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12th Annual Conference on Health Informatics Meets eHealth, eHealth 2018

作者： Alghamdi, Wajdi Stamate, Daniel Stahl, Daniel Zamyatin, Alexander Murray, Robin Di Forti, Marta Data Science and Soft Computing Lab Department of Computing Goldsmiths University of London United Kingdom Department of Biostatistics and Health Informatics Institute of Psychiatry Psychology and Neuroscience King's College London United Kingdom Faculty of Informatics Department of Applied Informatics National Research Tomsk State University Russia Department of Psychosis Studies Institute of Psychiatry Psychology and Neuroscience King's College London United Kingdom MRC Social Genetic and Developmental Psychiatry Centre Institute of Psychiatry Psychology and Neuroscience King's College London United Kingdom

ISBN: (纸本)9781614998570

Lately, several studies started to investigate the existence of links between cannabis use and psychotic disorders. This work proposes a refined Machine Learning framework for understanding the links between cannabis use and 1st episode psychosis. The novel framework concerns extracting predictive patterns from clinical data using optimised and post-processed models based on Gaussian Processes, Support Vector Machines, and Neural Networks algorithms. The cannabis use attributes' predictive power is investigated, and we demonstrate statistically and with ROC analysis that their presence in the dataset enhances the prediction performance of the models with respect to models built on data without these specific attributes. © 2018 The authors and IOS Press.

关键词： eHealth First-episode psychosis Gaussian processes Machine learning Neural networks Support vector machine

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Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution

arXiv

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arXiv 2019年

作者： Pretto, Alberto Aravecchia, Stéphanie Burgard, Wolfram Chebrolu, Nived Dornhege, Christian Falck, Tillmann Fleckenstein, Freya Fontenla, Alessandra Imperoli, Marco Khanna, Raghav Liebisch, Frank Lottes, Philipp Milioto, Andres Nardi, Daniele Nardi, Sandro Pfeifer, Johannes Popović, Marija Potena, Ciro Pradalier, Cédric Rothacker-Feder, Elisa Sa, Inkyu Schaefer, Alexander Siegwart, Roland Stachniss, Cyrill Walter, Achim Winterhalter, Wera Wu, Xiaolong Nieto, Juan Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy International Research Lab 2958 Georgia Tech-CNRS Metz France Department of Computer Science University of Freiburg Germany Toyota Research Institute Los Altos United States Photogrammetry & Robotics Lab University of Bonn Germany Robert Bosch GmbH Corporate Research Renningen Germany Osimo Italy Autonomous Systems Lab. Department of Mechanical and Process Engineering ETH Zurich Zürich Switzerland Department of Environmental Systems Science Institute of Agricultural Sciences ETH Zurich Zürich Switzerland IT+Robotics Srl Padova Italy Smart Robotics Lab. Department of Computing Imperial College London London United Kingdom CSIRO Data61 Australia

The application of autonomous robots in agriculture is gaining increasing popularity thanks to the high impact it may have on food security, sustainability, resource use efficiency, reduction of chemical treatments, and the optimization of human effort and yield. With this vision, the Flourish research project aimed to develop an adaptable robotic solution for precision farming that combines the aerial survey capabilities of small autonomous unmanned aerial vehicles (UAVs) with targeted intervention performed by multi-purpose unmanned ground vehicles (UGVs). This paper presents an overview of the scientific and technological advances and outcomes obtained in the project. We introduce multi-spectral perception algorithms and aerial and ground-based systems developed for monitoring crop density, weed pressure, crop nitrogen nutrition status, and to accurately classify and locate weeds. We then introduce the navigation and mapping systems tailored to our robots in the agricultural environment, as well as the modules for collab.rative mapping. We finally present the ground intervention hardware, software solutions, and interfaces we implemented and tested in different field conditions and with different crops. We describe a real use case in which a UAV collab.rates with a UGV to monitor the field and to perform selective spraying without human intervention. Copyright © 2019, The Authors. All rights reserved.

关键词： Mapping

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iSplit LBI: Individualized partial ranking with ties via split LBI

arXiv

引用

arXiv 2019年

作者： Xu, Qianqian Sun, Xinwei Yang, Zhiyong Cao, Xiaochun Huang, Qingming Yao, Yuan Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Microsoft Research Asia State Key Laboratory of Information Security Institute of Information Engineering CAS School of Cyber Security University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management CAS Peng Cheng Laboratory Department of Mathematics Hong Kong University of Science and Technology Hong Kong

