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4th International Conference on data Management Technologies and Applications, data 2015

作者： Caldarola, Enrico Giacinto Rinaldi, Antonio Maria Department of Electrical Engineering and Information Technologies University of Naples Federico II Napoli Italy Institute of Industrial Technologies and Automation National Research Council Bari Italy IKNOS-LAB Intelligent and Knowledge Systems University of Naples Federico II LUPT 80134 via Toledo Napoli402 Italy

ISBN: (纸本)9789897581038

For several years we are living in the era of information. Since any human activity is carried out by means of information technologies and tends to be digitized, it produces a humongous stack of data that becomes more and more attractive to different stakeholders such as data scientists, entrepreneurs or just privates. All of them are interested in the possibility to gain a deep understanding about people and things, by accurately and wisely analyzing the gold mine of data they produce. The reason for such interest derives from the competitive advantage and the increase in revenues expected from this deep understanding. In order to help analysts in revealing the insights hidden behind data, new paradigms, methodologies and tools have emerged in the last years. There has been a great explosion of technological solutions that arises the need for a review of the current state of the art in the Big data technologies scenario. Thus, after a characterization of the new paradigm under study, this work aims at surveying the most spread technologies under the Big data umbrella, throughout a qualitative analysis of their characterizing features.

关键词： Big data

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Learning relative similarity from data streams: Active online learning approaches 15

Learning relative similarity from data streams: Active onlin...

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24th ACM International Conference on information and knowledge Management, CIKM 2015

作者： Hao, Shuji Zhao, Peilin Hoi, Steven C.H. Miao, Chunyan Interdisciplinary Graduate School Nanyang Technological University NTU Singapore Singapore Data Analytics Department Institute for Infocomm Research A-STAR Singapore Singapore School of Information Systems Singapore Management University Singapore Singapore School of Computer Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)9781450337946

Relative similarity learning, as an important learning scheme for information retrieval, aims to learn a bi-linear similarity function from a collection of labeled instance-pairs, and the learned function would assign a high similarity value for a similar instance-pair and a low value for a dissimilar pair. Existing algorithms usually assume the labels of all the pairs in data streams are always made available for learning. However, this is not always realistic in practice since the number of possible pairs is quadratic to the number of instances in the database, and manually labeling the pairs could be very costly and time consuming. To overcome the limitation, we propose a novel framework of active online similarity learning. Specifically, we propose two new algorithms: (i) PAAS: Passive-Aggressive Active Similarity learning;(ii) CWAS: Confidence-Weighted Active Similarity learning, and we will prove their mistake bounds in theory. We have conducted extensive experiments on a variety of real-world data sets, and we find encouraging results that validate the empirical effectiveness of the proposed algorithms. © 2015 ACM.

关键词： Learning algorithms

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Neural network approach for T-wave end detection: A comparison of architectures 42

Neural network approach for T-wave end detection: A comparis...

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42nd Computing in Cardiology Conference, CinC 2015

作者： Suárez León, Alexander A. Molina, Danelia Matos Vázquez Seisdedos, Carlos R. Goovaerts, Griet Vandeput, Steven Van Huffel, Sabine Electrical Engineering Faculty Universidad de Oriente Departamento de Ingeniería Biomédica Ave. Américas y Casero. Santiago-de Cuba90400 Cuba KU Leuven Department of Electrical Engineering-ESAT STADIUS Centre for Dynamical Systems Signal Processing and Data Analytics Belgium IMinds Medical Information Teclmologies Belgium

ISBN: (纸本)9781509006854

In this paper, a new approach to the problem of detecting the end of the T wave (Te) on the electrocardiogram (ECG) using Multilayer Perceptron (MLP) neural networks is proposed and evaluated. The approach consists of a neural network acting as a regression function that estimates the Te location using the samples between two consecutive R peaks. The input vectors were taken using three dimensional reduction methods (Discrete Cosine Transform, DCT, Principal Component Analysis, PCA and resampling, RES) over a window of 100 samples. For training, Bayesian regularization has been used. A total of 1536 neural networks were trained. The results show that PCA and DCT are more feasible than RES as dimension reduction methods. Finally, a brief comparison with other algorithms proposed in the literature is included. © 2015 CCAL.

