. This process is performed using convolutional models to learn and identify some insights which define the concept of perception of a place (e.g. a street image). One approach of this field is urban perception of street images, we will focus on this approach to study the safety perception of a city and try to explain why and how the perception can be predicted by a mathematical model. As result, we present an analysis about the influence and impact of the visual components on the safety criteria and also an explanation about why a certain decision on the perception of the safety of the streets, such as safe or unsafe. © 2021, Springer Nature Switzerland AG"
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    Ontoslam: An ontology for representing location and simultaneous mapping information for autonomous robots
    (MDPI, 2021) Cornejo-Lupa, M.A.; Cardinale, Y.; Ticona-Herrera, R.; Barrios Aranibar, D.; Andrade, M.; Diaz-Amado, J.
    "Autonomous robots are playing an important role to solve the Simultaneous Localization and Mapping (SLAM) problem in different domains. To generate flexible, intelligent, and interopera-ble solutions for SLAM, it is a must to model the complex knowledge managed in these scenarios (i.e., robots characteristics and capabilities, maps information, locations of robots and landmarks, etc.) with a standard and formal representation. Some studies have proposed ontologies as the standard representation of such knowledge; however, most of them only cover partial aspects of the information managed by SLAM solutions. In this context, the main contribution of this work is a complete ontology, called OntoSLAM, to model all aspects related to autonomous robots and the SLAM problem, towards the standardization needed in robotics, which is not reached until now with the existing SLAM ontologies. A comparative evaluation of OntoSLAM with state-of-the-art SLAM ontologies is performed, to show how OntoSLAM covers the gaps of the existing SLAM knowledge representation models. Results show the superiority of OntoSLAM at the Domain Knowledge level and similarities with other ontologies at Lexical and Structural levels. Additionally, OntoSLAM is integrated into the Robot Operating System (ROS) and Gazebo simulator to test it with Pepper robots and demonstrate its suitability, applicability, and flexibility. Experiments show how OntoSLAM provides semantic benefits to autonomous robots, such as the capability of inferring data from organized knowledge representation, without compromising the information for the application and becoming closer to the standardization needed in robotics. © 2021 by the authors. Licensee MDPI, Basel, Switzerland"
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    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."
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    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.
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    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.
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    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.
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    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.
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    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.
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    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.
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    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.
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    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.
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    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.
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    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.