Due to the inherent uncertainty of data, the problem of predicting partial ranking from pairwise comparison data with ties has attracted increasing interest in recent years. However, in real-world scenarios, different individuals often hold distinct preferences. It might be misleading to merely look at a global partial ranking while ignoring personal diversity. In this paper, instead of learning a global ranking which is agreed with the consensus, we pursue the tie-aware partial ranking from an individualized perspective. Particularly, we formulate a unified framework which not only can be used for individualized partial ranking prediction, but also be helpful for abnormal user selection. This is realized by a variable splitting-based algorithm called iSplitLBI. Specifically, our algorithm generates a sequence of estimations with a regularization path, where both the hyperparameters and model parameters are updated. At each step of the path, the parameters can be decomposed into three orthogonal parts, namely, abnormal signals, personalized signals and random noise. The abnormal signals can serve the purpose of abnormal user selection, while the abnormal signals and personalized signals together are mainly responsible for individual partial ranking prediction. Extensive experiments on simulated and real-world datasets demonstrate that our new approach significantly outperforms state-of-the-art alternatives. The code is now availiable at https://***/qianqianxu010/ NeurIPS2019-iSplitLBI. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

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Introducing the HOBBIT platform into the Ontology Alignment Evaluation campaign 13

Introducing the HOBBIT platform into the Ontology Alignment ...

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13th International Workshop on Ontology Matching, OM 2018

作者： Jiménez-Ruiz, Ernesto Saveta, Tzanina Zamazal, Ondřej Hertling, Sven Röder, Michael Fundulaki, Irini Ngomo, Axel-Cyrille Ngonga Sherif, Mohamed Ahmed Annane, Amina Bellahsene, Zohra Ben Yahia, Sadok Diallo, Gayo Faria, Daniel Kachroudi, Marouen Khiat, Abderrahmane Lambrix, Patrick Li, Huanyu Mackeprang, Maximilian Mohammadi, Majid Rybinski, Maciej Balasubramani, Booma Sowkarthiga Trojahn, Cassia Alan Turing Institute London United Kingdom Department of Informatics University of Oslo Norway Institute of Computer Science - FORTH Greece University of Economics Prague Czech Republic Data and Web Science Group University of Mannheim Germany Paderborn University Data Science Group Pohlweg 51 PaderbornD-33098 Germany Ecole Nationale Superieure d'Informatique Alger Algeria LIRMM Universit de Montpellier CNRS Montpellier France Université de Tunis El Manar LIPAH-LR 1ES14 Tunis2092 Tunisia BPH Center INSERM U1219 Team ERIAS LaBRI UMR5800 University of Bordeaux France Instituto Gulbenkian de Ciência Portugal Human-Centered Computing Freie Universitat Berlin Berlin4195 Germany Linköping University Swedish e-Science Research Centre Linköping Sweden Delft University of Technology Netherlands Dpto. Lenguajes y Ciencias de la Computación University of Malaga Spain ADVIS Lab. Department of Computer Science University of Illinois Chicago United States Institut de Recherche en Informatique de Toulouse France

This paper describes the Ontology Alignment Evaluation Initiative 2017.5 pre-campaign. Like in 2012, when we transitioned the evaluation to the SEALS platform, we have also conducted a pre-campaign to assess the feasibility of moving to the HOBBIT platform. We report the experiences of this pre-campaign and discuss the future steps for the OAEI. © 2018 CEUR-WS.

关键词： Ontology

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A Prediction Modelling and Pattern Detection Approach for the First-Episode Psychosis Associated to Cannabis Use

A Prediction Modelling and Pattern Detection Approach for th...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Wajdi Alghamdi Daniel Stamate Katherine Vang Daniel Stahl Marco Colizzi Giada Tripoli Diego Quattrone Olesya Ajnakina Robin M. Murray Marta Di Forti Data Science & Soft Computing Lab. and Department of Computing Goldsmiths. University of London Department of Rinstatistics Institute of Psychiatry Psychology and Neuroscience London MRC Social Genetic and Developmental Psychiatry Centre Institute of Psychiatry Psychology and Neuroscience London Department of Psychosis Studies Institute of Psychology and Neuroscience London

ISBN: (纸本)9781509061686

Over the last two decades, a significant body of research has established a link between cannabis use and psychotic outcomes. In this study, we aim to propose a novel symbiotic machine learning and statistical approach to pattern detection and to developing predictive models for the onset of first-episode psychosis. The data used has been gathered from real cases in cooperation with a medical research institution, and comprises a wide set of variables including demographic, drug-related, as well as several variables specifically related to the cannabis use. Our approach is built upon several machine learning techniques whose predictive models have been optimised in a computationally intensive framework. The ability of these models to predict first-episode psychosis has been extensively tested through large scale Monte Carlo simulations. Our results show that Boosted Classification Trees outperform other models in this context, and have significant predictive ability despite a large number of missing values in the data. Furthermore, we extended our approach by further investigating how different patterns of cannabis use relate to new cases of psychosis, via association analysis and Bayesian techniques.

关键词： Predictive models Computational modeling data models Psychiatry Monte Carlo methods Bayes methods Machine learning algorithms

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Testing of a predictive pole-slip protection algorithm on a Real Time Digital Simulator

Testing of a predictive pole-slip protection algorithm on a ...

