Libro de Actas JCC&BD 2018 : VI Jornadas de Cloud Computing & Big Data

Se recopilan las ponencias presentadas en las VI Jornadas de Cloud Computing & Big Data (JCC&BD), realizadas entre el 25 al 29 de junio de 2018 en la Facultad de Informática de la Universidad Nacional de La Plata.Universidad Nacional de La Plata (UNLP) - Facultad de Informátic

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed

Performance Analysis of an Hybrid MPI/OpenMP ALM Software for Life Insurance Policies on Multi-core Architectures

The application of new insurance and reinsurance regulation introduced by the European Directive 2009/138 (Solvency II) [4] leads to a complex valuation process to assess risks and determine the overall solvency needs. The development of an “internal model” – “a risk management system developed by an insurer to analyse its overall risk position, to quantify risks and to determine the economic capital required to meet those risks” [5] – generates hard computational problems. The perfect timing of measurements and consequent management actions must be further safeguard. It stands to reason that the computational performance of the valuation process plays a relevant role; this motivates the need to develop both accurate and efficient numerical algorithms and to use High Performance Computing (HPC) methodologies and resources. The literature on the application of HPC in the development of “internal model” is very poor; a relevant contribution is given in [1] where is introduced DISAR (Dynamic Investment Strategy with Accounting Rules), a Solvency II compliant system designed to work on a grid of conventional computers. In [2] numerical experiments carried out applying to DISAR a parallelisation strategy based on the distribution of Monte Carlo simulations among processors are reported. The developed parallel software is tested on an IBM Bladecenter using pure MPI implementation and treating every core as a separate entity with its own address space. Now, we show some of experiences in adding a layer of shared memory threading trying to optimize the application built using MPI and OpenMP. At this aim, we use some tools and techniques for tuning the hybrid MPI/OpenMP DISAR implementation