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database Systems for Advanced Applications 1

丛书名： Lecture Notes in Computer science

1000年

作者： Weiyi Meng Ling Feng Stéphane Bressan Werner Winiwarter Wei Song

ISBN: (数字)9783642374876

ISBN: (纸本)9783642374869

This two volume set LNCS 7825 and LNCS 7826 constitutes the refereed proceedings of the 18th International Conference on database Systems for Advanced Applications, DASFAA 2013, held in Wuhan, China, in April 2013. The 51 revised full papers and 10 short papers presented together with 2 invited keynote talks, 1 invited paper, 3 industrial papers, 9 demo presentations, 4 tutorials and 1 panel paper were carefully reviewed and selected from a total of 227 submissions. The topics covered in part 1 are social networks; query processing; nearest neighbor search; index; query analysis; XML data management; privacy protection; and uncertain data management; and in part 2: graph data management; physical design; knowledge management; temporal data management; social networks; query processing; data mining; applications; and database applications.

关键词： database Management data Mining and Knowledge Discovery Information Storage and Retrieval Information Systems Applications (incl. Internet) Multimedia Information Systems User Interfaces and Human Computer Interaction

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PraK Tool: An Interactive Search Tool Based on Video data Services 30th

PraK Tool: An Interactive Search Tool Based on Video Data S...

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Lokoč, Jakub Vopálková, Zuzana Stroh, Michael Buchmueller, Raphael Schlegel, Udo SIRET Research Group Department of Software Engineering Faculty of Mathematics and Physics Charles University Prague Czech Republic Visual Computing Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany Data Analysis and Visualization Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany

ISBN: (纸本)9783031533013

This paper presents a tool relying on data service architecture, where technical details of all VBS datasets are completely hidden behind an abstract stateless data layer. The data services allow independent development of interactive search interfaces and refinement techniques, which is demonstrated by a smart front-end component. The component supports common search features and allows users to exploit content-based statistics for effective filtering. We believe that video data services might be a valuable addition to the open-source VBS toolkit, especially when available for the competition on a shared server with all VBS datasets, extracted features, and meta-data behind. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Video recording

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Artificial intelligence for advanced functional materials: exploring current and future directions

JPhys Materials

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JPhys Materials 2025年第2期8卷

作者： Malica, Cristiano Novoselov, Kostya S Barnard, Amanda S Kalinin, Sergei V Spurgeon, Steven R Reuter, Karsten Alducin, Maite Deringer, Volker L Csányi, Gábor Marzari, Nicola Huang, Shirong Cuniberti, Gianaurelio Deng, Qiushi Ordejón, Pablo Cole, Ivan Choudhary, Kamal Hippalgaonkar, Kedar Zhu, Ruiming von Lilienfeld, O Anatole Hibat-Allah, Mohamed Carrasquilla, Juan Cisotto, Giulia Zancanaro, Alberto Wenzel, Wolfgang Ferrari, Andrea C Ustyuzhanin, Andrey Roche, Stephan Bremen Center for Computational Materials Science MAPEX Center for Materials and Processes University of Bremen BremenD-28359 Germany Institute for Functional Intelligent Materials National University of Singapore Singapore117544 Singapore Department of Materials Science and Engineering National University of Singapore Singapore117575 Singapore School of Computing Australian National University Acton Australia Department of Materials Science and Engineering University of Tennessee KnoxvilleTN United States Pacific Northwest National Laboratory RichlandWA United States National Renewable Energy Laboratory GoldenCO United States University of Colorado BoulderCO United States Fritz-Haber-Institut der Max-Planck-Gesellschaft Berlin Germany Donostia-San Sebastián Spain Donostia International Physics Center Donostia-San Sebastián Spain Inorganic Chemistry Laboratory Department of Chemistry University of Oxford Oxford United Kingdom Engineering Laboratory University of Cambridge Cambridge United Kingdom Lausanne Switzerland Institute for Materials Science Max Bergmann Center for Biomaterials TUD Dresden University of Technology Dresden Germany School of Engineering RMIT University Melbourne Australia Campus UAB Bellaterra Spain School of Engineering Australia National University Acton Australia Materials Science and Engineering Division National Institute of Standards and Technology GaithersburgMD United States School of Materials Science and Engineering Nanyang Technological University Singapore639798 Singapore Singapore138634 Singapore Departments of Chemistry Materials Science & Engineering Physics and the Acceleration Consortium University of Toronto Canada Vector Institute TorontoON Canada ML Group Technische Universität Berlin Institute for the Foundations of Learning and Data Berlin Germany Department of Applied Mathematics University of Waterloo WaterlooON Canada Institute for Theoretical Physics ETH Zürich Switzerland Depar

