MirBot: A collaborative object recognition system for smartphones using convolutional neural networks

MirBot is a collaborative application for smartphones that allows users to perform object recognition. This app can be used to take a photograph of an object, select the region of interest and obtain the most likely class (dog, chair, etc.) by means of similarity search using features extracted from a convolutional neural network (CNN). The answers provided by the system can be validated by the user so as to improve the results for future queries. All the images are stored together with a series of metadata, thus enabling a multimodal incremental dataset labeled with synset identifiers from the WordNet ontology. This dataset grows continuously thanks to the users' feedback, and is publicly available for research. This work details the MirBot object recognition system, analyzes the statistics gathered after more than four years of usage, describes the image classification methodology, and performs an exhaustive evaluation using handcrafted features, convolutional neural codes and different transfer learning techniques. After comparing various models and transformation methods, the results show that the CNN features maintain the accuracy of MirBot constant over time, despite the increasing number of new classes. The app is freely available at the Apple and Google Play stores.Comment: Accepted in Neurocomputing, 201

A Security Pattern for Cloud service certification

Cloud computing is interesting from the economic, operational and even energy consumption perspectives but it still raises concerns regarding the security, privacy, governance and compliance of the data and software services offered through it. However, the task of verifying security properties in services running on cloud is not trivial. We notice the provision and security of a cloud service is sensitive. Because of the potential interference between the features and behavior of all the inter-dependent services in all layers of the cloud stack (as well as dynamic changes in them). Besides current cloud models do not include support for trust-focused communication between layers. We present a mechanism to implement cloud service certification process based on the usage of Trusted Computing technology, by means of its Trusted Computing Platform (TPM) implementation of its architecture. Among many security security features it is a tamper proof resistance built in device and provides a root of trust to affix our certification mechanism. We present as a security pattern the approach for service certification based on the use TPM.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

MirBot: A Multimodal Interactive Image Retrieval System

This study presents a multimodal interactive image retrieval system for smartphones (MirBot). The application is designed as a collaborative game where users can categorize photographs according to the WordNet hierarchy. After taking a picture, the region of interest of the target can be selected, and the image information is sent with a set of metadata to a server in order to classify the object. The user can validate the category proposed by the system to improve future queries. The result is a labeled database with a structure similar to ImageNet, but with contents selected by the users, fully marked with regions of interest, and with novel metadata that can be useful to constrain the search space in a future work. The MirBot app is freely available on the Apple app store.This study was supported by the Consolider Ingenio 2010 program (MIPRCV, CSD2007-00018), the PASCAL2 Network of Excellence IST-2007-216886, and the Spanish CICyT TIN2009-14205-C04-C1

Multi-label logo recognition and retrieval based on weighted fusion of neural features

Classifying logo images is a challenging task as they contain elements such as text or shapes that can represent anything from known objects to abstract shapes. While the current state of the art for logo classification addresses the problem as a multi-class task focusing on a single characteristic, logos can have several simultaneous labels, such as different colours. This work proposes a method that allows visually similar logos to be classified and searched from a set of data according to their shape, colour, commercial sector, semantics, general characteristics, or a combination of features selected by the user. Unlike previous approaches, the proposal employs a series of multi-label deep neural networks specialized in specific attributes and combines the obtained features to perform the similarity search. To delve into the classification system, different existing logo topologies are compared and some of their problems are analysed, such as the incomplete labelling that trademark registration databases usually contain. The proposal is evaluated considering 76,000 logos (seven times more than previous approaches) from the European Union Trademarks dataset, which is organized hierarchically using the Vienna ontology. Overall, experimentation attains reliable quantitative and qualitative results, reducing the normalized average rank error of the state-of-the-art from 0.040 to 0.018 for the Trademark Image Retrieval task. Finally, given that the semantics of logos can often be subjective, graphic design students and professionals were surveyed. Results show that the proposed methodology provides better labelling than a human expert operator, improving the label ranking average precision from 0.53 to 0.68.This work was supported by the Pattern Recognition and Artificial Intelligence Group (PRAIG) from the University of Alicante and the University Institute for Computing Research (IUII). The Conselleria d'Innovació, Universitats, Ciència I Societat Digital from Generalitat Valenciana and FEDER provided some of the computing resources used in this project through IDIFEDER/2020/003. This research was partially supported by the Conselleria de Educación, Universidades y Empleo, for the project "clasifIA" of the Escola Superior d'Art i Disseny d'Alacant

A selectional auto-encoder approach for document image binarization

Binarization plays a key role in the automatic information retrieval from document images. This process is usually performed in the first stages of document analysis systems, and serves as a basis for subsequent steps. Hence it has to be robust in order to allow the full analysis workflow to be successful. Several methods for document image binarization have been proposed so far, most of which are based on hand-crafted image processing strategies. Recently, Convolutional Neural Networks have shown an amazing performance in many disparate duties related to computer vision. In this paper we discuss the use of convolutional auto-encoders devoted to learning an end-to-end map from an input image to its selectional output, in which activations indicate the likelihood of pixels to be either foreground or background. Once trained, documents can therefore be binarized by parsing them through the model and applying a global threshold. This approach has proven to outperform existing binarization strategies in a number of document types.This work was partially supported by the Social Sciences and Humanities Research Council of Canada, the Spanish Ministerio de Ciencia, Innovación y Universidades through Juan de la Cierva - Formación grant (Ref. FJCI-2016-27873), and the Universidad de Alicante through grant GRE-16-04

