Energy Saving In Data Centers

Globally CO2 emissions attributable to Information Technology are on par with those resulting from aviation. Recent growth in cloud service demand has elevated energy efficiency of data centers to a critical area within green computing. Cloud computing represents a backbone of IT services and recently there has been an increase in high-definition multimedia delivery, which has placed new burdens on energy resources. Hardware innovations together with energy-efficient techniques and algorithms are key to controlling power usage in an ever-expanding IT landscape. This special issue contains a number of contributions that show that data center energy efficiency should be addressed from diverse vantage points. © 2017 by the authors. Licensee MDPI, Basel, Switzerland

Dagstuhl News January - December 2002

"Dagstuhl News" is a publication edited especially for the members of the Foundation "Informatikzentrum Schloss Dagstuhl" to thank them for their support. The News give a summary of the scientific work being done in Dagstuhl. Each Dagstuhl Seminar is presented by a small abstract describing the contents and scientific highlights of the seminar as well as the perspectives or challenges of the research topic

On the Value of Pedagogical Assets

[EN] University education is facing new strategical changes that will lead to deep structural changes. Course organization is evolving and the organizational decisions have an economical impact. We propose a method to measure the present value of a pedagogical asset under a return rate. We apply the method to three courses in the Computer Science curricula taught at the Facultat d’Informatica de Barcelona of the Universitat Politècnica de Catalunya, Barcelona Tech.  A large, compulsory, first year course (PRO1), a medium size undergraduate course (ALG) and a small specialized master course (AGT). Our results highlight that the present value gets higher values as a function of the size of the course and it goes in a negative relationship with respect  to the level of computer support involved in their teaching.This worok was partially supported by MINECO and FEDER funds under grant TIN2017-86727-C2-1-R (GRAMM) and AGAUR under grant 2017SGR-786. M. Serna was also partially funded by MINECO under grant MDM-2014-044 (BGSMath).http://ocs.editorial.upv.es/index.php/HEAD/HEAD18Gabarro, J.; Serna, M. (2018). On the Value of Pedagogical Assets. Editorial Universitat Politècnica de València. 425-432. https://doi.org/10.4995/HEAD18.2018.8007OCS42543

New technologies for big multimedia data treatment

With the technology advance and the growth of Internet, the information that can be found in this net, as well as the number of users that access to look for specific data is bigger. Therefore, it is desirable to have a search system that allows to retrieve information at a reasonable time and in an efficient way. In this paper we show two computing paradigms appropriate to apply in the treatment of large amounts of data consisting of objects such as images, text, sound and video, using hybrid computing over MPI+OpenMP and GPGPU. The proposal is developed through experience gained in the construction of various indexes and the subsequent search, through them, of multimedia objects.Fil: Barrionuevo, Mercedes Deolinda. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina;Fil: Britos, Luis. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina;Fil: Bustos, Fabricio. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina;Fil: Gil Costa, Graciela Verónica. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina;Fil: Lopresti, Mariela. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina;Fil: Mancini, Virginia. Universidad Nacional de San Luis. Facultad de Cs.fisico Matematicas y Naturales. Laboratorio de Inv.en Inteligencia Artificial; Argentina;Fil: Miranda, Natalia Carolina. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina;Fil: Ochoa, Cesar. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina;Fil: Piccoli, María Fabiana. Universidad Nacional de San Luis. Facultad de Cs.fisico Matematicas y Naturales. Laboratorio de Inv.en Inteligencia Artificial; Argentina;Fil: Printista, Alicia Marcela. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina;Fil: Reyes, Nora Susana. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina

A comparative evaluation of algorithms for discovering translational patterns in Baroque keyboard works

We consider the problem of intra-opus pattern discovery, that is, the task of discovering patterns of a specified type within a piece of music. A music analyst undertook this task for works by Domenico Scarlattti and Johann Sebastian Bach, forming a benchmark of 'target' patterns. The performance of two existing algorithms and one of our own creation, called SIACT, is evaluated by comparison with this benchmark. SIACT out-performs the existing algorithms with regard to recall and, more often than not, precision. It is demonstrated that in all but the most carefully selected excerpts of music, the two existing algorithms can be affected by what is termed the 'problem of isolated membership'. Central to the relative success of SIACT is our intention that it should address this particular problem. The paper contrasts string-based and geometric approaches to pattern discovery, with an introduction to the latter. Suggestions for future work are given

Persistence Bag-of-Words for Topological Data Analysis

Persistent homology (PH) is a rigorous mathematical theory that provides a robust descriptor of data in the form of persistence diagrams (PDs). PDs exhibit, however, complex structure and are difficult to integrate in today's machine learning workflows. This paper introduces persistence bag-of-words: a novel and stable vectorized representation of PDs that enables the seamless integration with machine learning. Comprehensive experiments show that the new representation achieves state-of-the-art performance and beyond in much less time than alternative approaches.Comment: Accepted for the Twenty-Eight International Joint Conference on Artificial Intelligence (IJCAI-19). arXiv admin note: substantial text overlap with arXiv:1802.0485