关键词： Multilayer neural networks

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An approach to ontology integration for ontology reuse in knowledge based digital ecosystems 15

An approach to ontology integration for ontology reuse in kn...

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7th ACM International Conference on Management of Computational and Collective IntElligence in Digital Ecosystems, MEDES 2015

作者： Caldarola, Enrico G. Picariello, Antonio Rinaldi, Antonio M. Department of Electrical Engineering and Information Technologies University of Naples Federico II Via Claudio 21 Napoli Italy Institute of Industrial Technologies and Automation National Research Council Via P. Lembo 38F Bari Italy IKNOS-LAB Intelligent and Knowledge Systems University of Naples Federico II LUPT Via Toledo 402 Napoli Italy

ISBN: (纸本)9781450334808

In the last years, the large availability of information and knowledge models formalized by ontologies has demanded effective and efficient methodologies for reusing and integrating such models in global conceptualizations of a specific knowledge or application domain. The ability to effectively and efficiently perform knowledge reuse is a crucial factor in the development of ontologies, which are a potential solution to the problem of information standardization and a viaticum towards the realization of knowledge-based digital ecosystem. In this paper, an approach to ontology reuse based on heterogeneous matching techniques will be presented;in particular, we will show how the process of ontology building will be improved and simplified, by automating the selection and the reuse of existing data models to support the creation of digital ecosystems. The proposed approach has been applied to the food domain, specifically to food production. © 2015 ACM.

关键词： Ontology

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Energy Spectrum of Cosmic-Ray Electron and Positron from 10 GeV to 3 TeV Observed with the Calorimetric Electron Telescope on the International Space Station

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Physical Review Letters 2017年第18期119卷 181101-181101页

作者： O. Adriani Y. Akaike K. Asano Y. Asaoka M. G. Bagliesi G. Bigongiari W. R. Binns S. Bonechi M. Bongi P. Brogi J. H. Buckley N. Cannady G. Castellini C. Checchia M. L. Cherry G. Collazuol V. Di Felice K. Ebisawa H. Fuke T. G. Guzik T. Hams M. Hareyama N. Hasebe K. Hibino M. Ichimura K. Ioka W. Ishizaki M. H. Israel A. Javaid K. Kasahara J. Kataoka R. Kataoka Y. Katayose C. Kato N. Kawanaka Y. Kawakubo H. S. Krawczynski J. F. Krizmanic S. Kuramata T. Lomtadze P. Maestro P. S. Marrocchesi A. M. Messineo J. W. Mitchell S. Miyake K. Mizutani A. A. Moiseev K. Mori M. Mori N. Mori H. M. Motz K. Munakata H. Murakami S. Nakahira J. Nishimura G. A. de Nolfo S. Okuno J. F. Ormes S. Ozawa L. Pacini F. Palma P. Papini A. V. Penacchioni B. F. Rauch S. B. Ricciarini K. Sakai T. Sakamoto M. Sasaki Y. Shimizu A. Shiomi R. Sparvoli P. Spillantini F. Stolzi I. Takahashi M. Takayanagi M. Takita T. Tamura N. Tateyama T. Terasawa H. Tomida S. Torii Y. Tsunesada Y. Uchihori S. Ueno E. Vannuccini J. P. Wefel K. Yamaoka S. Yanagita A. Yoshida K. Yoshida T. Yuda Department of Physics University of Florence Via Sansone 1–50019 Sesto Fiorentino Italy INFN Sezione di Florence Via Sansone 1–50019 Sesto Fiorentino Italy of Physics University of Maryland Baltimore County 1000 Hilltop Circle Baltimore Maryland 21250 USA Astroparticle Physics Laboratory NASA/GSFC Greenbelt Maryland 20771 USA Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa Chiba 277-8582 Japan Research Institute for Science and Engineering Waseda University 3-4-1 Okubo Shinjuku Tokyo 169-8555 Japan JEM Utilization Center Human Spaceflight Technology Directorate Japan Aerospace Exploration Agency 2-1-1 Sengen Tsukuba Ibaraki 305-8505 Japan Department of Physical Sciences Earth and Environment University of Siena via Roma 56 53100 Siena Italy INFN Sezione di Pisa Polo Fibonacci Largo B. Pontecorvo 3–56127 Pisa Italy Department of Physics Washington University One Brookings Drive St. Louis Missouri 63130-4899 USA Department of Physics and Astronomy Louisiana State University 202 Nicholson Hall Baton Rouge Louisiana 70803 USA Institute of Applied Physics (IFAC) National Research Council (CNR) Via Madonna del Piano 10 50019 Sesto Fiorentino Italy Department of Physics and Astronomy University of Padova Via Marzolo 8 35131 Padova Italy INFN Sezione di Padova Via Marzolo 8 35131 Padova Italy University of Rome “Tor Vergata ” Via della Ricerca Scientifica 1 00133 Rome Italy INFN Sezione di Rome “Tor Vergata ” Via della Ricerca Scientifica 1 00133 Rome Italy Institute of Space and Astronautical Science Japan Aerospace Exploration Agency 3-1-1 Yoshinodai Chuo Sagamihara Kanagawa 252-5210 Japan CRESST and Astroparticle Physics Laboratory NASA/GSFC Greenbelt Maryland 20771 USA St. Marianna University School of Medicine 2-16-1 Sugao Miyamae-ku Kawasaki Kanagawa 216-8511 Japan Kanagawa University 3-27-1 Rokkakubashi Kanagawa Yokohama Kanagawa 221-8686 Japan Faculty of Science and Technology Gradua

