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检索条件"任意字段=2nd International Conference on Algorithms for Big Data, ICABD 2014"

共 223 条记录，以下是191-200 订阅

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Model based clustering method as a new multivariate technique in high energy physics

Model based clustering method as a new multivariate techniqu...

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2nd international conference on Mathematical Modeling in Physical Sciences (IC-MSQUARE)

作者： Stepanek, Michal Franc, Jiri Kus, Vaclav Czech Tech Univ Fac Nucl Sci & Phys Engn Dept Math Prague 12000 2 Czech Republic

Analysis of experimental data has one of the most important roles in High Energy Physics. Commonly used multivariate techniques such as Boosted Decision Trees or Bayesian Neural Networks are based on learning algorithms using Monte Carlo generated samples. We implemented a new Model Based Clustering method using Bayesian statistics and a modified iterative Expectation-Maximization algorithm for weighted data that have never been applied in this area. This greatly promising method was developed especially for the data collected from the Dempty set experiment, which was one of two large particle physics experiments at the Tevatron proton-antiproton collider at Fermilab. We optimized and tested the proposed method in the single top search using a data sample of 9.7 fb(-1) of integrated luminosity, which corresponds to the entire Run II Dempty set dataset.

关键词： High energy physics

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Proceedings - 2014 IEEE international conference on big data, IEEE big data 2014

Proceedings - 2014 IEEE International Conference on Big Data...

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2nd IEEE international conference on big data, IEEE big data 2014

ISBN: (纸本)9781479956654

The proceedings contain 295 papers. The topics discussed include: never-ending language learning;smart data - how you and i will exploit big data for personalized digital health and many other activities;addressing human bottlenecks in big data;BayesWipe: a multimodal system for data cleaning and consistent query answering on structured bigdata;metadata capital: simulating the predictive value of self-generated health information (SGHI);towards building and evaluating a personalized location-based recommender system;on the performance of MapReduce: a stochastic approach;PGMHD: a scalable probabilistic graphical model for massive hierarchical data problems;FusionFS: toward supporting data-intensive scientific applications on extreme-scale high-performance computing systems;and detecting and identifying system changes in the cloud via discovery by example.

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Deep learning for neuroimaging: A validation study 2

Deep learning for neuroimaging: A validation study

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2nd international conference on Learning Representations, ICLR 2014

作者： Plis, Sergey M. Hjelm, Devon R. Salakhutdinov, Ruslan Calhoun, Vince D. Mind Research Network AlbuquerqueNM87106 United States University of New Mexico AlbuquerqueNM87131 United States University of Toronto TorontoONM5S 2E4 Canada

Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neuroimager’s toolbox. Success of these methods is, in part, explained by the flexibility of deep learning models. However, this flexibility makes the process of porting to new areas a difficult parameter optimization problem. In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data. We also describe a novel constraint-based approach to visualizing high dimensional data. We use it to ana- lyze the effect of parameter choices on data transformations. Our results show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data. © 2014 international conference on Learning Representations, ICLR. All rights reserved.

关键词： Clustering algorithms

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Handling smart environment devices, data and services at the semantic level with the FI-WARE core platform 2

Handling smart environment devices, data and services at the...

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2nd IEEE international conference on big data, IEEE big data 2014

作者： Ramparany, Fano Marquez, Fermin Galan Soriano, Javier Elsaleh, Tarek Orange Labs 28 chemin du Vieux Chene Meylan38243 France Telefónica I+D Ronda de la Comunicación s/n Madrid28050 Spain Universidad Politécnica de Madrid Boadilla del Monte Madrid28660 Spain University of Surrey Guildford SurreyGU2 7XH United Kingdom

ISBN: (纸本)9781479956654

The development of IoT (Internet of Things) applications poses a number of scientific and technological challenges which stem from the characteristics of the IoT domain itself. These include the huge and increasing number of connected entities (devices, physical objects, people.,) populating the physical environment, the variety of their types which leads to heterogeneity of the data produced or consumed by those connected entities. In this paper we argue that semantic modeling takes up many of these challenges and explain how the core platform developed by the FI-WARE project supports IoT application developers. After a short introduction of the characteristics and requirements of IoT applications we identify the contribution of semantic technologies to address some of them. We describe the FI-WARE platform enablers which support these technologies and illustrate through a real application how these enablers help developers satisfy these requirements. © 2014 IEEE.

