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  • Articles  (121,029)
  • 2015-2019  (121,029)
  • Computer Science  (121,029)
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  • Books  (79)
  • Articles  (121,029)
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  • 1
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    A Methodology for Operationalizing Enterprise IT Architecture and Evaluating its Modifiability (2019)
    Riga Technical University
    Details
    Publication Date: 2019-07-19
    Electronic ISSN: 2255-9922
    Topics: Computer Science
    Published by Riga Technical University
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  • 2
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    An optimization-based approach for efficient network monitoring using in-band network telemetry (2019)
    Springer
    Details
    Publication Date: 2019-06-25
    Print ISSN: 1867-4828
    Electronic ISSN: 1869-0238
    Topics: Computer Science
    Published by Springer
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  • 3
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    A semantic-based discovery service for the Internet of Things (2019)
    Springer
    Details
    Publication Date: 2019-05-15
    Print ISSN: 1867-4828
    Electronic ISSN: 1869-0238
    Topics: Computer Science
    Published by Springer
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  • 4
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    GARSAaaS: group activity recognition and situation analysis as a service (2019)
    Springer
    Details
    Publication Date: 2019-03-01
    Print ISSN: 1867-4828
    Electronic ISSN: 1869-0238
    Topics: Computer Science
    Published by Springer
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  • 5
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    Better safe than sorry: a vehicular traffic re-routing based on traffic conditions and public safety issues (2019)
    Springer
    Details
    Publication Date: 2019-09-11
    Description: Vehicular traffic re-routing is the key to provide better traffic mobility. However, taking into account just traffic-related information to recommend better routes for each vehicle is far from achieving the desired requirements of proper transportation management. In this way, context-aware and multi-objective re-routing approaches will play an important role in traffic management. Yet, most procedures are deterministic and cannot support the strict requirements of traffic management applications, since many vehicles potentially will take the same route, consequently degrading overall traffic efficiency. So, we propose an efficient algorithm named as Better Safe Than Sorry (BSTS), based on Pareto-efficiency. Simulation results have shown that our proposal provides a better trade-off between mobility and safety than state-of-the-art approaches and also avoids the problem of potentially creating different congestion spots.
    Print ISSN: 1867-4828
    Electronic ISSN: 1869-0238
    Topics: Computer Science
    Published by Springer
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  • 6
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    DG2CEP: a near real-time on-line algorithm for detecting spatial clusters large data streams through complex event processing (2019)
    Springer
    Details
    Publication Date: 2019-04-15
    Print ISSN: 1867-4828
    Electronic ISSN: 1869-0238
    Topics: Computer Science
    Published by Springer
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  • 7
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    A participatory sensing framework to classify road surface quality (2019)
    Springer
    Details
    Publication Date: 2019-07-09
    Print ISSN: 1867-4828
    Electronic ISSN: 1869-0238
    Topics: Computer Science
    Published by Springer
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  • 8
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    Evaluating CRoS-NDN: a comparative performance analysis of a controller-based routing scheme for named-data networking (2019)
    Springer
    Details
    Publication Date: 2019-11-09
    Print ISSN: 1867-4828
    Electronic ISSN: 1869-0238
    Topics: Computer Science
    Published by Springer
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  • 9
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    SCOPE: self-adaptive and policy-based data management middleware for federated clouds (2019)
    Springer
    Details
    Publication Date: 2019-01-30
    Print ISSN: 1867-4828
    Electronic ISSN: 1869-0238
    Topics: Computer Science
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  • 10
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    Clustering and reliability-driven mitigation of routing attacks in massive IoT systems (2019)
    Springer
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    Publication Date: 2019-09-06
    Print ISSN: 1867-4828
    Electronic ISSN: 1869-0238
    Topics: Computer Science
    Published by Springer
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  • 11
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    Low-rank Riemannian eigensolver for high-dimensional Hamiltonians (2019)
    Elsevier
    Details
    Publication Date: 2019
    Description: 〈p〉Publication date: Available online 8 July 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics〈/p〉 〈p〉Author(s): Maxim Rakhuba, Alexander Novikov, Ivan Oseledets〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Such problems as computation of spectra of spin chains and vibrational spectra of molecules can be written as 〈em〉high-dimensional eigenvalue problems〈/em〉, i.e., when the eigenvector can be naturally represented as a multidimensional tensor. Tensor methods have proven to be an efficient tool for the approximation of solutions of high-dimensional eigenvalue problems, however, their performance deteriorates quickly when the number of eigenstates to be computed increases. We address this issue by designing a new algorithm motivated by the ideas of 〈em〉Riemannian optimization〈/em〉 (optimization on smooth manifolds) for the approximation of multiple eigenstates in the 〈em〉tensor-train format〈/em〉, which is also known as matrix product state representation. The proposed algorithm is implemented in TensorFlow, which allows for both CPU and GPU parallelization.〈/p〉〈/div〉
