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检索条件"任意字段=10th International Workshop on Algorithms and Data Structures"

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Keynote abstract: Subspace search for community detection and community outlier mining in attributed graphs

Keynote abstract: Subspace search for community detection an...

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international workshop on Semantic Media Adaptation and Personalization (SMAP)

作者： Emmanuel Müller Knowledge Discovery and Data Mining Group Hasso-Plattner-Institute Germany

Attributed graphs are widely used for the representation of social networks, gene and protein interactions, communication networks, or product co-purchase in web stores. Each object is represented by its relationships to other objects (edge structure) and its individual properties (node attributes). For instance, social networks store friendship relations as edges and age, income, and other properties as attributes. these relationships and properties seem to be dependent on each other and exploiting these dependencies is beneficial, e.g. for community detection and community outlier mining. However, state-of-the-art techniques highly rely on this dependency assumption. In particular, community outlier mining is able to detect an outlier node if and only if connected nodes have similar values in all attributes. Such assumptions are generally known as homophily and are widely used. However, looking at multivariate spaces, one can observe that not all given attributes have high dependencies with the graph structure. For example, social properties such as income or age have strong dependencies with the graph structure of social networks. In contrast, properties such as gender are rather independent from it. Consequently, recent graph mining algorithms degenerate for multivariate attribute spaces that lack dependency with the graph structure in some of the attributes.

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Estimating vigilance from EEG using manifold clustering guided by instantaneous lapse rate

Estimating vigilance from EEG using manifold clustering guid...

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international Conference on Information, Communications and Signal Processing

作者： Haoqi Sun Yan Yang Olga Sourina Guang-Bin Huang Felix Klanner Cornelia Denk Ralph H. Rasshofer Fraunhofer IDM @ NTU A Joint Initiative of BMW Group & NTU Nanyang Technological University Singapore Energy Research Institute @ NTU (ERI@N) Interdisciplinary Graduate School School of Electrical and Electronic Engineering School of Computer Engineering Nanyang Technological University Singapore Singapore SG

ISBN: (纸本)9781467372190

Vigilance decrement happens in prolonged and monotonous tasks such as driving, therefore efficient estimation of vigilance using machine learning becomes a growing research field in road safety. However, the ground truth of vigilance level is often unknown. To address the estimation of brain states with unknown ground truth, we proposed an unsupervised manifold clustering method guided by task performance, namely instantaneous lapse rate, without directly using any artificially labels, using electroencephalogram (EEG) as data source. the proposed algorithm utilizes information from both data structure and task performance, which is especially suitable for applications with unknown ground truth. Future research directions include using advanced manifold clustering algorithms to increase the robustness towards the high nonlinearity in the EEG feature space and the embedded space, as well as allowing the mapping from multiple clusters to one vigilance level.

关键词： Manifolds Electroencephalography Time factors Clustering algorithms Feature extraction Estimation data structures

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Efficient compositing strategies for automotive HMI systems

Efficient compositing strategies for automotive HMI systems

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international Symposium on Industrial Embedded Systems

作者： Simon Gansel Stephan Schnitzer Riccardo Cecolin Frank Dürr Kurt Rothermel Christian Maihöfer System Architecture and Platforms Department Mercedes-Benz Cars Division Daimler AG Germany Institute of Parallel and Distributed Systems University of Stuttgart Germany

the relevance of graphical functions in vehicular applications has increased significantly during the last years. Modern cars are equipped with multiple displays used by different applications such as speedometer, navigation system, or media players. the rendered output of the applications is stored in so-called off-screen buffers and then bitblitted to the screen buffer at the respective window sizes and positions. To guarantee the visibility of the potentially overlapping windows, the compositing has to match the z-order of the windows. To this end, two common compositing strategies Tile compositing and Full compositing are used, each having performance issues depending on how windows overlap. Since automotive embedded platforms are restricted in power consumption, installation space, and hardware cost, their performance is limited which effectuates the need for highly efficient bitblitting. In order to increase the performance in compositing the windows, we propose Hybrid Compositing which predicts the required bitblitting time and chooses the most efficient strategy for each pair of overlapping windows. Using various scenarios we show that our approach is faster than the other strategies. In addition, we propose CacheHybrid Compositing which reduces the CPU execution time of our approach by up to 66 %. In case of an automotive scenario we show that our optimized approach saves up to 51% bitblitting time compared to existing approaches.

