Digital Image Quality Prediction System

[Abstract] “A picture is worth a thousand words.” Based on this well-known adage, we can say that images are important in our society, and increasingly so. Currently, the Internet is the main channel of socialization and marketing, where we seek to communicate in the most efficient way possible. People receive a large amount of information daily and that is where the need to attract attention with quality content and good presentation arises. Social networks, for example, are becoming more visual every day. Only on Facebook can you see that the success of a publication increases up to 180% if it is accompanied by an image. That is why it is not surprising that platforms such as Pinterest and Instagram have grown so much, and have positioned themselves thanks to their power to communicate with images. In a world where more and more relationships and transactions are made through computer applications, many decisions are made based on the quality, aesthetic value or impact of digital images. In the present work, a quality prediction system for digital images was developed, trained from the quality perception of a group of humans.Xunta de Galicia; ED431G 2019/01Xunta de Galicia; ED431D 201716Xunta de Galicia; ED431C 20184

Framework of fully integrated hybrid systems

The final publication is available at Springer via http://dx.doi.org/10.1007/s00521-011-0672-9A framework of fully integrated hybrid systems (HSs) is proposed for the development and management of HS which involve databases, advanced user interfaces, symbolic systems, and artificial neural networks. This framework provides a common input–output interface among those HS modules developed on the framework, with a completely two-directional flow control and a highly parallel processing. This integration framework facilitates the incorporation of heterogeneous modules, together with their subsequent management and updating

Music Recommendation System Based on Ratings Obtained from Amazon

Cursos e Congresos, C-155[Abstract] In the current context of an era in which a significant portion of people are constantly living online, with various multimedia streaming platforms serving as major sources of entertainment, and with e-commerce playing also a key role, recommender systems are carving out their place as one of the most important and widely used tools for enhancing user experiences on these platforms. This work undertakes a comparative study on some of the techniques used within these systems, mainly focused on those based in collaborative filtering. Multiple recommender systems will be implemented according to each of these methods, taking for this purpose the vinyl records and CDs Amazon’s user ratingsCITIC is funded by the Xunta de Galicia through the collaboration agreement between the Consellería de Cultura, Educación, Formación Profesional e Universidades and the Galician universities for the reinforcement of the research centres of the Galician University System (CIGUS)

Visual complexity modelling based on image features fusion of multiple kernels

[Abstract] Humans’ perception of visual complexity is often regarded as one of the key principles of aesthetic order, and is intimately related to the physiological, neurological and, possibly, psychological characteristics of the human mind. For these reasons, creating accurate computational models of visual complexity is a demanding task. Building upon on previous work in the field (Forsythe et al., 2011; Machado et al., 2015) we explore the use of Machine Learning techniques to create computational models of visual complexity. For that purpose, we use a dataset composed of 800 visual stimuli divided into five categories, describing each stimulus by 329 features based on edge detection, compression error and Zipf’s law. In an initial stage, a comparative analysis of representative state-of-the-art Machine Learning approaches is performed. Subsequently, we conduct an exhaustive outlier analysis. We analyze the impact of removing the extreme outliers, concluding that Feature Selection Multiple Kernel Learning obtains the best results, yielding an average correlation to humans’ perception of complexity of 0.71 with only twenty-two features. These results outperform the current state-of-the-art, showing the potential of this technique for regression.Xunta de Galicia; GRC2014/049Portuguese Foundation for Science and Technology; SBIRC; PTDC/EIA EIA/115667/2009Xunta de Galicia; Ref. XUGA-PGIDIT-10TIC105008-PRMinisterio de Ciencia y Tecnología; TIN2008-06562/TINMinisterio de Ecnomía y Competitividad; FJCI-2015-2607

Comparison of Outlier-Tolerant Models for Measuring Visual Complexity

[Abstract] Providing the visual complexity of an image in terms of impact or aesthetic preference can be of great applicability in areas such as psychology or marketing. To this end, certain areas such as Computer Vision have focused on identifying features and computational models that allow for satisfactory results. This paper studies the application of recent ML models using input images evaluated by humans and characterized by features related to visual complexity. According to the experiments carried out, it was confirmed that one of these methods, Correlation by Genetic Search (CGS), based on the search for minimum sets of features that maximize the correlation of the model with respect to the input data, predicted human ratings of image visual complexity better than any other model referenced to date in terms of correlation, RMSE or minimum number of features required by the model. In addition, the variability of these terms were studied eliminating images considered as outliers in previous studies, observing the robustness of the method when selecting the most important variables to make the prediction.The Carlos III Health Institute from the Spanish National plan for Scientific and Technical Research and Innovation 2013-2016 and the European Regional Development Funds (FEDER) “A way to build Europe” support this work through the “Colaborative Project in Genomic Data Integration (CICLOGEN)” Pl17/01826. This work has also been supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia (Ref. ED431G/01, ED431D 2017/16), the “Galician Network for Colorectal Cancer Research” (Ref. ED431D 2017/23) and Competitive Reference Groups (Ref. ED431C 2018/49). On the other hand, the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) was funded by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Funds (FEDER)Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431D 2017/23Xunta de Galicia; ED431C 2018/4