    Print ISSN: 0021-9991
    Electronic ISSN: 1090-2716
    Topics: Computer Science , Physics
    Published by Elsevier
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  • 12
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    An interior penalty discontinuous Galerkin approach for 3D incompressible Navier–Stokes equation for permeability estimation of porous media (2019)
    Elsevier
    Details
    Publication Date: 2019
    Description: 〈p〉Publication date: Available online 8 July 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics〈/p〉 〈p〉Author(s): Chen Liu, Florian Frank, Faruk O. Alpak, Béatrice Rivière〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Permeability estimation of porous media from directly solving the Navier–Stokes equations has a wide spectrum of applications in petroleum industry. In this paper, we utilize a pressure-correction projection algorithm in conjunction with the interior penalty discontinuous Galerkin scheme for space discretization to build an incompressible Navier–Stokes simulator and to use this simulator to calculate permeability of real rock samples. The proposed method is accurate, numerically robust, and exhibits the potential for tackling realistic problems.〈/p〉〈/div〉
    Print ISSN: 0021-9991
    Electronic ISSN: 1090-2716
    Topics: Computer Science , Physics
    Published by Elsevier
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  • 13
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    A partition of unity finite element method for three-dimensional transient diffusion problems with sharp gradients (2019)
    Elsevier
    Details
    Publication Date: 2019
    Description: 〈p〉Publication date: Available online 8 July 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics〈/p〉 〈p〉Author(s): Mustapha Malek, Nouh Izem, M. Shadi Mohamed, Mohammed Seaid, Omar Laghrouche〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉An efficient partition of unity finite element method for three-dimensional transient diffusion problems is presented. A class of multiple exponential functions independent of time variable is proposed to enrich the finite element approximations. As a consequence of this procedure, the associated matrix for the linear system is evaluated once at the first time step and the solution is obtained at subsequent time step by only updating the right-hand side of the linear system. This results in an efficient numerical solver for transient diffusion equations in three space dimensions. Compared to the conventional finite element methods with 〈em〉h〈/em〉-refinement, the proposed approach is simple, more efficient and more accurate. The performance of the proposed method is assessed using several test examples for transient diffusion in three space dimensions. We present numerical results for a transient diffusion equation with known analytical solution to quantify errors for the new method. We also solve time-dependent diffusion problems in complex geometries. We compare the results obtained using the partition of unity finite element method to those obtained using the standard finite element method. It is shown that the proposed method strongly reduces the necessary number of degrees of freedom to achieve a prescribed accuracy.〈/p〉〈/div〉
    Print ISSN: 0021-9991
    Electronic ISSN: 1090-2716
    Topics: Computer Science , Physics
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  • 14
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    A new numerical method for solving the Boltzmann transport equation using the PN method and the discontinuous finite elements on unstructured and curved meshes (2019)
    Elsevier
    Details
    Publication Date: 2019
    Description: 〈p〉Publication date: Available online 8 July 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics〈/p〉 〈p〉Author(s): Lahbib Bourhrara〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This document presents a new numerical scheme dealing with the Boltzmann transport equation. This scheme is based on the expansion of the angular flux in a truncated spherical harmonics function and the discontinuous finite element method for the spatial variable. The advantage of this scheme lies in the fact that we can deal with unstructured, non-conformal and curved meshes. Indeed, it is possible to deal with distorted regions whose boundary is constituted by edges that can be either line segments or circular arcs or circles. In this document, we detail the derivation of the method for 2D geometries. However, the generalization to 2D extruded geometries is trivial.〈/p〉〈/div〉
    Print ISSN: 0021-9991
    Electronic ISSN: 1090-2716
    Topics: Computer Science , Physics
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  • 15
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    Future Internet, Vol. 11, Pages 152: A Next-Generation Core Network Architecture for Mobile Networks (2019)
    MDPI
    Details
    Publication Date: 2019
    Description: Over the years, the cellular mobile network has evolved from a wireless plain telephone system to a very complex system providing telephone service, Internet connectivity and many interworking capabilities with other networks. Its air interface performance has increased drastically over time, leading to high throughput and low latency. Changes to the core network, however, have been slow and incremental, with increased complexity worsened by the necessity of backwards-compatibility with older-generation systems such as the Global System for Mobile communication (GSM). In this paper, a new virtualized Peer-to-Peer (P2P) core network architecture is presented. The key idea of our approach is that each user is assigned a private virtualized copy of the whole core network. This enables a higher degree of security and novel services that are not possible in today’s architecture. We describe the new architecture, focusing on its main elements, IP addressing, message flows, mobility management, and scalability. Furthermore, we will show some significant advantages this new architecture introduces. Finally, we investigate the performance of our architecture by analyzing voice-call traffic available in a database of a large U.S. cellular network provider.
