The number of malicious files detected every year are counted by millions. One of the main reasons for these high volumes of different files is the fact that, in order to evade detection, malware authors add mutation. This means that malicious files belonging to the same family, with the same malicious behavior, are constantly modified or obfuscated using several techniques, in such a way that they look like different files. In order to be effective in analyzing and classifying such large amounts of files, we need to be able to categorize them into groups and identify their respective families on the basis of their behavior. In this paper, malicious software is visualized as gray scale images since its ability to capture minor changes while retaining the global structure helps to detect variations. Motivated by the visual similarity between malware samples of the same family, we propose a file agnostic deep learning approach for malware categorization to efficiently group malicious software into families based on a set of discriminant patterns extracted from their visualization as images. The suitability of our approach is evaluated against two benchmarks: the MalImg dataset and the Microsoft Malware Classification Challenge dataset. Experimental comparison demonstrates its superior performance with respect to state-of-the-art techniques.

Artificial intelligence techniques have been successfully applied to control dynamic systems looking for an optimal control. Among those techniques, reinforcement learning has been shown as particularly effective at reducing the dimensionality of some real problems and solving control problems by learning from experience. The use of thermal energy storage active systems in the building sector is identified as suitable option to reduce their energy demand for heating and cooling. However, these systems might be expensive and require appropriate control strategies in order to improve the performance of the building. In this paper a ventilated facade with PCM is controlled using a reinforcement learning algorithm. The ventilated facade uses mechanical ventilation during nighttime to solidify the PCM and releases this cold stored to the inner environment during the peak demand period. It is crucial to decide correctly the schedule of charge and discharge process of the PCM according to the weather and indoor conditions. An experimentally validated numerical model is used to test the performance of the control algorithm under different weather conditions. Important improvements on the energy savings due to the use of control strategies were found and supported by the data under the different tested climatic conditions.The work partially funded by the Spanish government (ENE2011-28269-C03-01, ENE2011-22722 and ULLE10-4E-1305). The authors would like to thank the Catalan Government for the quality accreditation given to their research group (2014-SGR-123). The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement #PIRSES-GA-2013-610692 (INNOSTORAGE). Alvaro de Gracia would like to thank Education Ministry of Chile for Grant PMI ANT1201

The aim of this research is to confirm whether there is a relationship between the number of reviews and the hotel’s score on Booking.com and TripAdvisor and whether the relationship is different depending on the geographical area. Moreover, the study endeavors to confirm whether the number of reviews influences the score on each website. With the analysis of about 13,899 hotels in 146 cities, our findings suggest that there is some lineal relationship between the amount of reviews and the score on TripAdvisor but not on Booking.com. Moreover, by regions on TripAdvisor hotels from Middle East and Africa and Asia and Pacific have a stronger relationship between reviews and score than those from Europe and America.