Self-tuning of disk input–output in operating systems

The final publication is available via http://dx.doi.org/10.1016/j.jss.2011.07.030One of the most difficult and hard to learn tasks in computer system management is tuning the kernel parameters in order to get the maximum performance. Traditionally, this tuning has been set using either fixed configurations or the subjective administrator's criteria. The main bottleneck among the subsystems managed by the operating systems is disk input/output (I/O). An evolutionary module has been developed to perform the tuning of this subsystem automatically, using an adaptive and dynamic approach. Any computer change, both at the hardware level, and due to the nature of the workload itself, will make our module adapt automatically and in a transparent way. Thus, system administrators are released from this kind of task and able to achieve some optimal performances adapted to the framework of each of their systems. The experiment made shows a productivity increase in 88.2% of cases and an average improvement of 29.63% with regard to the default configuration of the Linux operating system. A decrease of the average latency was achieved in 77.5% of cases and the mean decrease in the request processing time of I/O was 12.79%

Wi-Fi Handshake: analysis of password patterns in Wi-Fi networks

This work is supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia (Ref. ED431G/01, ED431D 2017/16), the Galician Network for Colorectal Cancer Research (Ref. ED431D 2017/23), Competitive Reference Groups (Ref. ED431C 2018/49) and the Spanish Ministry of Economy and Competitiveness via funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.[Abstract]: This article seeks to provide a snapshot of the security of Wi-Fi access points in the metropolitan area of A Coruña. First, we discuss the options for obtaining a tool that allows the collection and storage of auditable information from Wi-Fi networks, from location to signal strength, security protocol or the list of connected clients. Subsequently, an analysis is carried out aimed at identifying password patterns in Wi-Fi networks with WEP, WPA and WPA2 security protocols. For this purpose, a password recovery tool called Hashcat was used to execute dictionary or brute force attacks, among others, with various word collections. The coverage of the access points in which passwords were decrypted is displayed on a heat map that represents various levels of signal quality depending on the signal strength. From the handshakes obtained, and by means of brute force, we will try to crack as many passwords as possible in order to create a targeted and contextualized dictionary both by geographical location and by the nature of the owner of the access point. Finally, we will propose a contextualized grammar that minimizes the size of the dictionary with respect to the most used ones and unifies the decryption capacity of the combination of all of them.Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431D 2017/23Xunta de Galicia; ED431C 2018/4

Artificial Intelligence Applied to Conceptual Design. A Review of Its Use in Architecture

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] Conceptual architectural design is a complex process that draws on past experience and creativity to generate new designs. The application of artificial intelligence to this process should not be oriented toward finding a solution in a defined search space since the design requirements are not yet well defined in the conceptual stage. Instead, this process should be considered as an exploration of the requirements, as well as of possible solutions to meet those requirements. This work offers a tour of major research projects that apply artificial intelligence solutions to architectural conceptual design. We examine several approaches, but most of the work focuses on the use of evolutionary computing to perform these tasks. We note a marked increase in the number of papers in recent years, especially since 2015. Most employ evolutionary computing techniques, including cellular automata. Most initial approaches were oriented toward finding innovative and creative forms, while the latest research focuses on optimizing architectural form.This project was supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia (Ref. ED431G/01, ED431D 2017/16), and the Spanish Ministry of Economy and Competitiveness via funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER)Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/1

Assisted surface redesign by perturbing its point cloud representation

[Abstract] This research study explores the use of point clouds for design geometrically complex surfaces based on genetic morphogenesis. To this end, a point-based genetic algorithm and the use of massive unstructured point clouds are proposed as a manipulation method of complex geometries. The intent of the algorithm is to improve the design experience, thus different solutions can be presented to designers. The main objective of this work is to provide examples to be adopted as user own or to help them in the creative process. This is not about providing them with a tool to ‘do’ the designer's creative work, but using it as a creative tool in which the user retains control of it. The powerfulness of this approach relies on the fact that the user can use any/diverse criteria (objective or subjective) to evaluate the individuals proposed as possible solutions. As part of this study, the convergence of the algorithm and the ability of diversity in the final populations of the search process will be demonstrated. Various examples of the use of the algorithm are displayed

Validation of an Aesthetic Assessment System for Commercial Tasks

[Abstract] Automatic prediction of the aesthetic value of images has received increasing attention in recent years. This is due, on the one hand, to the potential impact that predicting the aesthetic value has on practical applications. Even so, it remains a difficult task given the subjectivity and complexity of the problem. An image aesthetics assessment system was developed in recent years by our research group. In this work, its potential to be applied in commercial tasks is tested. With this objective, a set of three portals and three real estate agencies in Spain were taken as case studies. Images of their websites were taken to build the experimental dataset and a validation method was developed to test their original order with another proposed one according to their aesthetic value. So, in this new order, the images that have the high aesthetic score by the AI system will occupy the first positions of the portal. Relevant results were obtained, with an average increase of 52.54% in the number of clicks on the ads, in the experiment with Real Estate portals. A statistical analysis prove that there is a significant difference in the number of clicks after selecting the images with the AI system.This work is supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia (Ref. ED431D 201716), Competitive Reference Groups (Ref. ED431C 201849) and Ministry of Science and Innovation project Society challenges (Ref. PID2020-118362RB-I00). We also wish to acknowledge the support received from the Centro de Investigación de Galicia “CITIC”, funded by Xunta de Galicia and the European Union (European Regional Development Fund- Galicia 2014-2020 Program), by grant ED431G 2019/01Xunta de Galicia; ED431D 201716Xunta de Galicia; ED431C 201849Xunta de Galicia; ED431G 2019/0