Detección visual de vehículos automotrices en ambientes reales

Entre los algoritmos de detección de objetos, cascade ha demostrado ser uno de los más robustos y flexibles al ser aplicado sobre un gran número de diferentes tipos de objetos. La detección de rostros fue la primera aplicación, así como para muchos sistemas en producción. De igual forma, uno de los grandes objetivos buscados ha sido el de diseñar un vehículo completamente autónomo y donde la conducción se realice de forma automática sin intervención humana. Es por esto que se ha utilizado la combinación de algoritmos cascade y Adaboost para crear un sistema que sea capaz de detectar vehículos de forma eficiente. Como base para este trabajo, se ha utilizado la implementación de OpenCV, que es un software que se distribuye bajo una licencia open source, la cual ha permitido realizar cambios en la implementación de las características tipo HAAR para agregar una serie de características capaces de aumentar el poder de reconocimiento de vehículos. Estas características, en conjunto con las que originalmente se encuentran implementadas por OpenCV, han permitido mejorar los niveles de detección de vehículos en secuencias de imágenes, además, con los entrenamientos realizados se pudo observar cierta reducción en el número de falsos negativos. De acuerdo con la el esquema de este conjunto de algoritmos, adaboost es el encargado de realizar el entrenamiento; entonces, es durante el entrenamiento que se definen los tipos de características tipo HAAR que se utilizarán tanto en el entrenamiento como durante la etapa de detección. Durante el entrenamiento, dicho conjunto de características sirve únicamente como referencias para generar las ventanas de búsqueda en el proceso de detección.Consejo Nacional de Ciencia y Tecnologí

Detection of Temporality at Discourse Level on Financial News by Combining Natural Language Processing and Machine Learning

Finance-related news such as Bloomberg News, CNN Business and Forbes are valuable sources of real data for market screening systems. In news, an expert shares opinions beyond plain technical analyses that include context such as political, sociological and cultural factors. In the same text, the expert often discusses the performance of different assets. Some key statements are mere descriptions of past events while others are predictions. Therefore, understanding the temporality of the key statements in a text is essential to separate context information from valuable predictions. We propose a novel system to detect the temporality of finance-related news at discourse level that combines Natural Language Processing and Machine Learning techniques, and exploits sophisticated features such as syntactic and semantic dependencies. More specifically, we seek to extract the dominant tenses of the main statements, which may be either explicit or implicit. We have tested our system on a labelled dataset of finance-related news annotated by researchers with knowledge in the field. Experimental results reveal a high detection precision compared to an alternative rule-based baseline approach. Ultimately, this research contributes to the state-of-the-art of market screening by identifying predictive knowledge for financial decision making

Description of a session to assess the efectiveness of apps in medical education

[EN]Nowadays, there are few researches about the effectiveness of apps in education. With this paper, we want to describe the methodology and the initial results obtained with the first session performed with undergraduate students of Medical Schools in University of Salamanca. The session lasted roughly two hours and was formed by ten students. We used a descriptive correlational method and the instruments utilized to obtain the qualitative data were a questionnaire and the images recorded and observed during the session. Besides it was necessary to divide the participants in two groups in order to contrast the results between them: control group and experimental group. One of them had to attend a normal class and the other one was going to use an app, which describes the anatomical parts of a brain. The results were measured with a knowledge test performed before and after the session. Both groups obtained better results after the experiment, however with the participants of the experimental group, the results were higher than the students of the control group. It is very complex to measure the effectiveness of mobile apps in education, and although the study suggests that the mobile apps could be a tool to use in higher education, it is necessary to repeat the sessions with different students in order to have more samples, which allow showing and verifying the effectiveness of apps in education

Analysis of mobile devices as a support tool for professional medical education in the university school

[EN] According to the report of International Telecommunications Union (ITU), there are approximately 6.800 millions of users in the world with a mobile device. The fast evolution of these mobile devices for the last two decades has made the mobile phone become a minicomputer with a data connection. Because of the use of these mobile phones, the mobile applications appeared just one year later that the launching of the first iPhone and now the number of these applications reach more than 1 million in play store or app store (two application market for two different operation systems). All these data illustrates the considerable importance of the mobile devices in our society and the tremendous opportunity for the medical education. The physicians and the medical students are using more and more these mobile devices daily in their work or even in their self-learning. Currently, some university schools of medicine in EEUU have already introduced the tablets as a new tool for education. The aim of this study is to make an analysis of the current usage of mobile devices and mobile applications in the University medical school and the main advantages and disadvantages of introducing this tool as part of the education curriculum

Detection of financial opportunities in micro-blogging data with a stacked classification system

Micro-blogging sources such as the Twitter social network provide valuable real-time data for market prediction models. Investors' opinions in this network follow the fluctuations of the stock markets and often include educated speculations on market opportunities that may have impact on the actions of other investors. In view of this, we propose a novel system to detect positive predictions in tweets, a type of financial emotions which we term "opportunities" that are akin to "anticipation" in Plutchik's theory. Specifically, we seek a high detection precision to present a financial operator a substantial amount of such tweets while differentiating them from the rest of financial emotions in our system. We achieve it with a three-layer stacked Machine Learning classification system with sophisticated features that result from applying Natural Language Processing techniques to extract valuable linguistic information. Experimental results on a dataset that has been manually annotated with financial emotion and ticker occurrence tags demonstrate that our system yields satisfactory and competitive performance in financial opportunity detection, with precision values up to 83%. This promising outcome endorses the usability of our system to support investors' decision making

Review of the cutting-edge technology employed in medical education

[EN]The new technologies have advanced astonishingly in the last few decades. There are more and more Medical Schools adopting new tools to teach Medicine to undergraduate students or even to teach the continuous training for professionals. Not all the Universities adopt these technologies at same level or same grade of the speed but as a result it seems that they will adopt more and more often the new technologies as part of the curriculum. This paper wants to be a review of the state of the art technologies that have stepped in the Medical Schools in the last decade. Overall, we want to describe the function of these new tools, how all of them have been adopting to teach Medicine answering most part of the demands of physicians and how they could be evolved in the future to continue making the medical education a new revolutionary industry in continual progress. Not only that, it makes the engineering biomedical a field very interesting to explore and to be invested on, as they could enhance the skills of new professionals of Medicine to be prepared for the digital environment where they will work on. It is important to notice that the advanced technologies are enhanced at more speed than the education is able to adopt in. Sometimes, the reason could be the unawareness of these technologies. Occasionally, it could be the money needed to invest and from time to time it could be that the leaderships of Medical Schools are not convinced enough that the new technologies will work

A systematic review of using mobile devices in medical education

[EN] Abstract—There are many research studies carried out about the use of mobile devices in our society. Mobile devices offer great opportunities in different aspects of our daily routine as it enable people to be connected at any time. The increasing number of mobile devices and their use confirm that the new technologies are part of our lives. Not only that, medical professionals are starting to be involved actively in the use of mobile devices. This paper describes how students and medical professionals use mobile devices from an educational perspective and it investigates the roles of their involvement. To achieve this goal, we performed a crosssectional survey involving undergraduate medical students of University of Salamanca and medical professionals. The results confirm that the new technologies are becoming part of our lives and medical professionals are starting to be part of this upward trend