This perspective addresses the topic of harnessing the tools of artificial intelligence (AI) for boosting innovation in functional materials design and engineering as well as discovering new materials for targeted applications in energy storage, biomedicine, composites, nanoelectronics or quantum technologies. It gives a current view of experts in the field, insisting on challenges and opportunities provided by the development of large materials databases, novel schemes for implementing AI into materials production and characterization as well as progress in the quest of simulating physical and chemical properties of realistic atomic models reaching the trillion atoms scale and with near ab initio accuracy. © 2025 The Author(s). Published by IOP Publishing Ltd.

关键词： Yarn

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Diffusion Models for Graphs Benefit From Discrete State Spaces

arXiv

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arXiv 2022年

作者： Haefeli, Kilian Konstantin Martinkus, Karolis Perraudin, Nathanaël Wattenhofer, Roger ETH Zurich Switzerland Distributed Computing Group ETH Zurich Switzerland Swiss Data Science Center ETH Zurich Switzerland

Denoising diffusion probabilistic models and score-matching models have proven to be very powerful for generative tasks. While these approaches have also been applied to the generation of discrete graphs, they have, so far, relied on continuous Gaussian perturbations. Instead, in this work, we suggest using discrete noise for the forward Markov process. This ensures that in every intermediate step the graph remains discrete. Compared to the previous approach, our experimental results on four datasets and multiple architectures show that using a discrete noising process results in higher quality generated samples indicated with an average MMDs reduced by a factor of 1.5. Furthermore, the number of denoising steps is reduced from 1000 to 32 steps, leading to a 30 times faster sampling procedure. © 2022, CC BY.

关键词： Markov processes

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Typicality Distribution Function - A New Density-based data Analytics Tool

Typicality Distribution Function - A New Density-based Data ...

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International Joint Conference on Neural Networks

作者： Plamen Angelov Data Science Group School of Computing and Communications Lancaster University Lancaster LA1 4WA UK

ISBN: (纸本)9781479919611

In this paper a new density-based, non-frequentistic data analytics tool, called typicality distribution function (TDF) is proposed. It is a further development of the recently introduced typicality- and eccentricity-based data analytics (TEDA) framework. The newly introduced TDF and its standardized form offer an effective alternative to the widely used probability distribution function (pdf), however, remaining free from the restrictive assumptions made and required by the latter. In particular, it offers an exact solution for any (except a single point) amount of non-coinciding data samples. For a comparison, that the well developed and widely used traditional probability theory and related statistical learning approaches require (theoretically) an infinitely large amount of data samples/ observations, although, in practice this requirement is often ignored. Furthermore, TDF does not require the user to preselect or assume a particular distribution (e.g. Gaussian or other) or a mixture of such distributions or to pre-define the number of such distributions in a mixture. In addition, it does not require the individual data items to be independent. At the same time, the link with the traditional statistical approaches such as the well-known "no" analysis, Chebyshev inequality, etc. offers the interesting conclusion that without the restrictive prior assumptions listed above to which these traditional approaches are tied up the same type of analysis can be made using TDF automatically. TDF can provide valuable information for analysis of extreme processes, fault detection and identification were the amount of observations of extreme events or faults is usually disproportionally small. The newly proposed TDF offers a non-parametric, closed form analytical (quadratic) description extracted from the real data realizations exactly in contrast to the usual practice where such distributions are being pre-assumed or approximated. For example, so called particle filters are

