Estrategia basada en proyectos en el aprendizaje práctico

The formative process in educational institutions has been the subject of debate in relation to teachers, who have been criticized for the lack of development of their didactic skills to achieve the expected learning. The lack of application of project-based strategy leads to the maintenance of traditional schemes.. In this sense the teachers of basic education do not consider the student as the center of the teaching process, therefore the methodologies developed have placed the teacher as protagonist of the process before the student, who became a recipient of Knowledge before a builder of the same, in which the exhibition class has been the central axis for learning, has not diversified the use of strategies. This research aims to establish the impact of the project-based strategy on the practical learning of the children of the third degree of basic education of the educational unit Dr. Miguel Ángel Zambrano. The methodology of applied research is based on the qualitative approach of research, deductive-inductive methods, bibliographic and field research were used to describe the project-based strategy in learning Practical. The techniques used were the interview, survey and observation sheet, which allowed to collect information from the authority, teachers and students about the problems studied. From the data collected it is evident that the teacher is the center of the process, leaving aside the participation of the students in the formative process.El proceso formativo en las instituciones educativas ha sido tema de debate con relación a los docentes, quienes han sido criticados por la falta de desarrollo de sus competencias didácticas para lograr los aprendizajes esperados. La falta de aplicación de estrategia basada en proyectos conlleva a mantener esquemas tradicionales. En este sentido los docentes de Educación Básica no consideran al estudiante como el centro del proceso de enseñanza, por tanto las metodologías desarrolladas han colocado al docente como protagonista del proceso antes que al estudiante, quien pasó a ser un receptor de conocimientos antes que un constructor de los mismos, en el cual la clase expositiva ha sido el eje central para el aprendizaje, no ha diversificado el uso de estrategias. La presente investigación pretende establecer la incidencia de la estrategia basada en proyectos en el aprendizaje práctico de los niños del tercer grado de educación básica de la Unidad Educativa Dr. Miguel Ángel Zambrano. La metodología de la investigación aplicada se fundamenta en el enfoque cualitativo de la investigación, se utilizó los métodos deductivo - inductivo, la investigación bibliográfica y de campo para describir acerca de la estrategia basada en proyectos en el aprendizaje practico. Las técnicas utilizadas fueron la entrevista, la encuesta y ficha de observación, que permitieron recolectar información de la autoridad, docentes y estudiantes acerca de la problemática estudiada. De los datos recolectados se evidencia que el docente es el centro del proceso, dejando de lado la participación de los estudiantes en el proceso formativo

Secure Computer Network: Strategies and Challengers in Big Data Era

As computer networks have transformed in essential tools, their security has become a crucial problem for computer systems. Detecting unusual values from large volumes of information produced by network traffic has acquired huge interest in the network security area. Anomaly detection is a starting point to prevent attacks, therefore it is important for all computer systems in a network have a system of detecting anomalous events in a time near their occurrence. Detecting these events can lead network administrators to identify system failures, take preventive actions and avoid a massive damage. This work presents, first, how identify network traffic anomalies through applying parallel computing techniques and Graphical Processing Units in two algorithms, one of them a supervised classification algorithm and the other based in traffic image processing. Finally, it is proposed as a challenge to resolve the anomalies detection using an unsupervised algorithm as Deep Learning.Facultad de Informátic

Secure Computer Network: Strategies and Challengers in Big Data Era

As computer networks have transformed in essential tools, their security has become a crucial problem for computer systems. Detecting unusual values from large volumes of information produced by network traffic has acquired huge interest in the network security area. Anomaly detection is a starting point to prevent attacks, therefore it is important for all computer systems in a network have a system of detecting anomalous events in a time near their occurrence. Detecting these events can lead network administrators to identify system failures, take preventive actions and avoid a massive damage. This work presents, first, how identify network traffic anomalies through applying parallel computing techniques and Graphical Processing Units in two algorithms, one of them a supervised classification algorithm and the other based in traffic image processing. Finally, it is proposed as a challenge to resolve the anomalies detection using an unsupervised algorithm as Deep Learning.Facultad de Informátic

