Discovering the Needs of People at the 10/40 Window with Data Science

Abstract: We propose an approach to discover the needs of people in particular areas of Iraq, a country located at the 10/40 Window, by means of Data Science applied to Open Big Data. The resulting model predicts refugee crises and artillery attacks with high accuracy even in areas with scarce data

Facing uncertainty in web service compositions

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works[EN] Web service compositions run in complex computing infrastructures where arising events may affect the quality of the system. However, crucial Web service compositions cannot be stopped to apply changes to deal with problematic events. Therefore, the trend is moving towards context-aware Web service compositions, which use context information as a basis for autonomic changes. Under the closed-world assumption, the context and possible adaptations are fully known at design time. Nevertheless, it is difficult to foresee all the possible situations arising in uncertain contexts. In this paper, we leverage models at runtime to guide the dynamic evolution of context-aware Web service compositions to deal with unexpected events in the open world. In order to manage uncertainty, a model that abstracts the Web service composition, self-evolves to preserve requirements. The evolved model guides changes in the underlying WS-BPEL composition schema. A prototype and an evaluation demonstrate the feasibility of our approach.This work has been developed with the support of MICINN under the project everyWare TIN2010-18011 and co-financed with ERDF.Alférez, GH.; Pelechano Ferragud, V. (2013). Facing uncertainty in web service compositions. En Web Services (ICWS), 2013 IEEE 20th International Conference on. IEEE Computer Society. 219-226. https://doi.org/10.1109/ICWS.2013.38S21922

Achieving autonomic Web service compositions with models at runtime

[EN] Several exceptional situations may arise in the complex, heterogeneous, and changing contexts where Web service operations run. For instance, a Web service operation may have greatly increased its execution time or may have become unavailable. The contribution of this article is to provide a tool-supported framework to guide autonomic adjustments of context-aware service compositions using models at runtime. During execution, when problematic events arise in the context, models are used by an autonomic architecture to guide changes of the service composition. Under the closed-world assumption, the possible context events are fully known at design time. Nevertheless, it is difficult to foresee all the possible situations arising in uncertain contexts where service compositions run. Therefore, the proposed framework also covers the dynamic evolution of service compositions to deal with unexpected events in the open world. An evaluation demonstrates that our framework is efficient during dynamic adjustments.Alférez-Salinas, GH.; Pelechano Ferragud, V. (2017). Achieving autonomic Web service compositions with models at runtime. Computers & Electrical Engineering. 63:332-352. doi:10.1016/j.compeleceng.2017.08.004S3323526

¿Venimos de una simulación computacional?

Cada vez más científicos en diversas áreas del conocimiento reconocen un marcado orden en la naturaleza que hace pensar en un diseño inteligente. De hecho, muchos han llegado a la conclusión de que no hay campo para el azar en la formación del universo delicadamente equilibrado. No obstante, algunos no reconocen a Dios como el Gran Diseñador por temor a caer en la perspectiva conocida como “el Dios de los vacíos”. Al intentar encontrar una respuesta acerca de nuestros orígenes, científicos y personalidades públicas como Elon Musk, piensan que venimos de una simulación, creada por seres super inteligentes con alto conocimiento tecnológico. Esta idea se ha venido popularizando en la literatura científica y en el cine. En esta presentación ahondaremos en este tema actual relacionado con nuestros orígenes a la luz de la Biblia

Achieving digital transformation with data science

The world is changing and becoming rapidly digital. This digital transformation requires three new competences related to data: 1) collecting, handling and utilizing high volumes of data; 2) building agile data science and machine learning software products in the cloud; and 3) redesigning and adapting the existing business model. In this context, this presentation gives an introduction to data science for digital transformation. The presentation is focused on machine learning, a subfield of artificial intelligence, and a key component of data science. Several examples are given in different areas of knowledge

Introducing Database Normal Forms to Students: A Comparison Between Theory-First and Practice-First Educational Approaches

Educating the future generation of computer scientists and engineers often proves to be challenging, and how the content is introduced plays a large role in how well students will learn. One of the primary challenges that instructors face is regarding the introduction of important theory to students, both to show its essential nature to the field as well as its practicality. This paper analyzes two pedagogical methods for the instruction of normal forms in database management systems, a mandatory topic in any database course. The first of these methods is a theory-based approach that relies on written works (i.e., theory) to introduce the concept. The second of these focuses on a practice-based approach (i.e., practice) which aligns with the normal form as students implement a database schema. Through a small study, it was determined that most students have a strong predisposition to theory-first education, though students seemed to prefer the practice-based approach more than the theory-first approach. This paper compares the two methodologies, given this insight, and advises the use of an appropriate method for future educators

Database Query Execution Through Virtual Reality

Building database queries often requires technical knowledge of a query language. However, company employees (outside of software development, generally) may not have the expertise to accurately construct and execute database queries. Moreover, in the era of Big Data, we believe that it is important to count on more flexible interfaces that may allow users interact with data in a more natural and immersive way. Our contribution is to utilize virtual reality (VR) to construct database queries and execute them on relational databases. Using natural hand or controller gestures, this approach seeks to provide easy access to build and visualize database queries. This is a first approach towards more flexible interfaces to interact with data

Automatic Classification of Felsic, Mafic, and Ultramafic Rocks in Satellite Images from Palmira and La Victoria, Colombia

Manually inspecting and analyzing satellite images can lead to numerous errors and is quite time consuming. Our geological contribution is to offer a means for the automatic classification of areas with felsic, mafic, and ultramafic rocks via machine learning using satellite images from Palmira and La Victoria, Colombia. Specifically, this study focuses on two types of satellite images taken from the Earth Observation System (EOS), namely natural color (bands B04 B03 B02) and infrared color vegetation (B08 B04 B03). The following machine learning algorithms were used in this study: Random Forest, K-Nearest Neighbors, Support Vector Machines, Logistic Regression, and Multilayer Perceptron. The model generated with K-Nearest Neighbors performed best for classifying natural color images with an accuracy of 91%, a precision of 87%, and a recall of 88%. Random Forest was the best model for classifying infrared images with an overall accuracy of 83%, a precision of 31%, and a recall of 31%

Automatic Classification of Felsic, Mafic, and Ultramafic Rocks in Satellite Images from Palmira and La Victoria, Colombia

Manually inspecting and analyzing satellite images can lead to numerous errors and is quite time consuming. Our geological contribution is to offer a means for the automatic classification of areas with felsic, mafic, and ultramafic rocks via machine learning using satellite images from Palmira and La Victoria, Colombia. Specifically, this study focuses on two types of satellite images taken from the Earth Observation System (EOS), namely natural color (bands B04 B03 B02) and infrared color vegetation (B08 B04 B03). The following machine learning algorithms were used in this study: Random Forest, K-Nearest Neighbors, Support Vector Machines, Logistic Regression, and Multilayer Perceptron. The model generated with K-Nearest Neighbors performed best for classifying natural color images with an accuracy of 91%, a precision of 87%, and a recall of 88%. Random Forest was the best model for classifying infrared images with an overall accuracy of 83%, a precision of 31%, and a recall of 31%