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arXiv 2019年

作者： Bellekens, Xavier Jayasekara, Gayan Hindy, Hanan Bures, Miroslav Brosset, David Tachtatzis, Christos Atkinson, Robert Abertay University Division of Cyber-Security United Kingdom Naval Cyber Defense Ecole navale France Czech Technical University in Prague Dpt. of Computer Science Faculty of Electrical Engineering Czech Republic University of Strathclyde Dpt. of Electronic & Electrical Engineering United Kingdom

With the ever growing networking capabilities and services offered to users, attack surfaces have been increasing exponentially, additionally, the intricacy of network architectures has increased the complexity of cyber-defenses, to this end, the use of deception has recently been trending both in academia and industry. Deception enables to create proactive defense systems, luring attackers in order to better defend the systems at hand. Current applications of deception, only rely on static, or low interactive environments. In this paper we present a platform that combines human-computer-interaction, analytics, gamification and deception to lure malicious users into selected traps while piquing their interests. Furthermore we analyse the interactive deceptive aspects of the platform through the addition of a narrative, further engaging malicious users into following a predefined path and deflecting attacks from key network systems. Copyright © 2019, The Authors. All rights reserved.

关键词： Cybersecurity

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A Comprehensive Methodology for Assessing the Economic Viability of EV Charging Infrastructure in Brazil

A Comprehensive Methodology for Assessing the Economic Viabi...

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Smart Power & Internet Energy systems (SPIES), International Conference on

作者： Daywes Pinheiro Neto António Paulo Coimbra Aníbal T. De Almeida Tony R. Almeida Pedro Moura Letícia C. Ucker Raphael A. Gomes Alana S. Magalhães Center for Experimental and Technological Research and Studies Federal Institute of Goiás Brazil Dpt. of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Portugal

ISBN: (数字)9798350368864

ISBN: (纸本)9798350368871

This study proposes a comprehensive methodology to assess the economic viability of electric vehicle (EV) charging infrastructure projects in Brazil, specifically targeting connections with a capacity of up to 112.5 kW, which are subject to flat electricity tariffs. The methodology employs a novel EV energy consumption model, integrates the regulatory framework of net metering and relevant tax considerations, explores the potential cost-saving benefits of photovoltaic (PV) system integration, and utilizes key economic indicators like Net Present Value and Discounted Payback Time to quantify project profitability. By accounting for a set of EV demand scenarios (low, moderate, and high), the methodology provides a multifaceted assessment of project viability. A case study demonstrates the methodology's application, highlighting the potential economic benefits of such investments. The results suggest that the minimum charging service fee required for economic viability ranges from 0.17 to 0.29 USD/kWh, with a clear cost advantage associated with PV integration observed across all scenarios.

关键词： Economics Photovoltaic systems Costs Finance Transportation Tariffs System integration Charging stations Electric vehicle charging Sustainable development

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Leveraging protein-protein interactions in phenotype prediction through graph neural networks

Leveraging protein-protein interactions in phenotype predict...

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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： Riccardo Smeriglio Joana Rosell-Mirmi Petia Radeva Jordi Abante Dpt. of Physics Universitat de Barcelona Spain Control and Computer Engineering Dpt. Politecnico di Torino Turin Italy Headache Research Group Vall d’Hebron Institut de Recerca Barcelona Catalonia Spain Dpt. of Mathematics & Computer Science Institute of Neurosciences Universitat de Barcelona Barcelona Catalonia Spain Laboratory of Stem Cells & Regenerative Medicine Creatio Production & Validation Center of Advanced Therapies Dpt. of Biomedical Sciences Institute of Neurosciences Universitat de Barcelona Dpt. of Mathematics & Computer Science Universitat de Barcelona August Pi i Sunyer Biomedical Research Institute (IDIBAPS) Barcelona Catalonia Spain

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

Current genotype-to-phenotype models, such as polygenic risk scores, only account for linear relationships between genotype and phenotype and ignore epistatic interactions, limiting the complexity of the diseases that can be properly characterized. Protein-protein interaction networks have the potential to improve the performance of the models. Moreover, interactions at the protein level can have profound implications in understanding the genetic etiology of diseases and, in turn, for drug development. In this article, we propose a novel approach for phenotype prediction based on graph neural networks (GNNs) that naturally incorporates existing protein interaction networks into the model. As a result, our approach can naturally discover relevant epistatic interactions. We assess the potential of this approach using simulations and comparing it to linear and other non-linear approaches. We also study the performance of the proposed GNN-based methods in predicting Alzheimer’s disease, one of the most complex neurodegenerative diseases, where our GNN approach outperform state of the art methods. In addition, we show that our proposal is able to discover critical interactions in the Alzheimer’s disease. Our findings highlight the potential of GNNs in predicting phenotypes and discovering the underlying mechanisms of complex diseases.

关键词： Proteins Training Phenotypes Limiting Prevention and mitigation Predictive models Graph neural networks Proposals Alzheimer's disease Genotypes

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Interval Estimation of the Herfindahl-Hirschman Index under Incomplete Market Information

Interval Estimation of the Herfindahl-Hirschman Index under ...

