Pregrado - 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 categorization of simultaneous localization and mapping knowledge for mobile robots
(Universidad Católica San Pablo, 2020) Cornejo Lupa, Maria Alejandra; Ticona Herrera, Regina Paola
Autonomous robots are playing important roles in academic, technologi-cal, and scientific activities. Thus, their behavior is getting more complex. The main tasks of autonomous robots include mapping an environment and localize themselves. These tasks comprise the Simultaneous Localization and Mapping (SLAM) problem. Representation of the SLAM knowledge (e.g., robot charac-teristics, environment information, mapping and location information), with a standard and well-defined model, provides the base to develop efficient and interoperable solutions. However, as far as we know, there is not a common classification of such knowledge. Many existing works based on Semantic Web, have formulated ontologies to model information related to only some SLAM aspects, without a standard arrangement. In this work, we propose a category-zation of the knowledge managed in SLAM, based on existing ontologies and SLAM principles. We also classify recent and popular ontologies according to our proposed categories and highlight the lessons to learn from existing solu- tions. Showing the neccesity to develop a complete SLAM ontology in mobile robots.
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 deep learning approach for sentiment analysis in Spanish Tweets
(Springer Verlag, 2018) Vizcarra Aguilar, Gerson; Mauricio, Antoni; Mauricio, Leonidas
Sentiment Analysis at Document Level is a well-known problem in Natural Language Processing (NLP), being considered as a reference in NLP, over which new architectures and models are tested in order to compare metrics that are also referents in other issues. This problem has been solved in good enough terms for English language, but its metrics are still quite low in other languages. In addition, architectures which are successful in a language do not necessarily works in another. In the case of Spanish, data quantity and quality become a problem during data preparation and architecture design, due to the few labeled data available including not-textual elements (like emoticons or expressions). This work presents an approach to solve the sentiment analysis problem in Spanish tweets and compares it with the state of art. To do so, a preprocessing algorithm is performed based on interpretation of colloquial expressions and emoticons, and trivial words elimination. Processed sentences turn into matrices using the 3 most successful methods of word embeddings (GloVe, FastText and Word2Vec), then the 3 matrices merge into a 3-channels matrix which is used to feed our CNN-based model. The proposed architecture uses parallel convolution layers as k-grams, by this way the value of each word and their contexts are weighted, to predict the sentiment polarity among 4 possible classes. After several tests, the optimal tuple which improves the accuracy were <1, 2>. Finally, our model presents %61.58 and %71.14 of accuracy in InterTASS and General Corpus respectively. © Springer Nature Switzerland AG 2018.
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 graph-based approach for transcribing ancient documents
(Springer Verlag, 2012) Meza Lovón, Graciela Lecireth
Over the last years, the interest in preserving digitally ancient documents has increased resulting in databases with a huge amount of image data. Most of these documents are not transcribed and thus querying operations are limited to basic searching. We propose a novel approach for transcribing historical documents and present results of our initial experiments. Our method divides a text-line image into frames and constructs a graph using the framed image. Then Dijkstra algorithm is applied to find the line transcription. Experiments show a character accuracy of 79.3%. © Springer-Verlag Berlin Heidelberg 2012.
A multi-modal emotion recogniser based on the integration of multiple fusion methods
(Universidad Católica San Pablo, 2021) Heredia Parillo, Juanpablo Andrew; Ticona Herrera, Regina Paola
People naturally express emotions in simultaneous different ways. Thus, multimodal methods are becoming popular for emotion recognition and analysis of reactions to many aspects of daily life. This research work presents a multimodal method for emotion recognition from images. The multi-modal method analyses facial expressions, body gestures and the characteristics of the body and the environment to determine an emotional state, processing each modality with a specialised deep learning model and then applying the proposed fusion method. The fusion method, called EmbraceNet+, consists of a branched architecture that integrates the EmbraceNet fusion method with other fusion methods. The tests carried out on an adaptation of the EMOTIC dataset show that the proposed multi-modal method is effective and improves the results obtained by individual processings, as well as competing with other state-ofthe-art methods. The proposed method has many areas of application because it seeks to recognise emotions in any situation. Likewise, the proposed fusion method can be used in any multi-modal deep learning-based model.
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 new boosting design of Support Vector Machine classifiers
(Elsevier, 2015) Mayhua López, Efraín; Gómez Verdejo, Vanessa; Figueiras Vidal, Aníbal
Boosting algorithms pay attention to the particular structure of the training data when learning, by means of iteratively emphasizing the importance of the training samples according to their difficulty for being correctly classified. If common kernel Support Vector Machines (SVMs) are used as basic learners to construct a Real AdaBoost ensemble, the resulting ensemble can be easily compacted into a monolithic architecture by simply combining the weights that correspond to the same kernels when they appear in different learners, avoiding to increase the operation computational effort for the above potential advantage. This way, the performance advantage that boosting provides can be obtained for monolithic SVMs, i.e., without paying in classification computational effort because many learners are needed. However, SVMs are both stable and strong, and their use for boosting requires to unstabilize and to weaken them. Yet previous attempts in this direction show a moderate success. In this paper, we propose a combination of a new and appropriately designed subsampling process and an SVM algorithm which permits sparsity control to solve the difficulties in boosting SVMs for obtaining improved performance designs. Experimental results support the effectiveness of the approach, not only in performance, but also in compactness of the resulting classifiers, as well as that combining both design ideas is needed to arrive to these advantageous designs. © 2014 Elsevier B.V.All rights reserved.
