Lessons Learned From Research on Multimedia Summarization

workshopInternational audienc

DOME: recommendations for supervised machine learning validation in biology

Supervised machine learning is widely used in biology and deserves more scrutiny. We present a set of community-wide recommendations (DOME) aiming to help establish standards of supervised machine learning validation in biology. Formulated as questions, the DOME recommendations improve the assessment and reproducibility of papers when included as supplementary material.The work of the Machine Learning Focus Group was funded by ELIXIR, the research infrastructure for life-science data. IW was funded by the A*STAR Career Development Award (project no. C210112057) from the Agency for Science, Technology and Research (A*STAR), Singapore. D.F. was supported by Estonian Research Council grants (PRG1095, PSG59 and ERA-NET TRANSCAN-2 (BioEndoCar)); Project No 2014-2020.4.01.16-0271, ELIXIR and the European Regional Development Fund through EXCITE Center of Excellence. S.C.E.T. has received funding from the European Union’s Horizon 2020 research and innovation programme under Marie Skłodowska-Curie Grant agreements No. 778247 and No. 823886, and Italian Ministry of University and Research PRIN 2017 grant 2017483NH8.Peer Reviewed"Article signat per 8 autors més 28 autors/es de l' ELIXIR Machine Learning Focus Group: Emidio Capriotti, Rita Casadio, Salvador Capella-Gutierrez, Davide Cirillo, Alessio Del Conte, Alexandros C. Dimopoulos, Victoria Dominguez Del Angel, Joaquin Dopazo, Piero Fariselli, José Maria Fernández, Florian Huber, Anna Kreshuk, Tom Lenaerts, Pier Luigi Martelli, Arcadi Navarro, Pilib Ó Broin, Janet Piñero, Damiano Piovesan, Martin Reczko, Francesco Ronzano, Venkata Satagopam, Castrense Savojardo, Vojtech Spiwok, Marco Antonio Tangaro, Giacomo Tartari, David Salgado, Alfonso Valencia & Federico Zambelli"Postprint (author's final draft

Design and implementation of a Marking Strategy to Increase the Contactability in the Call Centers, Based on Machine Learning

Jamar is a company that belongs to the furniture sector, which manufactures and sells furniture and accessories for the home. Customer calls are one of the most trusted channels used in contact centers. Currently, the contactability indicator has a goal of 40% and is at 31%. The enemies of the efficiency of this channel are the terrible dimensioning, customers who evade answering these calls by identifying the numbers, non-market numbers in the databases, failures in the technological resources. Therefore, a proposal was made to design and implement a marking strategy in the call center, supported by a statistical model for dimensioning. Likewise, emerging technology such as Machine Learning is performed to help the marking strategy in outbound campaigns, reconfiguration of the dialplan to make it more efficient, and a redundant architecture design in the operators. Basic concepts of Teletraffic are explained, showing its primary functions, relevant for the management of the company's telephone system. In the same way, fundamentals of the Asterisk IP PBX are exposed, one of the most used in our environment due to its versatility and low implementation cost. The methodology of descriptive and applied research is used for the development of the project. The results and discussion show the dialing strategy and some call statistics from previous years, necessary to establish a correct dimensioning of the solution. The proposed solution allows having redundancy management for SIP and trunk operators, to have backup and reliability in case of failure