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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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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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Recognition of food-texture attributes using an in-ear microphone

arXiv

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arXiv 2021年

作者： Mars, Monica Diou, Christos Papapanagiotou, Vasileios van den Boer, Janet Delopoulos, Anastasios Multimedia Understanding Group Dpt. Electrical and Computer Engineering Faculty of Engineering Aristotle University of Thessaloniki Greece Department of Informatics and Telematics Harokopio University of Athens Greece Dpt. of Biomedical Signals and Systems Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands Division of Human Nutrition and Health Wageningen University Netherlands

Food texture is a complex property;various sensory attributes such as perceived crispiness and wetness have been identified as ways to quantify it. Objective and automatic recognition of these attributes has applications in multiple fields, including health sciences and food engineering. In this work we use an in-ear microphone, commonly used for chewing detection, and propose algorithms for recognizing three food-texture attributes, specifically crispiness, wetness (moisture), and chewiness. We use binary SVMs, one for each attribute, and propose two algorithms: one that recognizes each texture attribute at the chew level and one at the chewing-bout level. We evaluate the proposed algorithms using leave-one-subject-out cross-validation on a dataset with 9 subjects. We also evaluate them using leave-one-food-type-out cross-validation, in order to examine the generalization of our approach to new, unknown food types. Our approach performs very well in recognizing crispiness (0.95 weighted accuracy on new subjects and 0.93 on new food types) and demonstrates promising results for objective and automatic recognition of wetness and chewiness. Copyright © 2021, The Authors. All rights reserved.

关键词： Wearable technology

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On partitioning-based optimisations in expressive fuzzy Description Logics

On partitioning-based optimisations in expressive fuzzy Desc...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Fernando Bobillo Umberto Straccia Dpt. of Computer Science and Systems Engineering University of Zaragoza Spain ISTI-CNR Istituto di Scienza e Tecnologie dell' Informazione Pisa Italy

Fuzzy Description Logics (DLs) are a formalism for the representation of structured knowledge affected by imprecision or vagueness. A key factor in the practical success of fuzzy DLs is the availability of highly implemented reasoners. This paper studies two optimisation techniques (ABox partitioning based on individual groups and optimisation problem partitioning) in the setting of the fuzzy ontology reasoner fuzzy DL. We study the applicability of these techniques in expressive fuzzy DL languages, proposing a new strategy, and perform an empirical evaluation proving that they are not helpful in practice so far.

关键词： Optimization Semantics Cognition Fuzzy logic Description logic Standards

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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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Modiàed Kirchhoff diffraction of pulsed EM waves radiated from a slot-excited Fabry-Pérot resonator antenna 12

Modiàed Kirchhoff diffraction of pulsed EM waves radiated f...

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12th European Conference on Antennas and Propagation, EuCAP 2018

作者： Stumpf, Martin Vandenbosch, Guy A.E. Lager, Ioan E. SIX Research Centre Brno University of Technology Technická 3082/12 Brno616 00 Czech Republic Electrical Engineering Dpt. TELEMIC Katholieke Universiteit Leuven Kasteelpark Arenberg 10 bus 2444 LeuvenB-3001 Belgium Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Mekelweg 4 Delft2628 CD Netherlands

ISBN: (纸本)9781785617911

Diffraction of pulsed electromagnetic (EM) waves in a slot excited Fabry Perót type resonator antenna is studied analytically in the modiÀed Kirchhoff approximation. Closed-form space-time expressions for the radiated EM Àelds are found using the Cagniard-DeHoop technique. Illustrative numerical examples are given. © 2018 Institution of engineering and *** Rights Reserved.

关键词： Diffraction

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OPTIMAL LOGIC SYNTHESIS OF COMBINATIONAL CIRCUITS USING K-LUT FPGA

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IFAC Proceedings Volumes 2007年第18期40卷 661-666页

作者： Ion I. Bucur Alexandru E. şuşu University Politehnica of Bucharest Computer Science and Engineering Dpt. bucurb@cs.pub.ro Swiss Federal Institute of Technology Lausanne (EPFL) IC/STI Department alex.susu@epfl.ch

Determining if a given arbitrary, wide function can be implemented by a programmable logic block, is unfortunately, in general, a very difficult problem, called the Boolean matching problem. We introduce a novel implemented algorithm able to map for performance combinational networks using k -LUT based FPGAs. We consider in this paper delay optimum and area optimal k-LUT FPGA mapping algorithms and compare them against a newly developed and recently improved algorithm.

关键词： k-LUT based FPGA algorithms for delay optimal mapping k-feasible cones

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