关键词： TEDA typicality eccentricity data density pdf non-parametric data distributions

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Τ-FPL: Tolerance-constrained learning in linear time 32

Τ-FPL: Tolerance-constrained learning in linear time

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32nd AAAI Conference on Artificial Intelligence, AAAI 2018

作者： Zhang, Ao Li, Nan Pu, Jian Wang, Jun Yan, Junchi Zha, Hongyuan Shanghai Key Laboratory of Trustworthy Computing MOE International Joint Lab of Trustworthy Software School of Computer Science and Software Engineering East China Normal University Shanghai China Institute of Data Science and Technologies Alibaba Group Hangzhou China IBM Research China Georgia Institute of Technology Atlante United States

ISBN: (纸本)9781577358008

In many real-world applications, learning a classifier with false-positive rate under a specified tolerance is appealing. Existing approaches either introduce prior knowledge dependent label cost or tune parameters based on traditional classifiers, which are of limitation in methodology since they do not directly incorporate the false-positive rate tolerance. In this paper, we propose a novel scoring-thresholding approach, τ-False Positive Learning (τ-FPL) to address this problem. We show that the scoring problem which takes the false-positive rate tolerance into accounts can be efficiently solved in linear time, also an out-of-bootstrap thresholding method can transform the learned ranking function into a low false-positive classifier. Both theoretical analysis and experimental results show superior performance of the proposed τ-FPL over the existing approaches. Copyright © 2018, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Mathematical transformations

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Categorization of computing education resources with utilization of crowdsourcing 12

Categorization of computing education resources with utiliza...

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12th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL '12

作者： Chen, Yinlin Bogen II, Paul Logasa Hsieh, Haowei Fox, Edward A. Cassel, Lillian N. Department of Computer Science Virginia Tech. Blacksburg VA United States Intelligent Computing Research Team Computational Data Analytics Group Oak Ridge National Laboratory Oak Ridge TN 37931 United States School of Library and Information Science University of Iowa Iowa City IA 52242 United States Department of Computing Sciences Villanova University Villanova PA 19085 United States

ISBN: (纸本)9781450311540

The Ensemble Portal harvests resources from multiple heterogeneous federated collections. Managing these dynamically increasing collections requires an automatic mechanism to categorize records in to corresponding topics. We propose an approach to use existing ACM DL metadata to build classifiers for harvested resources in the Ensemble project. We also present our experience with utilizing the Amazon Mechanical Turk platform to build ground truth training data sets from Ensemble collections. © 2012 ACM.

关键词： Digital libraries

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Large strain constitutive modelling of soft compressible and incompressible solids: Generalised isotropic and anisotropic viscoelasticity

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Journal of the Mechanics and Physics of Solids 2025年

作者： Zeng Liu Rogelio Ortigosa Antonio J. Gil Javier Bonet Centre Internacional de Mètodes Numèrics en Enginyeria (CIMNE) Barcelona Spain Departament de Enginyeria Civil i Ambiental (DECA) Universitat Politècnica de Catalunya Barcelona Spain Computational Mechanics and Scientific Computing Group Technical University of Cartagena Campus Muralla del Mar 30202 Cartagena (Murcia) Spain Zienkiewicz Institute for Modelling Data and AI Faculty of Science and Engineering Swansea University Bay Campus SA1 8EN United Kingdom