First results of a cosmic-ray electron and positron spectrum from 10 GeV to 3 TeV is presented based upon observations with the CALET instrument on the International Space Station starting in October, 2015. Nearly a half million electron and positron events are included in the analysis. CALET is an all-calorimetric instrument with total vertical thickness of 30 X0 and a fine imaging capability designed to achieve a large proton rejection and excellent energy resolution well into the TeV energy region. The observed energy spectrum over 30 GeV can be fit with a single power law with a spectral index of −3.152±0.016 (stat+syst). Possible structure observed above 100 GeV requires further investigation with increased statistics and refined data analysis.

关键词： Cosmic ray acceleration Cosmic ray composition & spectra Cosmic ray propagation Cosmic ray sources Cosmic rays & astroparticles Particle dark matter

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Automatic collection of the parallel corpus with little prior knowledge

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8801卷 95-106页

作者： Ma, Shutian Zhang, Chengzhi Department of Information Management Nanjing University of Science and Technology Nanjing210094 China Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing University Nanjing210093 China

As an important resource for machine translation and cross-language information retrieval, collecting large-scale parallel corpus has been paid wide attention. With the development of the Internet, researchers begin to mine the parallel corpora from the multilingual websites. They use some prior knowledge like ad hoc heuristics or calculate the similarity of the webpages structure and content to find the bilingual webpages. This paper presents a method that uses the search engine and little prior knowledge about the URL patterns to get the bilingual websites from the Internet. The method is fast for its low time cost and there is no need for large-scale computation on URL pattern matching. We have collected 88 915 candidate parallel Chinese-English webpages, which average accuracy is around 90.8%. During the evaluation, the true bilingual websites that we found have high similar html structure and good quality translations. © Springer International Publishing Switzerland 2014.

