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European Signal Processing Conference

作者： Emre Besler Pablo Ruiz Rafael Molina Aggelos K. Katsaggelos Dpt. of Electrical Engineering and Computer Science. Northwestern University Dpto. de Ciencias de la Computación e I.A. Universidad de Granada

ISBN: (纸本)9781509018918

In this paper we address supervised learning problems where, instead of having a single annotator who provides the ground truth, multiple annotators, usually with varying degrees of expertise, provide conflicting labels for the same sample. Once Gaussian Process classification has been adapted to this problem we propose and describe how Variational Bayes inference can be used to, given the observed labels, approximate the posterior distribution of the latent classifier and also estimate each annotator's reliability. In the experimental section, we evaluate the proposed method on both generated synthetic and real data, and compare it with state of the art crowdsourcing methods.

关键词： Crowdsourcing Gaussian processes Solid modeling Europe Signal processing Supervised learning Bayes methods Semi-supervised Learning entity modeling Gaussian processes Bayes methods signal processing Crowdsource detection zone Gauss

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BACH: grand challenge on breast cancer histology images

arXiv

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arXiv 2018年

作者： Aresta, Guilherme Araújo, Teresa Kwok, Scotty Chennamsetty, Sai Saketh Safwan, Mohammed Alex, Varghese Marami, Bahram Prastawa, Marcel Chan, Monica Donovan, Michael Fernandez, Gerardo Zeineh, Jack Kohl, Matthias Walz, Christoph Ludwig, Florian Braunewell, Stefan Baust, Maximilian Vu, Quoc Dang Nhat To, Minh Nguyen Kim, Eal Kwak, Jin Tae Galal, Sameh Sanchez-Freire, Veronica Brancati, Nadia Frucci, Maria Riccio, Daniel Wang, Yaqi Sun, Lingling Ma, Kaiqiang Fang, Jiannan Kone, Ismael Boulmane, Lahsen Campilho, Aurélio Eloy, Catarina Polónia, António Aguiar, Paulo INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto4200-465 Portugal Faculty of Engineering of University of Porto Porto4200-465 Portugal Seek AI Limited Hong Kong Bangalore India Gurgaon India Chennai India Center for Computational and Systems Pathology Dpt. of Pathology Icahn School of Medicine at Mount Sinai Mount Sinai Hospital New York United States Konica Minolta Laboratory Europe Munich Germany Institute of Pathology Faculty of Medicine LMU Munich Munich Germany Department of Computer Science and Engineering Sejong University Seoul05006 Korea Republic of Chicago IL United States Naples Italy University of Naples "Federico II" Naples Italy Key Laboratory of RF Circuits and Systems Ministry of Education Hangzhou Dianzi University Hangzhou310018 China Zhejiang Provincial Laboratory of Integrated Circuits Design Hangzhou Dianzi University Hangzhou310018 China p2MIA Research Group LEM2A Lab Faculté des Sciences Université Moulay Ismail Meknes Morocco qLaboratório de Anatomia Patológica Ipatimup Diagnósticos Rua Júilio Amaral de Carvalho 45 Porto4200-135 Portugal Faculdade de Medicina Universidade do Porto Alameda Prof Hernâni Monteiro Porto4200-319 Portugal Universidade do Porto Rua Alfredo Allen 208 Porto4200-135 Portugal Universidade do Porto Rua Alfredo Allen 208 Porto4200-135 Portugal

Breast cancer is the most common invasive cancer in women, affecting more than 10% of women worldwide. Microscopic analysis of a biopsy remains one of the most important methods to diagnose the type of breast cancer. This requires specialized analysis by pathologists, in a task that i) is highly time- and cost-consuming and ii) often leads to nonconsensual results. The relevance and potential of automatic classification algorithms using hematoxylin-eosin stained histopathological images has already been demonstrated, but the reported results are still sub-optimal for clinical use. With the goal of advancing the state-of-the-art in automatic classification, the Grand Challenge on BreAst Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018). BACH aimed at the classification and localization of clinically relevant histopathological classes in microscopy and whole-slide images from a large annotated dataset, specifically compiled and made publicly available for the challenge. Following a positive response from the scientific community, a total of 64 submissions, out of 677 registrations, effectively entered the competition. The submitted algorithms improved the state-of-the-art in automatic classification of breast cancer with microscopy images to an accuracy of 87%. Convolutional neuronal networks were the most successful methodology in the BACH challenge. Detailed analysis of the collective results allowed the identification of remaining challenges in the field and recommendations for future developments. The BACH dataset remains publicly available as to promote further improvements to the field of automatic classification in digital pathology. Copyright © 2018, The Authors. All rights reserved.