关键词： Internet of things

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data Dissemination algorithms for Communicating Materials using Wireless Sensor Networks 2

Data Dissemination Algorithms for Communicating Materials us...

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2nd international conference on Future Internet of Things and Cloud (FiCloud)

作者： Mekki, Kais Derigent, William Zouinkhi, Ahmed Rondeau, Eric Abdelkrim, Mohamed Naceur Univ Lorraine CRAN UMR 7039 2 Ave Foret Haye F-54516 Vandoeuvre Les Nancy France CNRS CRAN UMR 7039 F-75700 Paris France Natl Engn Sch Gabes Res Unit Modeling Anal & Control Syst MACS Rue Omar Ibn Elkhattab Gabes 6029 Tunisia

ISBN: (纸本)9781479943579

The communicating material is a new paradigm of Internet of Things. It is designed to perform efficient product control and ensure an information continuum all along the product life cycle. Therefore, storage of life cycle information and data dissemination in communicating materials are very important issues. This paper provides solutions for storing data on the material by systematic dissemination through integrated ultra-small wireless sensors nodes using counter-based broadcasting scheme, hop-counter and probabilistic storage mechanisms. Different algorithms are developed for non-localized and localized dissemination. The performances of our solutions are evaluated for non-segmented and then segmented data. Comparison results between storage mechanisms via simulation using Castalia/OMNeT++ show that probabilistic algorithm provides uniform and efficient data dissemination than hop-counter one for different density level.

关键词： communicating materials wireless sensor networks data dissemination

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Sustainable Choices for Cloud Applications: a Focus on CO₂ Emissions 2

Sustainable Choices for Cloud Applications: a Focus on CO<su...

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2nd international conference on ICT for Sustainability (ICTS)

作者： Cappiello, Cinzia Melia, Paco Pernici, Barbara Plebani, Pierluigi Vitali, Monica Politecn Milan Dipartimento Elettron Informaz & Bioingn I-20133 Milan Italy

ISBN: (纸本)9789462520226

The widespread adoption of cloud computing is having a big impact on the environment since the energy consumption of data centers and the resulting emissions are significantly increasing. Researchers and practitioners in this field are looking for methods to improve the energy efficiency of data centers and increase the use of green energy sources. In fact, besides the energy consumption, the greenness of a data center can be characterized by the quantity of CO2 emissions associated with the use of electricity (from a specific energy mix) and/or fuels (e.g., for heating or cooling). In this paper, we propose an approach in which environmental impacts are considered as an important factor for the selection of the cloud site for the deployment of applications. In detail, considering a user perspective and focusing on the assessment of energy consumption and CO2 emissions, this paper proposes a method to support the users towards greener choices in the deployment of cloud applications.

关键词： Cloud applications CO2 emissions energy mix energy consumption

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Research on cultural industry distributed network control model based on genetic algorithm

Research on cultural industry distributed network control mo...

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2nd international conference on Precision Mechanical Instruments and Measurement Technology, ICPMIMT 2014

作者： Xue, Hong Wei School of International Tourism and Culture Guizhou Normal University Guiyang 550001 China

ISBN: (纸本)9783038351948

The network economy era brings new challenges to all aspects of society, and the construction of network culture industry also brings an urgent request. Due to the complexity of network culture industry, the construction and study of cultural network industry has certain challenging. In order to realize the massive network industrial data analysis, we introduce the genetic algorithm into the network control model, and put forward the distributed network control model based on genetic algorithm, which can realize the massive data analysis. In order to verify the effectiveness of the algorithm, this paper uses MATLAB genetic algorithms toolbox to debug the algorithm, and obtains the genetic algorithm fitness curve and the data transmission quantity statistics. It provides the theory reference for the construction of network culture industry. © (2014) Trans Tech Publications, Switzerland.