关键词： Websites

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Multivariate spearman's ρ for aggregating ranks using copulas

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2016年第1期17卷

作者： Kevin Murphy Bernhard Schölkopf Justin Bedö Cheng Soon Ong Google MPI for Intelligent Systems The Walter and Eliza Hall Institute Victoria Australia and The Department of Computing and Information Systems The University of Melbourne VIC Australia Data61 CSIRO Canberra ACT Australia and Research School of Computer Science The Australian National University Australia and The Department of Electrical and Electronic Engineering The University of Melbourne VIC Australia

We study the problem of rank aggregation: given a set of ranked lists, we want to form a consensus ranking. Furthermore, we consider the case of extreme lists: i.e., only the rank of the best or worst elements are known. We impute missing ranks and generalise Spearman's ρ to extreme ranks. Our main contribution is the derivation of a non-parametric estimator for rank aggregation based on multivariate extensions of Spearman's ρ, which measures correlation between a set of ranked lists. Multivariate Spearman's ρ is defined using copulas, and we show that the geometric mean of normalised ranks maximises multivariate correlation. Motivated by this, we propose a weighted geometric mean approach for learning to rank which has a closed form least squares solution. When only the best (top-k) or worst (bottom-k) elements of a ranked list are known, we impute the missing ranks by the average value, allowing us to apply Spearman's ρ. We discuss an optimistic and pessimistic imputation of missing values, which respectively maximise and minimise correlation, and show its effect on aggregating university rankings. Finally, we demonstrate good performance on the rank aggregation benchmarks MQ2007 and MQ2008.

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knowledge engineering with Big data

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IEEE Intelligent systems 2015年第5期30卷 46-55页

作者： Wu, Xindong Chen, Huanhuan Wu, Gongqing Liu, Jun Zheng, Qinghua He, Xiaofeng Zhou, Aoying Zhao, Zhong-Qiu Wei, Bifang Gao, Ming Li, Yang Zhang, Qiping Zhang, Shichao Lu, Ruqian Zheng, Nanning School of Computer Science and Information Engineering Hefei University of Technology China University of Vermont United States University of Edinburgh School of Computer Science United Kingdom School of Computer Science University of Science and Technology of China China Department of Computer Science Xi'An Jiaotong University China Institute for Data Science and Engineering East China Normal University China Pennsylvania State University United States Fudan University China Department of Computer Science Zhejiang Gongshang University China Deakin University Australia Academy of Mathematics and Systems Science Chinese Academy of Sciences China Institute of Artificial Intelligence and Robotics Xi'An Jiaotong University China Keio University Japan

In the era of big data, knowledge engineering faces fundamental challenges induced by fragmented knowledge from heterogeneous, autonomous sources with complex and evolving relationships. The knowledge representation, acquisition, and inference techniques developed in the 1970s and 1980s, driven by research and development of expert systems, must be updated to cope with both fragmented knowledge from multiple sources in the big data revolution and in-depth knowledge from domain experts. This article presents BigKE, a knowledge engineering framework that handles fragmented knowledge modeling and online learning from multiple information sources, nonlinear fusion on fragmented knowledge, and automated demand-driven knowledge navigation. © 2001-2011 IEEE.

关键词： Intelligent systems

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Sustainable computational science: The ReScience initiative

