Computational cost of GNG3D algorithm for mesh simplification

In this paper we present a study of the computational cost of the GNG3D algorithm for mesh optimization. This algorithm has been implemented taking as a basis a new method which is based on neural networks and consists on two differentiated phases: an optimization phase and a reconstruction phase. The optimization phase is developed applying an optimization algorithm based on the Growing Neural Gas model, which constitutes an unsupervised incremental clustering algorithm. The primary goal of this phase is to obtain a simplified set of vertices representing the best approximation of the original 3D object. In the reconstruction phase we use the information provided by the optimization algorithm to reconstruct the faces thus obtaining the optimized mesh. The computational cost of both phases is calculated, showing some examples

Criptosistemas de clave pública basados en acciones del anillo Ep(m)

El objetivo de este trabajo es la introducción de aplicaciones criptográficas de una extensión del anillo End(ZpxZp2 ), denotado por Ep(m). Mostramos cómo las acciones del anillo Ep(m) sobre dos conjuntos distintos nos permiten introducir dos criptosistemas de clave pública diferentes y basados en la dificultad de resolver los problemas de la acción del semigrupo y de la descomposición respectivamente. Observamos cómo la no conmutatividad del anillo, así como la existencia de un gran número de divisores de cero lo hacen apropiado para tales aplicaciones criptográficas.El primer autor ha sido parcialmente financiado por el proyecto MTM2011-24858 del Ministerio de Economía y Competitividad del Gobierno de España. El segundo autor está financiado por el grupo de investigación de la Junta de Andalucía FQM 211

Ranking places in attributed temporal urban mobility networks

Drawing on the recent advances in complex network theory, urban mobility flow patterns, typically encoded as origin-destination (OD) matrices, can be represented as weighted directed graphs, with nodes denoting city locations and weighted edges the number of trips between them. Such a graph can further be augmented by node attributes denoting the various socio-economic characteristics at a particular location in the city. In this paper, we study the spatio-temporal characteristics of “hotspots” of different types of socio-economic activities as characterized by recently developed attribute-augmented network centrality measures within the urban OD network. The workflow of the proposed paper comprises the construction of temporal OD networks using two custom data sets on urban mobility in Rome and London, the addition of socio-economic activity attributes to the OD network nodes, the computation of network centrality measures, the identification of “hotspots” and, finally, the visualization and analysis of measures of their spatio-temporal heterogeneity. Our results show structural similarities and distinctions between the spatial patterns of different types of human activity in the two cities. Our approach produces simple indicators thus opening up opportunities for practitioners to develop tools for real-time monitoring and visualization of interactions between mobility and economic activity in cities.This work is supported by the Spanish Government, Ministerio de Economía y Competividad, grant number TIN2017-84821-P. It is also funded by the EU H2020 programme under Grant Agreement No. 780754, “Track & Know”

An optimized pseudorandom generator using packed matrices

Most cryptographic services and information security protocols require a dependable source of random data; pseudorandom generators are convenient and efficient for this application working as one of the basic foundation blocks on which to build the required security infrastructure. We propose a modification of a previously published matricial pseudorandom generator that significantly improves performance and security by using word packed matrices and modifying key scheduling and bit extraction schemes. The resulting generator is then successfully compared to world class standards.This research was partially supported by the Spanish grant GV06/018

An algorithm to hide information in binary images

The objective of this paper is to develop a method to hide information inside a binary image. An algorithm to embed data in scanned text or figures is proposed, based on the detection of suitable pixels, which verify some conditions in order to be not detected. In broad terms, the algorithm locates those pixels placed at the contours of the figures or in those areas where some scattering of the two colors can be found. The hidden information is independent from the values of the pixels where this information is embedded. Notice that, depending on the sequence of bits to be hidden, around half of the used pixels to keep bits of data will not be modified. The other basic characteristic of the proposed scheme is that it is necessary to take into consideration the bits that are modified, in order to perform the recovering process of the information, which consists on recovering the sequence of bits placed in the proper positions. An application to banking sector is proposed for hidding some information in signatures

Deep learning model of convolutional neural networks powered by a genetic algorithm for prevention of traffic accidents severity

