Nowadays, the constant development of information and communication technologies (ICTs) has changed the inter-personal interaction, allowing to transfer real experiences to a virtualized medium such as Internet. In this sense, although the space-time barriers of traditional communication are broken and social relationships are strengthened, problems related to adverse behaviors may arise. Bullying, defined as an act that threatens a person’s holistic well-being, becomes cyberbullying when it is done over Internet, causing anxiety problems, depression and even suicide attempts. For this reason, it is essential to detect this type of behaviour in time. This research deploys a Spanish cyberbullying prevention system (SPC), which relies on Natural Language Processing (NLP) methods and different machine learning techniques (Naive Bayes, Support Vector Machine and Logistic Regression), using Twitter as the basis for the extraction of knowledge bases or corpus. Several precision metrics and variable corpus sizes are used for the training. The learning results reach a maximum accuracy of 93%, verified through the application of three study cases.

The measurement of consumption of public services such as drinking water and electric energy, and information management is one of the problems facing intelligent cities. In this article we describe the design of a computer system that allows the management and administration of consumption records made by smart meters that communicate through MQTT using mobile telecommunications networks. The results obtained allowed to determine the efficiency of the connectivity through MQTT over GPRS and to perform tests of the computer system in order to validate its functionalities.

Latent Semantic Analysis (LSA) is a method that allows us to automatically index and retrieve information from a set of objects by reducing the term-by-document matrix using the Singular Value Decomposition (SVD) technique. However, LSA has a high computational cost for analyzing large amounts of information. The goals of this work are (i) to improve the execution time of semantic space construction, dimensionality reduction, and information retrieval stages of LSA based on heterogeneous systems and (ii) to evaluate the accuracy and recall of the information retrieval stage. We present a heterogeneous Latent Semantic Analysis (hLSA) system, which has been developed using General-Purpose computing on Graphics Processing Units (GPGPUs) architecture, which can solve large numeric problems faster through the thousands of concurrent threads on multiple CUDA cores of GPUs and multi-CPU architecture, which can solve large text problems faster through a multiprocessing environment. We execute the hLSA system with documents from the PubMed Central (PMC) database. The results of the experiments show that the acceleration reached by the hLSA system for large matrices with one hundred and fifty thousand million values is around eight times faster than the standard LSA version with an accuracy of 88% and a recall of 100%.