关键词： data structures Hardware Libraries Graphics Graphics processing units Predictive models Prediction algorithms

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Knowledge Extraction algorithms Dedicated for Identification of Steel Bars in Reinforced Concrete structures

Knowledge Extraction Algorithms Dedicated for Identification...

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10th international Conference on Barkhausen and Micro-Magnetics (ICBM)

作者： Frankowski, P. K. Chady, T. Sikora, R. West Pomeranian Univ Tech Dept Elect & Comp Engn PL-70313 Szczecin Poland

ISBN: (纸本)9780735412125

the purpose of this paper is to present knowledge extraction algorithms, dedicated for new electromagnetic system used to evaluate steel bars in reinforced concrete structures. All stages of the rebar identification process have been presented. At the first step, relations between parameters of the tested structure and measured waveform are extracted. For this purpose, a dedicated association rules learning algorithm is proposed. In the next stage, the collected data are filtered and smoothed. Finally, classification models are implemented, tested and evaluated. the experimental verification of the applied techniques was carried out, and the selected results are presented.

关键词： Eddy Current Testing Reinforced Concrete Structure data Analysis data Mining

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Exploiting the structures of the U-Matrix

Exploiting the Structures of the U-Matrix

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10th international workshop on Self-Organizing Maps (WSOM)

作者： Loetsch, Joern Ultsch, Alfred Goethe Univ Inst Clin Pharmacol Theodor Stern Kai 7 D-60590 Frankfurt Germany Fraunhofer Inst Mol Biol & Appl Ecol Translat Med & Pharmacol IME TMP Project Grp D-60590 Frankfurt Germany Univ Marburg DataBion Res Grp D-35032 Marburg Germany

ISBN: (纸本)9783319076959;9783319076942

the U-matrix has become a standard visualization of self-organizing feature maps (SOM). Here we present the abstract U-matrix, which formalizes the structures on a U-matrix such that distance calculations between best-matching units w.r.t. the height structures of a U-matrix are precisely defined (U-cell distance). this enables the assessment of the topological correctness of the SOM and the implementation of clustering algorithms that take the structures seen on the U-matrix into account. A weighted Delaunay graph of the U-cell distances allows the calculation of a dendrogram corresponding to the structures of the U-matrix. the method is shown to detect and visualize meaningful cluster structures on difficult artificial and real-life data.

关键词： Self organizing maps

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Progress on the Development of Automated data Analysis algorithms and Software for Ultrasonic Inspection of Composites

Progress on the Development of Automated Data Analysis Algor...

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10th international Conference on Barkhausen and Micro-Magnetics (ICBM)

作者： Aldrin, John C. Coughlin, Chris Forsyth, David S. Welter, John T. Air Force Res Lab AFRL RXCA Wright Patterson AFB OH 45433 USA Computat Tool Gurnee IL 60031 USA TRI Austin Austin TX 78746 USA

ISBN: (纸本)9780735412125

Progress is presented on the development and implementation of automated data analysis (ADA) software to address the burden in interpreting ultrasonic inspection data for large composite structures. the automated data analysis algorithm is presented in detail, which follows standard procedures for analyzing signals for time-of-flight indications and backwall amplitude dropout. ADA processing results are presented for test specimens that include inserted materials and discontinuities produced under poor manufacturing conditions.

关键词： Automated data Analysis Composites Image Processing Ultrasonic NDE

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Partial Mutual Information for Classification of Gene Expression data by Learning Vector Quantization

Partial Mutual Information for Classification of Gene Expres...

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10th international workshop on Self-Organizing Maps (WSOM)

作者： Lange, Mandy Nebel, David Villmann, thomas Univ Appl Sci Mittweida Dept Math Mittweida Germany

ISBN: (纸本)9783319076959;9783319076942

Gene expression data analysis is frequently performed using correlation measures whereas unsupervised and supervised vector quantization methods are usually designed for Euclidean distances. In this paper we summarize recent approaches to apply correlation measures to those vector quantization algorithms for analysis of microarray gene expression data. Additionally, we consider k-th order partial correlations as a natural extension if pseudo-correlations should be avoided. Further, we draw the focus to mutual information as powerful alternatives to correlation measures. Related to this we provide the concept of k-th order partial mutual information as counterpart to partial correlations. We apply these methods to an exemplary but real classification problem in gene expression analysis for detection of diabetic patients.