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International Conference on computer Modeling and Simulation, UKSIM

作者： Maurizio Naldi Marta Flamini Dpt. of Computer Science and Civil Engineering Università di Roma Tor Vergata Roma Italy Faculty of Engineering Università Telematica Internazionale UNINETTUNO Roma Italy

An interval estimate is provided for the Herfindahl-Hirschman Index (HHI) when the knowledge about the market is incomplete, and we know just the largest market shares. Two rigorous bounds are provided for the HHI, without any further assumptions. Though the interval gets wider as the sum of the known market shares gets smaller, the estimate proves to be quite tight even when the fraction of the market that we do not know in detail is as high as 30%. This robustness is shown through three examples, considering respectively a set of real data and two sets of synthetic data, with the company sizes (a proxy for market shares) following respectively a Zipf law and a Pareto distribution.

关键词： Companies Upper bound Indexes Context Computational modeling Mobile communication

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Model for the organization, storage and processing of large data banks of physiological variables

Model for the organization, storage and processing of large ...

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International Conference on computers Communications and Control (ICCCC)

作者： Fredi E. Palominos Hernán Diaz Felisa M. Córdova A. Lucio Canete Claudia A. Durán Dpt. Mathematics and Computer Science University of Santiago de Chile Santiago Chile Dpt. Chemistry and Biology University of Santiago de Chile Santiago Chile School of Industrial Engineering University Finis Terrae Santiago Chile Universidad de Santiago de Chile Santiago de Chile CL Dpt. of Industrial Engineering University of Santiago de Chile Santiago Chile

ISBN: (纸本)9781509017362

The proliferation and popularization of new instruments for measuring different types of electrophysiological variables, has generated the need to store huge volumes of information, corresponding to the records obtained by applying this instruments on experimental subjects. Together with this must be added the data derived from the analysis and purification processes. Moreover, several stages involved in the processing of data, is associated with one or more specific methods related to the area of research and to the treatment at which the base information (RAW) is subjected. As a result of this and with the passage of time, various problems occur, which are the most obvious consequence of that data and metadata derived from the treatment processes and analysis, and can end up accumulating and requiring more storage space than the base data. In addition, the enormous amount of information, as it increases over time, can lead to the loss of the link between the processed data, the methods of treatment used, and the analysis performed, so that eventually all becomes simply a huge repository of biometric data, devoid of meaning and sense. Current approaches around the concept of big data, while take over the storage and other aspects such as information search mechanisms, are far from incorporating metadata about the neurophysiological and emotional records. This type of information requires the construction of chronologies of events, including the methods of processing and analysis. In addition, it is required to maintain an adequate link between those responsible for the data (those who recorded and analyzed) and subjects that are under investigation, without breaking confidentiality to which they are entitled. This paper presents an approach founded in a data model that can adequately handle different types of chronologies of physiological and emotional information, ensuring confidentiality of information according to the experimental protocols and relevant ethical

关键词： Electroencephalography Big data Instruments Metadata computers Cleaning

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BAYESIAN CLASSIFICATION AND ACTIVE LEARNING USING l_p-PRIORS. APPLICATION TO IMAGE SEGMENTATION

BAYESIAN CLASSIFICATION AND ACTIVE LEARNING USING l_p-PRIORS...

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European Signal Processing Conference

作者： Pablo Ruiz Nicolas Perez de la Blanca Rafael Molina Aggelos K. Katsaggelos Dept. Ciencias de la Computacion e I.A. Universidad de Granada Dpt. of Electrical Engineering and Computer Science Northwestern University

ISBN: (纸本)9781479946037

In this paper we utilize Bayesian modeling and inference to learn a softmax classification model which performs Supervised Classification and Active Learning. For p < 1, l_p-priors are used to impose sparsity on the adaptive parameters. Using variational inference, all model parameters are estimated and the posterior probabilities of the classes given the samples are calculated. A relationship between the prior model used and the independent Gaussian prior model is provided. The posterior probabilities are used to classify new samples and to define two Active Learning methods to improve classifier performance: Minimum Probability and Maximum Entropy. In the experimental section the proposed Bayesian framework is applied to Image Segmentation problems on both synthetic and real datasets, showing higher accuracy than state-of-the-art approaches.