Seguridad en redes de computadoras: estrategias y desafíos en la era de big data

As computer networks have transformed in essential tools, their security has become a crucial problem for computer systems. Detecting unusual values from large volumes of information produced by network traffic has acquired huge interest in the network security area. Anomaly detection is a starting point to prevent attacks, therefore it is important for all computer systems in a network have a system of detecting anomalous events in a time near their occurrence. Detecting these events can lead network administrators to identify system failures, take preventive actions and avoid a massive damage. This work presents, first, how identify network traffic anomalies through applying parallel computing techniques and Graphical Processing Units in two algorithms, one of them a supervised classification algorithm and the other based in network traffic image processing. Finally, it is proposed as a challenge to resolve the anomalies detection using an unsupervised algorithm as Deep Learning.Dado que las redes de computadoras se han transformado en una herramienta esencial, su seguridad se ha convertido en un problema crucial para los sistemas de computación. Detectar valores inusuales en grandes volúmenes de información producidos por el tráfico de red ha adquirido un enorme interés en el área de seguridad de redes. La detección de anomalías es el punto de partida para prevenir ataques, por lo tanto es importante para todos los sistemas de computación pertenecientes a una red tener un sistema de detección de eventos anómalos en un tiempo cercano a su ocurrencia. Detectar estos eventos permitiría a los administradores de red identificar fallas en el sistema, tomar acciones preventivas y evitar daños masivos. Este trabajo presenta, primero, cómo identificar anomalías de tráfico en la red aplicando técnicas de computación paralela y Unidades de Procesamiento Gráfico en dos algoritmos, un algoritmo de clasificación supervisada y otro basado en procesamiento de imágenes de tráfico de red. Finalmente, se propone como desafío resolver la detección de anomalías usando un algoritmo no supervisado como Aprendizaje Profundo.Facultad de Informátic

Seguridad en redes de computadoras: estrategias y desafíos en la era de big data

As computer networks have transformed in essential tools, their security has become a crucial problem for computer systems. Detecting unusual values from large volumes of information produced by network traffic has acquired huge interest in the network security area. Anomaly detection is a starting point to prevent attacks, therefore it is important for all computer systems in a network have a system of detecting anomalous events in a time near their occurrence. Detecting these events can lead network administrators to identify system failures, take preventive actions and avoid a massive damage. This work presents, first, how identify network traffic anomalies through applying parallel computing techniques and Graphical Processing Units in two algorithms, one of them a supervised classification algorithm and the other based in network traffic image processing. Finally, it is proposed as a challenge to resolve the anomalies detection using an unsupervised algorithm as Deep Learning.Dado que las redes de computadoras se han transformado en una herramienta esencial, su seguridad se ha convertido en un problema crucial para los sistemas de computación. Detectar valores inusuales en grandes volúmenes de información producidos por el tráfico de red ha adquirido un enorme interés en el área de seguridad de redes. La detección de anomalías es el punto de partida para prevenir ataques, por lo tanto es importante para todos los sistemas de computación pertenecientes a una red tener un sistema de detección de eventos anómalos en un tiempo cercano a su ocurrencia. Detectar estos eventos permitiría a los administradores de red identificar fallas en el sistema, tomar acciones preventivas y evitar daños masivos. Este trabajo presenta, primero, cómo identificar anomalías de tráfico en la red aplicando técnicas de computación paralela y Unidades de Procesamiento Gráfico en dos algoritmos, un algoritmo de clasificación supervisada y otro basado en procesamiento de imágenes de tráfico de red. Finalmente, se propone como desafío resolver la detección de anomalías usando un algoritmo no supervisado como Aprendizaje Profundo.Facultad de Informátic

Seguridad en redes de computadoras: estrategias y desafíos en la era de big data

As computer networks have transformed in essential tools, their security has become a crucial problem for computer systems. Detecting unusual values from large volumes of information produced by network traffic has acquired huge interest in the network security area. Anomaly detection is a starting point to prevent attacks, therefore it is important for all computer systems in a network have a system of detecting anomalous events in a time near their occurrence. Detecting these events can lead network administrators to identify system failures, take preventive actions and avoid a massive damage. This work presents, first, how identify network traffic anomalies through applying parallel computing techniques and Graphical Processing Units in two algorithms, one of them a supervised classification algorithm and the other based in network traffic image processing. Finally, it is proposed as a challenge to resolve the anomalies detection using an unsupervised algorithm as Deep Learning.Dado que las redes de computadoras se han transformado en una herramienta esencial, su seguridad se ha convertido en un problema crucial para los sistemas de computación. Detectar valores inusuales en grandes volúmenes de información producidos por el tráfico de red ha adquirido un enorme interés en el área de seguridad de redes. La detección de anomalías es el punto de partida para prevenir ataques, por lo tanto es importante para todos los sistemas de computación pertenecientes a una red tener un sistema de detección de eventos anómalos en un tiempo cercano a su ocurrencia. Detectar estos eventos permitiría a los administradores de red identificar fallas en el sistema, tomar acciones preventivas y evitar daños masivos. Este trabajo presenta, primero, cómo identificar anomalías de tráfico en la red aplicando técnicas de computación paralela y Unidades de Procesamiento Gráfico en dos algoritmos, un algoritmo de clasificación supervisada y otro basado en procesamiento de imágenes de tráfico de red. Finalmente, se propone como desafío resolver la detección de anomalías usando un algoritmo no supervisado como Aprendizaje Profundo.Facultad de Informátic