Mathematical Modeling of the Mojave Solar Plants

Competitiveness of solar energy is one of current main research topics. Overall efficiency of solar plants can be improved by using advanced control strategies. To design and tuning properly advanced control strategies, a mathematical model of the plant is needed. The model has to fulfill two important points: (1) It has to reproduce accurately the dynamics of the real system; and (2) since the model is used to test advanced control strategies, its computational burden has to be as low as possible. This trade-off is essential to optimize the tuning process of the controller and minimize the commissioning time. In this paper, the modeling of the large-scale commercial solar trough plants Mojave Beta and Mojave Alpha is presented. These two models were used to test advanced control strategies to operate the plants.Comisión Europea OCONTSOLAR 78905

Epistolario de Leopoldo Balbás a Antonio Gallego Burín

Epistolario de Leopoldo Balbás a Antonio Gallego Burí

Mathematical Modeling of the Parabolic Trough Collector Field of the TCP-100 Research Plant

The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016 Oulu (Finlandia)There are two main drawbacks when operating solar energy systems: a) the resulting energy costs are not yet competitive and b) solar energy is not always available when needed. In order to improve the overall solar plants efficiency, advances control techniques play an important role. In order to develop efficient and robust control techniques, the use of accurate mathematical models is crucial. In this paper, the mathematical modeling of the new TCP100 parabolic trough collector (PTC) research facility at the Plataforma Solar de Almería is presented. Some simulations are shown to demonstrate the adequate behavior of the model compared to the facility design conditions.Junta de Andalucía P11-TEP-8129Unión Europea FP7-ICT-ICT-2013.3.4-611281Ministerio de Economía y Competitividadt DPI2014-56364-C2-2-

Detection of bodies in maritime rescue operations using Unmanned Aerial Vehicles with multispectral cameras

In this study, we use unmanned aerial vehicles equipped with multispectral cameras to search for bodies in maritime rescue operations. A series of flights were performed in open‐water scenarios in the northwest of Spain, using a certified aquatic rescue dummy in dangerous areas and real people when the weather conditions allowed it. The multispectral images were aligned and used to train a convolutional neural network for body detection. An exhaustive evaluation was performed to assess the best combination of spectral channels for this task. Three approaches based on a MobileNet topology were evaluated, using (a) the full image, (b) a sliding window, and (c) a precise localization method. The first method classifies an input image as containing a body or not, the second uses a sliding window to yield a class for each subimage, and the third uses transposed convolutions returning a binary output in which the body pixels are marked. In all cases, the MobileNet architecture was modified by adding custom layers and preprocessing the input to align the multispectral camera channels. Evaluation shows that the proposed methods yield reliable results, obtaining the best classification performance when combining green, red‐edge, and near‐infrared channels. We conclude that the precise localization approach is the most suitable method, obtaining a similar accuracy as the sliding window but achieving a spatial localization close to 1 m. The presented system is about to be implemented for real maritime rescue operations carried out by Babcock Mission Critical Services Spain.This study was performed in collaboration with BabcockMCS Spain and funded by the Galicia Region Government through the Civil UAVs Initiative program, the Spanish Government’s Ministry of Economy, Industry, and Competitiveness through the RTC‐2014‐1863‐8 and INAER4‐14Y (IDI‐20141234) projects, and the grant number 730897 under the HPC‐EUROPA3 project supported by Horizon 2020

A Performance-Oriented Monitoring System for Security Properties in Cloud Computing Applications

Este artículo presenta una arquitectura robusta para el monitoreo dinámico de propiedades de seguridad en entornos de computación en la nube. Reconociendo que, a pesar de los avances en medidas preventivas, siempre existe la posibilidad de fallos debido a la complejidad y la interdependencia de múltiples capas de software e infraestructura, este trabajo propone complementarlas con medidas reactivas, como el monitoreo. El enfoque presentado incluye una arquitectura de tres capas diseñada específicamente para escenarios de computación en la nube, un nuevo lenguaje para la expresión de reglas de monitoreo y una estrategia basada en la generación de autómatas de estado finito para mejorar el rendimiento del motor de monitoreo. Además, aborda los retos que las arquitecturas de monitoreo tradicionales no logran gestionar adecuadamente en entornos distribuidos y en la nube, proponiendo soluciones innovadoras. La principal aportación de este trabajo radica en la mejora del rendimiento del monitoreo y la capacidad de respuesta ante incidentes de seguridad en entornos distribuidos, gracias a un enfoque más eficiente y específico para las necesidades de la computación en la nube.Proyecto europeo PASSIV