关键词： Neurons

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Camera calibration method for an automotive safety driving system

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Journal of Institute of Control, Robotics and Systems 2015年第7期21卷 621-626页

作者： Park, Jong-Seop Kim, Gi-Seok Roh, Soo-Jang Cho, Jae-Soo Dpt. of Computer Science and Engineering Korea University of Technology and Education Korea Republic of

This paper presents a camera calibration method in order to estimate the lane detection and inter-vehicle distance estimation system for an automotive safety driving system. In order to implement the lane detection and vision-based inter-vehicle distance estimation to the embedded navigations or black box systems, it is necessary to consider the computation time and algorithm complexity. The process of camera calibration estimates the horizon, the position of the car's hood and the lane width for extraction of region of interest (ROI) from input image sequences. The precision of the calibration method is very important to the lane detection and inter-vehicle distance estimation. The proposed calibration method consists of three main steps: 1) horizon area determination;2) estimation of the car's hood area;and 3) estimation of initial lane width. Various experimental results show the effectiveness of the proposed method. © ICROS 2015.

关键词： Vehicle safety

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Mesh Generation for Atmospheric Boundary Layer Simulation in Wind Farm Design and Management

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Procedia engineering 2015年 124卷 239-251页

作者： A. Gargallo-Peiró M. Avila H. Owen L. Prieto A. Folch Dpt. Computer Applications in Science and Engineering Barcelona Supercomputing Center C/ Gran Capitán 2-4 08034 Barcelona Spain Energy Resource Dpt. Iberdrola Renovables Energía C/ Tomás Redondo 1 28033 Madrid Spain

We propose a new automatic procedure to generate hybrid meshes to simulate turbulent flows for wind farm design and management. In particular, numerical modeling of wind farms involves a RANS simulation of the Atmospheric Boundary Layer (ABL) flow and modeling the turbines using the actuator disc theory. Therefore, the generated meshes have to fulfill several geometrical requirements to answer to the different modeling features. They must: capture the topography features that can influence the wind flow; have a boundary layer close to the terrain to resolve the ABL; be conformally adapted to the disc (turbine) to simulate the turbine effects on the wind flow; be adapted around the disc to capture the wake effect; and present a smooth mesh size transition around the disc to conform the mesh size of the ABL mesh, in which the turbines are immersed. We highlight that the mesh generation procedure is fully automatic once given the mesh size, a topography description, the wind inflow direction, a list of turbine insertion points, and the turbine models. To illustrate the applicability of the proposed procedure, we present two wind farm cases: an offshore wind farm mesh where we illustrate the computed wind deficit, and an onshore farm assimilating real topography data.

关键词： hybrid meshes hexahedral meshes quadrilateral meshes mesh optimization wind farm wind turbine atmospheric boundary layer

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Bitcoin Message: Data Insertion on a Proof-of-Work Cryptocurrency System

Bitcoin Message: Data Insertion on a Proof-of-Work Cryptocur...

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International Conference on Cyberworlds

作者： Matthew D. Sleiman Adrian P. Lauf Roman Yampolskiy Dpt. of Computer Engineering and Computer Science University of Louisville Louisville Kentucky USA

The Bit coin currency is steadily growing in popularity as an alternative to physical currencies. This paper presents an approach for utilizing the Bit coin system for creating permanent messages that are located on computers worldwide. By manipulating the amount field of Bit coin transactions, messages can be embedded into the Bit coin block chain. This approach was implemented by extending the Bit coin-Qt v0.7.2 application and the source code is freely available.