A New Improvement of Human Bodies Detection
(Institute of Electrical and Electronics Engineers Inc., 2016) Cervantes Jilaja, Claudia; Tejada Begazo, Maria; Patiño Escarcina, Raquel Esperanza; Barrios Aranibar, Dennis
The HOG method is applied in the detection of human bodies in an vertical position. However, when human bodies are in other positions, HOG method has several fails, another disadvantage of this method is the occlusion of human bodies by objects. This research presents a new improvement to HOG method with erosion morphological operator and cascade classifier (vector points describing the face of a person). The experiments show that the improved HOG method add erosion operator and classifier of faces (MFHD, Morphological Face HOG Detection) in 79.02% with respect the HOG method whose percentage is 64.46%. © 2015 IEEE.
A New Method for Static Video Summarization Using Local Descriptors and Video Temporal Segmentation
(Scopus, 2013) Cayllahua Cahuina, Edward; Camara Chavez, Guillermo
The continuous creation of digital video has caused an exponential growth of digital video content. To increase the usability of such large volume of videos, a lot of research has been made. Video summarization has been proposed to rapidly browse large video collections. To summarize any type of video, researchers have relied on visual features contained in frames. In order to extract these features, different techniques have used local or global descriptors. In this paper, we propose a method for static video summarization that can produce meaningful and informative video summaries. We perform an evaluation using over 100 videos in order to achieve a stronger position about the performance of local descriptors in semantic video summarization. Our experimental results show, with a confidence level of 99%, that our proposed method using local descriptors and temporal video segmentation produces better summaries than state of the art methods. We also demonstrate the importance of a more elaborate method for temporal video segmentation, improving the generation of summaries, achieving 10% improvement in accuracy. We also acknowledge a marginal importance of color information when using local descriptors to produce video summaries. © 2013 IEEE.
A new parallel training algorithm for optimum-path forest-based learning
(Springer Verlag, 2017) Culquicondor, Aldo; Castelo Fernández, Cesar; Paulo Papa, Joao
In this work, we present a new parallel-driven approach to speed up Optimum-Path Forest (OPF) training phase. In addition, we show how to make OPF up to five times faster for training using a simple parallel-friendly data structure, which can achieve the same accuracy results to the ones obtained by traditional OPF. To the best of our knowledge, we have not observed any work that attempted at parallelizing OPF to date, which turns out to be the main contribution of this paper. The experiments are carried out in four public datasets, showing the proposed approach maintains the trade-off between efficiency and effectiveness. © Springer International Publishing AG 2017.
A Semi-Automated Approach for Recognizing Moving Targets Using a Global Vision System
(Institute of Electrical and Electronics Engineers Inc., 2016) Ripas Mamani, Roger; Cervantes Jilaja, Claudia; Rosas Cuevas, Yessica; Patiño Escarcina, Raquel Esperanza; Barrios Aranibar, Dennis
Global vision system works with processes of sorting, recognition and identification through some external characteristics as: color, shape and size depending of specific targets. In this paper we propose a semi-automated approach to recognize the targets in moving, where first is performed the image calibration with respect to the lighting and then proceeds to recognize a variety of colors and sizes, through several channels of different color spaces in the processing of video sequences to recognize moving targets, using the proposed algorithm called Color Segmentation (Algorithm 1) to identify a variety of light and dark colors. After semi-automated process is performed the sorting or recognizing of the moving target, where is obtained the position (x, y) of central point and the size of the area (pixels) of the segmentation region. Tests were conducted in: the location of robots in a soccer robot environment (with 94.36% of accuracy) and chestnuts selection process (with 91.80% of accuracy), if the image needs to recognize more than five detections then it proceeds to add parallelism, i.e. add a thread for each segmented color, thus improving processing time. © 2016 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.
Abnormal event detection in video using motion and appearance information
(Springer Verlag, 2018) Menejes Palomino, Neptalí; Cámara Chávez, Guillermo
This paper presents an approach for the detection and localization of abnormal events in pedestrian areas. The goal is to design a model to detect abnormal events in video sequences using motion and appearance information. Motion information is represented through the use of the velocity and acceleration of optical flow and the appearance information is represented by texture and optical flow gradient. Unlike literature methods, our proposed approach provides a general solution to detect both global and local abnormal events. Furthermore, in the detection stage, we propose a classification by local regions. Experimental results on UMN and UCSD datasets confirm that the detection accuracy of our method is comparable to state-of-the-art methods. © Springer International Publishing AG, part of Springer Nature 2018.