关键词： Bayes methods Training Support vector machines Adaptation models Image segmentation Vectors Entropy

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Analysis of classifier training on synthetic data for cross-domain datasets

arXiv

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arXiv 2024年

作者： Cortés, Andoni Rodríguez, Clemente Vélez, Gorka Barandiarán, Javier Nieto, Marcos Deparment of Intelligent Transportation Systems and Engineering Deparment Vicomtech Paseo Mikeletegi 57 San Sebastian20009 Spain Dpt. of Computer Architecture San Sebastian Spain

A major challenges of deep learning (DL) is the necessity to collect huge amounts of training data. Often, the lack of a sufficiently large dataset discourages the use of DL in certain applications. Typically, acquiring the required amounts of data costs considerable time, material and effort. To mitigate this problem, the use of synthetic images combined with real data is a popular approach, widely adopted in the scientific community to effectively train various detectors. In this study, we examined the potential of synthetic data-based training in the field of intelligent transportation systems. Our focus is on camera-based traffic sign recognition applications for advanced driver assistance systems and autonomous driving. The proposed augmentation pipeline of synthetic datasets includes novel augmentation processes such as structured shadows and gaussian specular highlights. A well-known DL model was trained with different datasets to compare the performance of synthetic and real image-based trained models. Additionally, a new, detailed method to objectively compare these models is proposed. Synthetic images are generated using a semi-supervised errors-guide method which is also described. Our experiments showed that a synthetic image-based approach outperforms in most cases real image-based training when applied to cross-domain test datasets (+10% precision for GTSRB dataset) and consequently, the generalization of the model is improved decreasing the cost of acquiring images. © 2024, CC BY-NC-ND.

关键词： Advanced driver assistance systems

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A letter on convergence of in-parameter-linear nonlinear neural architectures with gradient learnings

arXiv

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arXiv 2021年

作者： Bukovsky, Ivo Dohnal, Gejza Benes, Peter M. Ichiji, Kei Homma, Noriyasu The Dpt. of Computer Science Faculty of Science University of South Bohemia Ceske Budejovice Czech Republic The Dpt. of Mechaniccs Biomechanics and Mechatronics Faculty of Mechanical Engineering Czech Technical University in Prague Czech Republic The Dpt. of Technical Mathematics Faculty of Mechanical Engineering Technical University Prague Czech Republic The Tohoku University Graduate School of Medicine 2-1 Seriyo-machi Aoba-ku Sendai980-8575 Japan

This letter summarizes and proves the concept of bounded-input bounded-state (BIBS) stability for weight convergence of a broad family of in-parameter-linear nonlinear neural architectures as it generally applies to a broad family of incremental gradient learning algorithms. A practical BIBS convergence condition results from the derived proofs for every individual learning point or batches for real-time applications. © 2021, CC BY-NC-ND.

关键词： Network architecture

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Analytical and Experimental IP Encapsulation Efficiency Comparison of GSE, MPE, and ULE over DVB-S2

Analytical and Experimental IP Encapsulation Efficiency Comp...

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International Workshop on Satellite and Space Communications (IWSSC)

作者： A. Mayer B. Collini-Nocker F. Vieira J. Lei M.A. Vazquez Castro Multimedia Comm. Group Dpt. of Computer Sciences University of Salzburg Salzburg Austria Department Telecommunications and Systems Engineering Universitat Autònoma Barcelona Barcelona Spain

Transmitting variable-length network layer (IP) packets over satellite links with fixed frame lengths (or lengths depending on the ACM transmission mode) requires (IP) encapsulation. For DVB-S links with fixed-size 188-byte TS packets, MPE and ULE encapsulations are available. DVB-S2 provides a compatibility mode to pack TS packets into the longer base band frames (BBFrames). Thus MPE and ULE are also available for S2, as well as a native generic stream encapsulation (GSE), avoiding the double overhead of TS and BBFrame encapsulation. The present paper gives a short overview on the available encapsulation protocols for DVB-S2, and then provides a mathematical efficiency calculation model for these encapsulations, in order to allow for performing theoretical efficiency simulations. Comparison graphs of the efficiency values are presented, using both the efficiency models and measurements from real satellite traffic.

关键词： Encapsulation Digital video broadcasting Protocols Satellite broadcasting Traffic control Modeling Telecommunication computing systems engineering and theory Artificial satellites TV broadcasting

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Geographic information for vision-based road detection

Geographic information for vision-based road detection

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IEEE Symposium on Intelligent Vehicle

作者： José M. Alvarez Felipe Lumbreras Theo Gevers Antonio M. López Computer Vision Center and Computer Science Dpt. Univ. Autonoma de Barcelona Spain Intelligent Systems Lab Amsterdam University of Amsterdam and Comp. Vision Center Barcelona Spain

Road detection is a vital task for the development of autonomous vehicles. The knowledge of the free road surface ahead of the target vehicle can be used for autonomous driving, road departure warning, as well as to support advanced driver assistance systems like vehicle or pedestrian detection. Using vision to detect the road has several advantages in front of other sensors: richness of features, easy integration, low cost or low power consumption. Common vision-based road detection approaches use low-level features (such as color or texture) as visual cues to group pixels exhibiting similar properties. However, it is difficult to foresee a perfect clustering algorithm since roads are in outdoor scenarios being imaged from a mobile platform. In this paper, we propose a novel high-level approach to vision-based road detection based on geographical information. The key idea of the algorithm is exploiting geographical information to provide a rough detection of the road. Then, this segmentation is refined at low-level using color information to provide the final result. The results presented show the validity of our approach.

关键词： Vehicle detection Remotely operated vehicles Road vehicles Mobile robots Vehicle driving Clustering algorithms Sensor phenomena and characterization Costs Energy consumption Image segmentation

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