This paper discusses a new phenomenological continuum formulation for the constitutive modelling of viscoelastic materials at large strains. Following pioneering works in Sidoroff (1974), Lubliner (1985), Bergström (1998) and Reese and Govindjee (1997), the formulation shares some common ingredients with other phenomenological approaches, including the multiplicative decomposition of the deformation gradient into viscous and elastic contributions, the additive Maxwell-type decomposition of the strain energy density, and the definition of a set of kinematic internal state variables with their associated evolution laws. Our formulation departs from other state-of-the-art methodologies via three distinct novelties. First, and revisiting previous work by Bonet (2001), the paper introduces a thermodynamically consistent linear rate type evolution law in terms of stress-type variables, which resembles the return mapping algorithm typically used in elastoplasticity, facilitating the modelling link between both inelastic constitutive models. In this sense, the proposed viscoelastic evolution law can be identified with a classical plastic flow rule. Very importantly, the evolution law is shown to be compatible with the second law of thermodynamics by construction and have a closed-form solution in the case of incompressible viscoelasticity when using a prototypical neo-Hookean type of non-equilibrium strain energy density. Moreover, the paper shows how using the concept of a stress-driven dissipative potential, more general non-linear type of stress evolution laws can be straightforwardly constructed. Second, to facilitate the joint consideration of anisotropy and thermodynamic equilibrium, a frame indifferent stress free configuration is introduced which facilitates the definition of objective strain measures. Third, the methodology is extended from isotropy to transverse isotropy via the consideration of the appropriate structural tensor. The formulation is first displayed

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A hybrid method for word segmentation with English-Vietnamese bilingual text

A hybrid method for word segmentation with English-Vietnames...

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2nd International Conference on Control, Automation and Information sciences, ICCAIS 2013

作者： Ngo, Quoc Hung Dien, Dinh Winiwarter, Werner Computer Science Faculty University of Information Technology HoChiMinh City Viet Nam Faculty of Information Technology University of Sciences HoChiMinh City Viet Nam University of Vienna Research Group Data Analytics and Computing Währinger Straße 29 1090 Wien Austria

ISBN: (纸本)9781479905720

This paper proposes a hybrid approach for Vietnamese word segmentation. The approach combines a dictionary-based method and a machine learning method to detect word boundaries in Vietnamese text by comparing English-Vietnamese pairs. We also point out several characteristics of Vietnamese which affect the Vietnamese word segmentation task and word alignment of English-Vietnamese text. Moreover, we built an English-Vietnamese bilingual corpus with nearly 10 million words, namely EVBCorpus, while a part of EVBNews has been manually segmented at the word level. We evaluate the performance of our approach by comparing its word segmentation results on this corpus. Our hybrid approach achieves 97% accuracy on the EVBNews corpus. © 2013 IEEE.

关键词： Learning systems

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On the Mental Fatigue Analysis of SSVEP Entrainment

On the Mental Fatigue Analysis of SSVEP Entrainment

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International Conference on BioSignal Analysis, Processing and Systems (ICBAPS)

作者： Ramaswamy Palaniappan Surej Mouli Tugce Balli Ian McLoughlin Data Science Research Group School of Computing University of Kent Chatham UK Dept of Software Engineering Uskudar University Istanbul Turkey

Steady state visual evoked potential (SSVEP) has been employed significantly in various electrophysiological studies, most notably in the brain-computer interface (BCI) designs. While the standard approach of generating SSVEP is to use a 50% duty cycle pulse width modulation, i.e. with similar on and off periods of flashing, recent advancements in LED technology have allowed higher duty cycles such as 85% to be generated. While higher duty cycles are more successful in the entrainment and hence the improved response, it is unclear whether mental fatigue is also affected by a higher duty cycle. In this paper, we evaluate mental fatigue using a beta band energy analysis of electroencephalogram in five users while they visualise conventional 50% duty cycle SSVEP and 85% duty cycle SSVEP. Our results indicate that mental fatigue reduces with increased duty cycle and also confirms the comfort ratings by subjects, who report feeling more comfortable with the higher duty cycle.

关键词： Electroencephalography Visualization Fatigue Brain-computer interfaces Light emitting diodes Steady-state Pulse width modulation

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