Fast GPU audio identification

Audio identification consist in the ability to pair audio signals of the same perceptual nature. In other words, the aim is to be able to compare an audio signal with a modified versions perceptually equivalent. To accomplish that, an audio fingerprint is extracted from the signals and only the fingerprints are compared to asses the similarity. Some guarantee have to be given about the equivalence between comparing audio fingerprints and perceptually comparing the signals. In designing AFPs, a dense representation is more robust than a sparse one. A dense representation also imply more compute cycles and hence a slower processing speed. To speedup the computing of a very dense audio fingerprint, able to stand stable under noise, re-recording, low-pass filtering, etc., we propose the use of a massive parallel architecture based on the Graphics Processing Unit (GPU) with the CUDA programming kit. We prove experimentally that even with a relatively small GPU and using a single core in the GPU, we are able to obtain a notable speedup per core in a GPU/CPU model. We compared our FFT implementation against state of the art CUFFT obtaining impressive results, hence our FFT implementation can help other areas of application.Presentado en el X Workshop Procesamiento Distribuido y Paralelo (WPDP)Red de Universidades con Carreras en Informática (RedUNCI

Efficient similarity search on multimedia databases

Manipulating and retrieving multimedia data has received increasing attention with the advent of cloud storage facilities. The ability of querying by similarity over large data collections is mandatory to improve storage and user interfaces. But, all of them are expensive operations to solve only in CPU; thus, it is convenient to take into account High Performance Computing (HPC) techniques in their solutions. The Graphics Processing Unit (GPU) as an alternative HPC device has been increasingly used to speedup certain computing processes. This work introduces a pure GPU architecture to build the Permutation Index and to solve approximate similarity queries on multimedia databases. The empirical results of each implementation have achieved different level of speedup which are related with characteristics of GPU and the particular database used.Eje: Workshop Bases de datos y minería de datos (WBDDM)Red de Universidades con Carreras en Informática (RedUNCI

Efficient similarity search on multimedia databases

Manipulating and retrieving multimedia data has received increasing attention with the advent of cloud storage facilities. The ability of querying by similarity over large data collections is mandatory to improve storage and user interfaces. But, all of them are expensive operations to solve only in CPU; thus, it is convenient to take into account High Performance Computing (HPC) techniques in their solutions. The Graphics Processing Unit (GPU) as an alternative HPC device has been increasingly used to speedup certain computing processes. This work introduces a pure GPU architecture to build the Permutation Index and to solve approximate similarity queries on multimedia databases. The empirical results of each implementation have achieved different level of speedup which are related with characteristics of GPU and the particular database used.Eje: Workshop Bases de datos y minería de datos (WBDDM)Red de Universidades con Carreras en Informática (RedUNCI

Towards a parallel image mining system

El análisis de imágenes puede revelar información útil para los usuarios El significativo aumento del uso de imágenes en diferentes campos de la ciencia, medicina, negocios, etc., requiere de mayor poder de procesamiento. Con el avance en la adquisición de dato multimedial y de técnicas de almacenamiento, la necesidad de descubrir automáticamente conocimiento de grandes colecciones de imágenes aumenta. La minería de imágenes, área de investigación relativamente nueva y prometedora, trata de facilitar este trabajo proponiendo soluciones para la extracción de patrones significativos y potencialmente útiles a partir de grandes volúmenes de datos. Comprende diferentes etapas demandantes de recursos y de tiempo computacional. El uso de computación paralela representa un buen punto de partida. El proceso de minería de imágenes parece ser algorítmicamente complejo, requiriendo niveles de poder computacional que solamente los paradigmas paralelos pueden proveer. Dado que involucra conjuntos de datos de rápido crecimiento y las imágenes representan una fuente natural de paralelismo, el paralelismo puede manejar semejante colección en forma efectiva. En este trabajo examinamos el problema de la minería de imágenes y su costo computacional, proponemos una posible solución global y local y definimos futuras extensiones para la minería de imágenes paralela.Images can reveal useful information to human users when are analyzed. The explosive growth in applying images as data in many fields of science, business, medicine, etc, demands greater processing power. With the advances in multimedia data acquisition and storage techniques, the need for automatically discovering knowledge from large image collections is becoming more and more relevant. Image mining, a relatively new and very promising field of investigation, tries to ease this problem proposing some solutions for the extraction of significant and potentially useful patterns from these tremendous data volume. This research field implies different stages, most of them demanding so many resources and computational time. The use of parallel computation is a good starting-point. Image mining process appears to be algorithmically complex requiring computing power levels that only parallel paradigms can provide in a timely way. As data sets involved are large, rapidly growing larger and images provide a natural source of parallelism, parallels computers could be organized to handle such big collection effectively. At this work we will examine the image mining problem with its computational cost, propose a possible global or local parallel solution and also identify some future research directions for image mining parallelism.V Workshop de Computación Gráfica, Imágenes Y VisualizaciónRed de Universidades con Carreras en Informática (RedUNCI