The World Health Organization highlights that the number of annual road traffic deaths has reached 1.35 million (Global Status Report on Road Safety 2018). In addition, million of people suffer more or less important injuries as a consequence of this type of accidents. In this scenario, the prediction of the severity of traffic accidents is an essential point when it comes to improving the prevention and reaction of the entities responsible. On the other hand, the development of reliable methodologies to predict and classify the level of severity of traffic accidents, based on various variables, is a key component in the field of research in road safety. This work aims to propose a new approach, based on convolutional neural networks, for the detection of the severity of traffic accidents. Behind this objective is the preprocessing, analysis and visualization of data as well as the design, implementation and comparison of machine learning models considering accuracy as a performance indicator. For this purpose, a scalable and easily reusable methodology has been implemented. This methodology has been compared with other deep learning models verifying that the results of the designed neural network offer better performance in terms of quality measures.Financial support provided under grant number PID2020-112827GB-I00 funded by MCIN/AEI/10.13039/501100011033

Combining the Two-Layers PageRank Approach with the APA Centrality in Networks with Data

[EN] Identifying the influential nodes in complex networks is a fundamental and practical topic at the moment. In this paper, a new centrality measure for complex networks is proposed based on two contrasting models that have their common origin in the well-known PageRank centrality. On the one hand, the essence of the model proposed is taken from the Adapted PageRank Algorithm (APA) centrality, whose main characteristic is that constitutes a measure to establish a ranking of nodes considering the importance of some dataset associated to the network. On the other hand, a technique known as two-layers PageRank approach is applied to this model. This technique focuses on the idea that the PageRank centrality can be understood as a two-layer network, the topological and teleportation layers, respectively. The main point of the proposed centrality is that it combines the APA centrality with the idea of two-layers; however, the difference now is that the teleportation layer is replaced by a layer that collects the data present in the network. This combination gives rise to a new algorithm for ranking the nodes according to their importance. Subsequently, the coherence of the new measure is demonstrated by calculating the correlation and the quantitative differences of both centralities (APA and the new centrality). A detailed study of the differences of both centralities, taking different types of networks, is performed. A real urban network with data randomly generated is evaluated as well as the well-known Zachary's karate club network. Some numerical results are carried out by varying the values of the alpha parameter-known as dumping factor in PageRank model-that varies the importance given to the two layers (topology and data) within the computation of the new centrality. The proposed algorithm takes the best characteristics of the models on which it is based: on the one hand, it is a measure of centrality, in complex networks with data, whose calculation is stable numerically and, on the other hand, it is able to separate the topological properties of the network and the influence of the data.Partially supported by the Spanish Government, Ministerio de Economia y Competividad, grant number TIN2017-84821-P.Agryzkov, T.; Pedroche Sánchez, F.; Tortosa, L.; Vicent, JF. (2018). Combining the Two-Layers PageRank Approach with the APA Centrality in Networks with Data. ISPRS International Journal of Geo-Information. 7(12):1-22. https://doi.org/10.3390/ijgi7120480S122712Crucitti, P., Latora, V., & Porta, S. (2006). Centrality measures in spatial networks of urban streets. Physical Review E, 73(3). doi:10.1103/physreve.73.036125Bonacich, P. (1991). Simultaneous group and individual centralities. 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Quantifying the influence of scientists and their publications: distinguishing between prestige and popularity. New Journal of Physics, 14(3), 033033. doi:10.1088/1367-2630/14/3/033033Porta, S., Crucitti, P., & Latora, V. (2006). The network analysis of urban streets: A dual approach. Physica A: Statistical Mechanics and its Applications, 369(2), 853-866. doi:10.1016/j.physa.2005.12.063Jiang, B. (2009). Ranking spaces for predicting human movement in an urban environment. International Journal of Geographical Information Science, 23(7), 823-837. doi:10.1080/13658810802022822Bonacich, P. (1987). Power and Centrality: A Family of Measures. American Journal of Sociology, 92(5), 1170-1182. doi:10.1086/228631Boldi, P., & Vigna, S. (2014). Axioms for Centrality. Internet Mathematics, 10(3-4), 222-262. doi:10.1080/15427951.2013.865686Freeman, L. C. (1977). A Set of Measures of Centrality Based on Betweenness. Sociometry, 40(1), 35. doi:10.2307/3033543Brandes, U. (2001). A faster algorithm for betweenness centrality*. The Journal of Mathematical Sociology, 25(2), 163-177. doi:10.1080/0022250x.2001.9990249Haveliwala, T. H. (2003). Topic-sensitive pagerank: A context-sensitive ranking algorithm for web search. IEEE Transactions on Knowledge and Data Engineering, 15(4), 784-796. doi:10.1109/tkde.2003.1208999Berkhin, P. (2005). A Survey on PageRank Computing. Internet Mathematics, 2(1), 73-120. doi:10.1080/15427951.2005.10129098García, E., Pedroche, F., & Romance, M. (2013). On the localization of the personalized PageRank of complex networks. Linear Algebra and its Applications, 439(3), 640-652. doi:10.1016/j.laa.2012.10.051Langville, A., & Meyer, C. (2004). Deeper Inside PageRank. Internet Mathematics, 1(3), 335-380. doi:10.1080/15427951.2004.10129091Bianchini, M., Gori, M., & Scarselli, F. (2005). Inside PageRank. ACM Transactions on Internet Technology, 5(1), 92-128. doi:10.1145/1052934.1052938Migallón, H., Migallón, V., Palomino, J. A., & Penadés, J. (2018). A heuristic relaxed extrapolated algorithm for accelerating PageRank. Advances in Engineering Software, 120, 88-95. doi:10.1016/j.advengsoft.2016.01.024Agryzkov, T., Oliver, J. L., Tortosa, L., & Vicent, J. F. (2012). An algorithm for ranking the nodes of an urban network based on the concept of PageRank vector. Applied Mathematics and Computation, 219(4), 2186-2193. doi:10.1016/j.amc.2012.08.064Agryzkov, T., Tortosa, L., & Vicent, J. F. (2016). New highlights and a new centrality measure based on the Adapted PageRank Algorithm for urban networks. Applied Mathematics and Computation, 291, 14-29. doi:10.1016/j.amc.2016.06.036Agryzkov, T., Tortosa, L., Vicent, J. F., & Wilson, R. (2017). A centrality measure for urban networks based on the eigenvector centrality concept. Environment and Planning B: Urban Analytics and City Science, 46(4), 668-689. doi:10.1177/2399808317724444Conti, M., & Kumar, M. (2010). Opportunities in Opportunistic Computing. Computer, 43(1), 42-50. doi:10.1109/mc.2010.19Zhang, Y., Song, L., Jiang, C., Tran, N. H., Dawy, Z., & Han, Z. (2017). A Social-Aware Framework for Efficient Information Dissemination in Wireless Ad Hoc Networks. IEEE Communications Magazine, 55(1), 174-179. doi:10.1109/mcom.2017.1600029cmBoccaletti, S., Bianconi, G., Criado, R., del Genio, C. I., Gómez-Gardeñes, J., Romance, M., … Zanin, M. (2014). The structure and dynamics of multilayer networks. Physics Reports, 544(1), 1-122. doi:10.1016/j.physrep.2014.07.001Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203-271. doi:10.1093/comnet/cnu016Pedroche, F., Romance, M., & Criado, R. (2016). A biplex approach to PageRank centrality: From classic to multiplex networks. Chaos: An Interdisciplinary Journal of Nonlinear Science, 26(6), 065301. doi:10.1063/1.4952955Zachary, W. W. (1977). 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Optimizing matrix operations in Z2 by word packing