Acelerando el tiempo de busqueda en consultas de tipo Timebox en series de tiempo usando el Segment Buddy Tree con Range Maximun-Minimum Query
(Universidad Católica San Pablo, 2024) Velasquez Rios, Diego Arturo; Gomez Nieto, Erick Mauricio
Las series de tiempo son esenciales en una variedad de campos, incluidas las finanzas, la meteorología, la salud y la informática urbana, entre otros. Estos datos se generan y almacenan a un ritmo cada vez mayor. Los analistas generalmente necesitan explorar, comparar y relacionar los datos de múltiples series de tiempo cuyos números pueden oscilar desde las decenas hasta los millares; por ejemplo, múltiples acciones de la bolsa de valores, consumo de energía de las máquinas, etc. llegando a ser millones de datos a explorar. Actualmente, existe una gran demanda para la exploración de datos de series de tiempo a gran escala. Sin embargo, las consultas usadas han demostrado tener un tiempo linealmente proporcional al tamaño de las series de tiempo, lo que las hace poco prácticas. Una de estas consultas difíciles es la consulta de tipo Timebox. En esta investigación, proponemos acelerar la consulta de tipo Timebox haciendo uso de la estructura de datos Buddy Tree y filtrando los resultados con consultas de Range Minimum/Maximum Query (RMQ). Este tipo de búsqueda ha sido recientemente abordado; a diferencia de los resultados obtenidos por el KD-Box, nuestros resultados son obtenidos a partir del conjunto total de datos y no con aproximaciones de las series de tiempo, esto elimina la posibilidad de obtener series que no pertenecen al Timebox y series de tiempo que perteneciendo no son encontradas por el método de búsqueda. Para comprobar nuestras hipótesis realizamos un conjunto de experimentos que evidencian la eficiencia de nuestra propuesta
ACM/IEEE-CS computer science curricula 2013: Implementing the final report
(Association for Computing Machinery, 2014) Sahami, Mehran; Roach, Steve; Cuadros Vargas, Ernesto; Hawthorne, Elizabeth; Kumar, Amruth; LeBlanc, Richard; Reed, David; Seker, Remzi
For over 40 years, the ACM and IEEE-Computer Society have sponsored international curricular guidelines for undergraduate programs in computing. The rapid evolution and expansion of the computing field and the growing number of topics in computer science have made regular revision of curricular recommendations necessary. Thus, the Computing Curricula volumes are updated on an approximately 10-year cycle, with the aim of keeping curricula modern and relevant. The latest volume in the series, Computer Science Curricula 2013 (CS2013), is due for release in the Fall of 2013. This panel seeks to inform the SIGCSE community about the final version of the report, provide insight on interpreting the CS2013 guidelines, and give guidance regarding how the guidelines may be implemented at different institutions.
Actualización dinámica del modelo Bag of Visual Words para la generación de visualizaciones de colecciones dinámicas de imágenes
(Universidad Católica San Pablo, 2015) Fajardo Portugal, Anthony Armando; San Román Salazar, Frizzi Alejandra
Hoy en día, muchos conjuntos de datos disponibles son dina´micos, es decir, que evolucionan a medida que nuevos elementos son gradualmente agregados o removidos. En este ámbito, es importante contar con representaciones visuales que acepten conjuntos de datos dina´micos. Este trabajo de investigación busca principalmente adaptar el método Bag of Visual Words (BOVW) estático, para que acepte un comportamiento dina´mico, cuando los conjuntos de entrada sean incrementales. Esta actualización no debería alterar la efectividad en cuanto a recuperación y agrupamiento de imágenes se refiere. Se consideró de forma general, el Vector Space Model (VSM) ya que presentó mejores resultados en cuanto a la discriminación de clases. Además, el VSM convencional no es incremental, es decir, nuevas adiciones a la colección fuerzan el re-cálculo del espacio de datos y de las disimilitudes anteriormente computadas. En este sentido, se considera un nuevo modelo incremental basado en el VSM (Incremental Vector Space Model (iVSM)) en nuestros estudios comparativos. El iVSM presento´ los mejores resultados cuantitativos y cualitativos en diversas configuraciones probadas. Los resultados de la evaluación se presentan y se ofrecen recomendaciones sobre la aplicación de diferentes medidas de similitud de imágenes en tareas de análisis visual.
AL-DDoS attack detection optimized with genetic algorithms
(Springer Verlag, 2018) Quequezana Buendia, Jan Camilo; Santisteban Pablo, Julio Omar
Application Layer DDoS (AL-DDoS) is a major danger for Internet information services, because these attacks are easily performed and implemented by attackers and are difficult to detect and stop using traditional firewalls. Managing to saturate physically and computationally the information services offered on the network. Directly harming legitimate users, to deal with this type of attacks in the network layer previous approaches propose to use a configurable statistical model and observed that when being optimized in various configuration parameters Using Genetic Algorithms was able to optimize the effectiveness to detect Network Layer DDoS (NL-DDoS), however this method is not enough to stop DDoS at the level of application because this level presents different characteristics, that is why we propose a new method Configurable and optimized for different scenarios of Attacks that effectively detect AL-DDoS. © Springer Nature Switzerland AG 2018.

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