关键词： Online banking Encoding Peer-to-peer computing Cryptography computers Internet Decoding

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A Probability Model for the Size of Investment Projects

A Probability Model for the Size of Investment Projects

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computer Modeling and Simulation, EMS, UKSIM European Symposium on FLAGGED

作者： Maurizio Naldi Dpt. of Computer Science and Civil Engineering Università di Roma Tor Vergata Rome Italy

ISBN: (纸本)9781509002078

Project size, as measured by the amount of investment required, is a relevant parameter to be used in project selection. The evaluation of a project portfolio must consider the variety of project sizes that may be met, so that a proper model should be adopted to describe that variety, especially for its use in simulation. In this paper, a log-normal probability model is suggested to describe the dispersion of project sizes within a project portfolio. The model is obtained on the basis of two real datasets spanning over ten years of observations, and after comparison with competing Gamma and Pareto models. The parameters of the log-normal model are provided as resulting from the best-fit procedure, and indications are also given for the values to use in a simulation study.

关键词： Portfolios Computational modeling Predictive models Data models Investment Probability density function Companies

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Semantic social analytics and linked open data cloud

Semantic social analytics and linked open data cloud

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International Workshop on Semantic Media Adaptation and Personalization (SMAP)

作者： Gerasimos Razis Ioannis Anagnostopoulos Michalis Vafopoulos Computer Science and Biomedical Informatics Dpt. University of Thessaly Lamia Greece NCSR “Demokritos” Software and Knowledge Engineering Laboratory Athens Greece

ISBN: (纸本)9781467383967

In this paper, we propose an ontology schema towards linking semantified Twitter social analytics with the Linked Open Data cloud. The ontology is deployed over a publicly available service that measures how influential a Twitter account is by combining its social activity in Twitter. According to our knowledge this is the first work that combines social analytics with the Linked Open Data (LOD) cloud.

关键词： Twitter Ontologies Resource description framework Measurement Semantics Tagging

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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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Variational Gaussian process for sensor fusion

Variational Gaussian process for sensor fusion

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European Signal Processing Conference (EUSIPCO)

作者： Neda Rohani Pablo Ruiz Emre Besler Rafael Molina Aggelos K. Katsaggelos Dpt. of Electrical Engineering and Computer Science Northwestern University USA Dpto. de Ciencias de la Computación e I.A Universidad de Granada Spain

In this paper, we introduce a new Gaussian Process (GP) classification method for multisensory data. The proposed approach can deal with noisy and missing data. It is also capable of estimating the contribution of each sensor towards the classification task. We use Bayesian modeling to build a GP-based classifier which combines the information provided by all sensors and approximates the posterior distribution of the GP using variational Bayesian inference. During its training phase, the algorithm estimates each sensor's weight and then uses this information to assign a label to each new sample. In the experimental section, we evaluate the classiication performance of the proposed method on both synthetic and real data and show its applicability to different scenarios.

关键词： Robot sensing systems Signal processing algorithms Bayes methods Training Europe Signal processing Gaussian processes

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A MILP-based decision procedure for the (Fuzzy) description logic ALCB 27

A MILP-based decision procedure for the (Fuzzy) description ...

引用

27th International Workshop on Description Logics, DL 2014

作者： Bobillo, Fernando Straccia, Umberto Dpt. of Computer Science and Systems Engineering University of Zaragoza Spain Pisa Italy

To overcome the inability of Description Logics (DLs) to represent vague or imprecise information, several fuzzy extensions have been proposed in the literature. In this context, an important family of reasoning algorithms for fuzzy DLs is based on a combination of tableau algorithms and Operational Research (OR) problems, specifically using Mixed Integer Linear Programming (MILP). In this paper, we present a MILP-based tableau procedure that allows to reason within fuzzy ALCB, i.e., ALC with individual value restrictions. Interestingly, unlike classical tableau procedures, our tableau algorithm is deterministic, in the sense that it defers the inherent non-determinism in ALCB to a MILP solver.

关键词： Data description

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