关键词： Classification (of information)

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Scheduling for SPARQL endpoints 10

Scheduling for SPARQL endpoints

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10th international workshop on Scalable Semantic Web Knowledge Base Systems, SSWS 2014, Co-Located with 13th international Semantic Web Conference, ISWC 2014

作者： Maali, Fadi Hassan, Islam A. Decker, Stefan Insight Centre for Data Analytics National University of Ireland Galway Ireland

When providing public access to data on the Semantic Web, publishers have various options that include downloadable dumps, Web APIs, and SPARQL endpoints. Each of these methods is most suitable for particular scenarios. SPARQL provides the richest access capabilities and is the most suitable option when granular access to the data is needed. However, SPARQL expressivity comes at the expense of high evaluation cost. the potentially large variance in the cost of different SPARQL queries makes guaranteeing consistently good quality of service a very difficult task. Current practices to enhance the reliability of SPARQL endpoints, such as query timeouts and limiting the number of results returned, are far from ideal. they can result in under utilisation of resources by rejecting some queries even when the available resources are sitting idle and they do not isolate "well-behaved" users from "ill-behaved" ones and do not ensure fair sharing among different users. In similar scenarios, where unpredictable contention for resources exists, scheduling algorithms have proven to be effective and to significantly enhance the allocation of resources. To the best of our knowledge, using scheduling algorithms to organise query execution at SPARQL endpoints has not been studied. In this paper, we study, and evaluate through simulation, the applicability of a few algorithms to scheduling queries received at a SPARQL endpoint.

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Algebra and Geometry Combined Explains How the Mind Does Math 1

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10th international Conference on Parallel Processing and Applied Mathematics (PPAM)

作者： Gustavson, Fred G. IBM Corp TJ Watson Res Ctr Ossining NY USA

ISBN: (数字)9783642552243

ISBN: (纸本)9783642552243

this paper updates my talk on Cache Blocking for Dense Linear algorithms since 1985 given at PPAM 11;see [11]. We again apply Dimension theory to matrices in the Fortran and C programming languages. New data structures (NDS) for matrices are given. We use the GCD algorithm to transpose a n by m matrix A in CMO order, standard layout, in-place. Algebra and Geometry are used to make this idea concrete and practical;it is the reason for title of our paper: make a picture of any matrix by the GCD algorithm to convert it into direct sum of square submatrices. the picture is Geometry and the GCD algorithm is Algebra. Also, the in-place transposition of the GKK and TT algorithms will be compared. Finally, the importance of using negative integers will be used to give new results about subtraction and finding primitive roots which also make a priori in-place transpose more efficient.

关键词： New data structures (NDS) In-place matrix transposition Cache blocking Dimension theory Negative integers

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the Sky Is Not the Limit

The Sky Is Not the Limit

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10th international workshop on Self-Organizing Maps (WSOM)

作者： Merenyi, Erzsebet Rice Univ Dept Stat 6100 Main St MS-138 Houston TX 77005 USA Rice Univ Dept Elect & Comp Engn Houston TX 77005 USA

ISBN: (纸本)9783319076959;9783319076942

We live in the era of Big data, or at least our awareness of Big data's presence and impact has sharpened in the past ten years. Compared to data characteristics decades ago, Big data not only means a deluge of unfiltered bytes, but even more importantly it represents a dramatic increase in data dimensionality (the number of variables) and complexity (the relationships among the often interdependent variables, intricacy of cluster structure). Along with the opportunities for nuanced understanding of processes and for decision making, these data created new demands for information extraction methods in terms of the detail that is expected to be identified in analysis tasks such as clustering, classification, regression, and parameter inference. Many traditionally favored techniques do not meet these challenges if one's aim is to fully exploit the rich information captured by sophisticated sensors and other automated data collection techniques, to ensure discovery of surprising small anomalies, discriminate important, subtle differences, and more. A flurry of technique developments has been spawned, many augmenting existing algorithms with increasingly complex features. © Springer international Publishing Switzerland 2014.

关键词： Big data

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