arXiv

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arXiv 2017年

作者： Rougier, Nicolas P. Hinsen, Konrad Alexandre, Frédéric Arildsen, Thomas Barba, Lorena Benureau, Fabien C.Y. Titus Brown, C. de Buyl, Pierre Caglayan, Ozan Davison, Andrew P. Delsuc, Marc André Detorakis, Georgios Diem, Alexandra K. Drix, Damien Enel, Pierre Girard, Benoît Guest, Olivia Hall, Matt G. Henriques, Rafael Neto Hinaut, Xavier Jaron, Kamil S. Khamassi, Mehdi Klein, Almar Manninen, Tiina Marchesi, Pietro McGlinn, Dan Metzner, Christoph Petchey, Owen L. Plesser, Hans Ekkehard Poisot, Timothée Ram, Karthik Ram, Yoav Roesch, Etienne Rossant, Cyrille Rostami, Vahid Shifman, Aaron Stimberg, Marcel Stachelek, Joseph Stollmeier, Frank Vaggi, Federico Viejo, Guillaume Vitay, Julien Vostinar, Anya Yurchak, Roman Zito, Tiziano INRIA Bordeaux Sud-Ouest Talence France Institut des Maladies Neurodégénératives Université de Bordeaux CNRS UMR 5293 Bordeaux France LaBRI Université de Bordeaux Bordeaux INP CNRS UMR 5800 Talence France Centre de Biophysique Moléculaire CNRS UPR4301 Orléans France Synchrotron SOLEIL Division Expériences Gif sur Yvette France Department of Electronic Systems Technical Faculty of IT and Design Aalborg University Denmark Department of Mechanical and Aerospace Engineering George Washington University WashingtonDC United States Department of Population Health and Reproduction University of California Davis DavisCA United States Instituut voor Theoretische Fysica KU Leuven Belgium Le Mans France Unité de Neurosciences Information et Complexité CNRS FRE 3693 Gif sur Yvette France Institut de Génétique et de Biologie Moléculaire et Cellulaire INSERM U964 CNRS UMR 7104 Université de Strasbourg Illkirch France Department of Cognitive Sciences University of California Irvine United States Computational Engineering and Design Faculty of Engineering and the Environment University of Southampton United Kingdom Department of Computer Science Humboldt-Universität zu Berlin Friedman Brain Institute Icahn School of Medicine Mount SinaiNY United States Institut des Systèmes Intelligent et de Robotique Sorbonne Universités UPMC Univ Paris 06 CNRS Paris France Experimental Psychology University College London United Kingdom UCL Great Ormond St Institute of Child Health University College London London United Kingdom MRC Cognition and Brain Sciences Unit Cambridge United Kingdom Department of Ecology and Evolution University of Lausanne Lausanne Switzerland Independent scholar Enschede Netherlands BioMediTech Institute and Faculty of Biomedical Sciences and Engineering Tampere University of Technology Tampere Finland Swammerdam Institute for Life Sciences Center for Neuroscience Faculty of Science University of Amsterdam Amsterdam Netherlands Depart

Computer science offers a large set of tools for prototyping, writing, running, testing, validating, sharing and reproducing results, however computational science lags behind. In the best case, authors may provide their source code as a compressed archive and they may feel confident their research is reproducible. But this is not exactly true. Jonathan Buckheit and David Donoho proposed more than two decades ago that an article about computational results is advertising, not scholarship. The actual scholarship is the full software environment, code, and data that produced the result. This implies new workflows, in particular in peer-reviews. Existing journals have been slow to adapt: source codes are rarely requested, hardly ever actually executed to check that they produce the results advertised in the article. ReScience is a peer-reviewed journal that targets computational research and encourages the explicit replication of already published research, promoting new and open-source implementations in order to ensure that the original research can be replicated from its description. To achieve this goal, the whole publishing chain is radically different from other traditional scientific journals. ReScience resides on GitHub where each new implementation of a computational study is made available together with comments, explanations, and software tests. Copyright © 2017, The Authors. All rights reserved.

关键词： Open source software

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Big graph-based data visualization experiences: The WordNet case study 7th

Big graph-based Data visualization experiences: The WordNet ...

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International Joint Conference on knowledge Discovery, knowledge engineering and knowledge Management (IC3K)

作者： Enrico G. Caldarola Antonio Picariello Antonio M. Rinaldi Institute of Industrial Technologies and Automation National Research Council Bari Italy Department of Electrical Engineering and Information Technologies University of Naples Federico II Napoli Italy IKNOS-LAB Intelligent and Knowledge Systems University of Naples Federico II Napoli Italy

ISBN: (纸本)9781509019670

In the Big data era, the visualization of large data sets is becoming an increasingly relevant task due to the great impact that data have from a human perspective. Since visualization is the closer phase to the users within the data life cycle's phases, there is no doubt that an effective, efficient and impressive representation of the analyzed data may result as important as the analytic process itself. This paper presents an experience for importing, querying and visualizing graph database and in particular, we describe as a case study the WordNet database using Neo4J and Cytoscape. We will describe each step in this study focusing on the used strategies for overcoming the different problems mainly due to the intricate nature of the case study. Finally, an attempt to define some criteria to simplify the large-scale visualization of WordNet will be made, providing some examples and considerations which have arisen.

关键词： Layout data visualization Visualization Springs Visual databases Semantics Three-dimensional displays

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