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IEEE International Workshop on Applied Measurements for Power Systems (AMPS)

作者： Lafras Lamont Johan Rens Jan de Kock Department of Applied Electronics Tokyo University of Science Tokyo Japan School of Data and Computer Science Sun Yat-sen University Guangzhou China Key Lab. of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

Predictive pole-slip protection is not implemented in commercially availab.e protection relays. All pole-slip protection relays on the market will only trip a generator after it has pole-slipped one or more times. A predictive pole-slip protection algorithm was developed that can trip a generator before a pole-slip occurs. The Predictive Pole-Slip algorithm was tested on a Real Time Digital Simulator. This paper explains the testing setup as well as the scenarios tested. The feasibility of predictive pole-slip protection algorithm in a practical application is also discussed.

关键词： Generators Circuit faults Prediction algorithms Transient analysis Relays Power system stability

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROscience 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Pivot probability induction for statistical machine translation with topic similarity

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Journal of Computational Information Systems 2013年第20期9卷 8351-8359页

作者： Huang, Yanzhou Shi, Xiaodong Su, Jinsong Chen, Yidong Huang, Guimin Fujian Key Lab. of the Brain-Like Intelligent Systems Xiamen University Xiamen 361005 China Department of Cognitive Science School of Information Science and Technology Xiamen University Xiamen 361005 China School of Software Xiamen University Xiamen 361005 China Research Center on Data Science and Social Computing Guilin University of Electronic Technology Guilin 541004 China

Previous works employ the pivot language approach to conduct statistical machine translation when encountering with limited amount of bilingual corpus. Conventional solutions based upon phrase-table combination overlook the semantic discrepancy between the source-pivot corpus and pivot-target corpus and consequently lead to probability estimation inaccuracy for the induced translation rules. In this paper, the latent topic structure of the document-level training data is learned automatically and each phrase translation rule is assigned to a topic distribution. Furthermore, the phrase probability induction is carried out on the basis of the topic similarity, allowing the translation system to consider the semantic relatedness among different rules. Using BLEU as a metric of translation accuracy, we find out our system is capable of achieving a absolute improvement in in-domain test compared with the baseline system. © 2013 Binary Information Press.

关键词： Semantics

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Practical privacy for value-added applications in vehicular ad hoc networks 5th

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5th International Conference on Internet and Distributed computing Systems, IDCS 2012

作者： Zhang, Lei Wu, Qianhong Qin, Bo Domingo-Ferrer, Josep Shanghai Key Laboratory of Trustworthy Computing Software Engineering Institute East China Normal University Shanghai China UNESCO Chair in Data Privacy Dept. of Comp. Eng. and Maths Universitat Rovira i Virgili TarragonaCatalonia Spain Key Lab. of Aerospace Information Security and Trusted Computing Ministry of Education Wuhan University School of Computer China Department of Maths School of Science Xi’an University of Technology China

ISBN: (纸本)9783642348822

Advances in mobile networking and information processing technologies have triggered vehicular ad hoc networks (VANETs) for traffic safety and value-added applications. Most efforts have been made to address the security concerns while little work has been done to investigate security and privacy for value-added applications in VANETs. To fill this gap, we propose a value-added application, specifically, a security and privacy preserving location-based service (LBS) scheme for VANETs. For each LBS transaction, the scheme provides authentication, integrity and non-repudiation for both the service provider and the user. A user can obtain the service in an anonymous way and hence user privacy is well protected. However, a tracing procedure can be invoked to find malicious users, thereby efficiently preventing users from abusing the anonymity provided by the system. © Springer-Verlag Berlin Heidelberg 2012.

关键词： Location based services

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Hierarchical certificateless signatures

Hierarchical certificateless signatures

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International Workshop on Wireless Network Algorithm and Theory

作者： Zhang, Lei Wu, Qianhong Domingo-Ferrer, Josep Qin, Bo UNESCO in Data Privacy Department of Computer Engineering and Mathematics Universitat Rovira i Virgili Catalonia Spain Wuhan University School of Computer Key Lab. of Aerospace Information Security and Trusted Computing China Xi'an University of Technology School of Science Department of Maths China

ISBN: (纸本)9780769543222

Certificateless cryptography eliminates the key escrow problem in identity-based cryptography. Hierarchical cryptography exploits a practical security model to mirror the organizational hierarchy in the real world. In this paper, to incorporate the advantages of both types of cryptosystems, we instantiate hierarchical certificateless cryptography by formalizing the notion of hierarchical certificateless signatures. Furthermore, we propose an HCLS scheme which, under the hardness of the computational Diffie-Hellman (CDH) problem, is proven to be existentially unforgeable against adaptive chosen-message attacks in the random oracle model. As to efficiency, our scheme has constant complexity, regardless of the depth of the hierarchy. Hence, the proposal is secure and scalab.e for practical applications. © 2010 IEEE.

关键词： Network security

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