关键词： Genetic algorithms

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Urban Infrastructure Risk Assessment toward Extreme Snowstorms Using Satellite data 2

Urban Infrastructure Risk Assessment toward Extreme Snowstor...

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2nd international conference on Vulnerability and Risk Analysis and Management, ICVRAM 2014 and the 6th international Symposium on Uncertainty Modeling and Analysis, ISUMA 2014

作者： Kostyuchenko, Yuriy V. Bilous, Yu. H. Solovyov, D.M. Kopachevsky, I.M. Scientific Centre for Aerospace Research of the Earth National Academy of Sciences of Ukraine 55-b O. Honchar street Kiev01601 Ukraine Dept. of Soil Sciences and Geomorphology Faculty of Geography Taras Shevchenko National University of Kiev 2A Glushkov Avenue Kiev03075 Ukraine Marine Hydrophysical Institute National Academy of Sciences of Ukraine 2 Kapitanska street Sevastopol05312 Ukraine

ISBN: (纸本)9780784413609

Some aspects of the theoretical and methodological basis for the use of remote sensing data of snow cover for hazards assessment related to meteorological, climatic, hydrological and hydrogeological risks over urban areas are discussed in this paper. A method for determining the snow cover parameters on remote sensing data base - in particular, MOD10A1 and SWE products, normalized index of snow depth (ndSI) and local meteorological observations - have been proposed. A method for data integrating from various sources on the basis of the modified Ensemble Transform Kalman Filter (ETKF) and Kernel Principal Component Analysis (KPCA) of data distributions have been proposed. It is shown that the proposed approach has quite high relative accuracy in comparison with existing methods, algorithms and products (if MOD10A1 and SWE products have been used separately) with application for urban agglomeration. As an example, the approach has been used to analyze extreme snowfall in Kiev (21-23 March 2013). Quantitative assessments of risk indicators and vulnerability parameters of municipal infrastructure under emergency effects have been proposed. © 2014 American Society of Civil Engineers.

关键词： Risk assessment

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HNSARA: A history-and-node-state-based active routing algorithm for DTN

HNSARA: A history-and-node-state-based active routing algori...

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international conference on Cyberspace Technology, CCT 2014

作者： Zhang, Haipeng Lu, Ningning Ma, Jing Yang, Yue 54th Research Institure of China Electronics Technology Group Corportion Building 10 2nd Section Fengtai District Beijing100070 China

ISBN: (纸本)9781849199285

The routing algorithm of the DTN, which experience frequent long-duration partitions, is quite different from the normal networks. Active routing algorithms usually adopt an active host (data mule/message ferry) to exchange and forward the messages. In the existing studies in DTN, the active routing problem is generally solved by adopting the solutions of the Travelling Salesman Problem (TSP). In such solutions, the active routing problem is often solved by finding a simple cycle with no repeated nodes nor edges. By considering a more general scenario, we propose an active routing algorithm, which is based on the historical information and node states, where the route is a closed walk. The simulations verify the accuracy and efficiency of the active routing algorithm we proposed in this paper.

关键词： Routing algorithms

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ACM international conference Proceeding Series

ACM International Conference Proceeding Series

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2nd Workshop on Machine Learning for Sensory data Analysis, MLSDA 2014

ISBN: (纸本)9781450331593

The proceedings contain 10 papers. The topics discussed include: real time unobtrusive human behaviors recognition;long-term tracking in batmon: lessons and open challenges;anomaly detection using autoencoders with nonlinear dimensionality reduction;spatial-temporal pyramid matching for crowd scene analysis;multi-action recognition via stochastic modeling of optical flow and gradients;multi-feature fusion based non negative matrix factorization: facial expression recognition from imaging sensors;multi-level analysis of peace and conflict data in GDELT;extracting user interests from graph connections for machine learning in location-based social networks;air pollution exposure estimation and finding association with human activity using wearable sensor network;and distributed feature selection with big sensor data.

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