AbstractWe propose a new storage scheme (word packing) for matrices with elements in Z2 that enables improved performance. This scheme is based on utilizing the full register length of modern microprocessors to perform multiple Z2 operations in parallel. We analyze several operations over word packed matrices and compare them with their conventional equivalents

Explainability techniques applied to road traffic forecasting using Graph Neural Network models

In recent years, several new Artificial Intelligence methods have been developed to make models more explainable and interpretable. The techniques essentially deal with the implementation of transparency and traceability of black box machine learning methods. Black box refers to the inability to explain why the model turns the input into the output, which may be problematic in some fields. To overcome this problem, our approach provides a comprehensive combination of predictive and explainability techniques. Firstly, we compared statistical regression, classic machine learning and deep learning models, reaching the conclusion that models based on deep learning exhibit greater accuracy. Of the great variety of deep learning models, the best predictive model in spatio-temporal traffic datasets was found to be the Adaptive Graph Convolutional Recurrent Network. Regarding the explainability technique, GraphMask shows a notably higher fidelity metric than other methods. The integration of both techniques was tested by means of experimental results, concluding that our approach improves deep learning model accuracy, making such models more transparent and interpretable. It allows us to discard up to 95% of the nodes used, facilitating an analysis of its behavior and thus improving the understanding of the model

Key agreement protocols for distributed secure multicast over the ring Ep(m)

Protocols for authenticated key exchange allow parties within an insecure network to establish a common session key which can then be used to secure their future communication. In this paper we introduce a protocol for distributed key agreement over a noncommutative ring with a large number of noninvertible elements. This protocol uses polynomials with coefficients in the center of the ring. We also present the necessary steps for recalculating the shared secret key when a new user joins the system, or when a user leaves the system.The work of the first author was partially supported by Spanish grant MTM2011-24858 of the Ministerio de Economía y Competitividad of the Gobierno de España. The work of the second author was partially supported by the grant FQM 0211 of the Junta de Andalucía