    Electronic ISSN: 1999-5903
    Topics: Computer Science
    Published by MDPI
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  • 16
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    Master Recession Curve Parameterization Tool (MRCPtool): Different approaches to recession curve analysis (2019)
    Elsevier
    Details
    Publication Date: 2019
    Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Computers & Geosciences, Volume 132〈/p〉 〈p〉Author(s): T. Carlotto, P.L.B. Chaffe〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Streamflow recession analysis is crucial for understanding how catchments release water in periods of drought and therefore is important for water resources planning and management. Despite there being several theories on how to model recession curves, few studies compare the different approaches to that problem. In this work, we developed the Master Recession Curve Parameterization tool (MRCPtool), which brings together a set of automated methods for the analysis of recession periods based only on streamflow data. The methods include: (i) hydrograph separation using numerical filters; (ii) automatic extraction of recession periods; (iii) creation of the MRC with the matching strip method; (iv) creation of the MRC for different flow classes defined from the flow duration curve; (v) analysis of flow recession rates 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" altimg="si1.svg"〉〈mrow〉〈mo〉(〈/mo〉〈mo linebreak="goodbreak" linebreakstyle="after"〉−〈/mo〉〈mi〉d〈/mi〉〈mi〉Q〈/mi〉〈mo〉∕〈/mo〉〈mi〉d〈/mi〉〈mi〉t〈/mi〉〈mo〉)〈/mo〉〈/mrow〉〈/math〉 as a function of flow 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" altimg="si2.svg"〉〈mrow〉〈mo〉(〈/mo〉〈mi〉Q〈/mi〉〈mo〉)〈/mo〉〈/mrow〉〈/math〉 and (vi) creation of the MRC from simulated recession curves with different analytical approaches, including linear and nonlinear models. The MRCPtool contains a graphical user interface developed in MATLAB software that facilitates the analysis of streamflow datasets. Finally, we present an example application of the MRCPtool using a streamflow dataset of 44 years. The MRCPtool is an open source tool that can be downloaded from the site: 〈a href="http://www.labhidro.ufsc.br/static/software/MRCPtool.rar" target="_blank"〉http://www.labhidro.ufsc.br/static/software/MRCPtool.rar〈/a〉.〈/p〉〈/div〉
    Print ISSN: 0098-3004
    Electronic ISSN: 1873-7803
    Topics: Geosciences , Computer Science
    Published by Elsevier
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  • 17
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    Semi-supervised machine-learning classification of materials synthesis procedures (2019)
    Details
    Publication Date: 2019
    Electronic ISSN: 2057-3960
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
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  • 18
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    Future Internet, Vol. 11, Pages 151: Structuring Reference Architectures for the Industrial Internet of Things (2019)
    MDPI
    Details
    Publication Date: 2019
    Description: The ongoing digital transformation has the potential to revolutionize nearly all industrial manufacturing processes. However, its concrete requirements and implications are still not sufficiently investigated. In order to establish a common understanding, a multitude of initiatives have published guidelines, reference frameworks and specifications, all intending to promote their particular interpretation of the Industrial Internet of Things (IIoT). As a result of the inconsistent use of terminology, heterogeneous structures and proposed processes, an opaque landscape has been created. The consequence is that both new users and experienced experts can hardly manage to get an overview of the amount of information and publications, and make decisions on what is best to use and to adopt. This work contributes to the state of the art by providing a structured analysis of existing reference frameworks, their classifications and the concerns they target. We supply alignments of shared concepts, identify gaps and give a structured mapping of regarded concerns at each part of the respective reference architectures. Furthermore, the linking of relevant industry standards and technologies to the architectures allows a more effective search for specifications and guidelines and supports the direct technology adoption.
    Electronic ISSN: 1999-5903
    Topics: Computer Science
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  • 19
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    Information, Vol. 10, Pages 233: A Novel Approach for Web Service Recommendation Based on Advanced Trust Relationships (2019)
    MDPI
    Details
    Publication Date: 2019
    Description: Service recommendation is one of the important means of service selection. Aiming at the problems of ignoring the influence of typical data sources such as service information and interaction logs on the similarity calculation of user preferences and insufficient consideration of dynamic trust relationship in traditional trust-based Web service recommendation methods, a novel approach for Web service recommendation based on advanced trust relationships is presented. After considering the influence of indirect trust paths, the improved calculation about indirect trust degree is proposed. By quantifying the popularity of service, the method of calculating user preference similarity is investigated. Furthermore, the dynamic adjustment mechanism of trust is designed by differentiating the effect of each service recommendation. Integrating these efforts, a service recommendation mechanism is introduced, in which a new service recommendation algorithm is described. Experimental results show that, compared with existing methods, the proposed approach not only has higher accuracy of service recommendation, but also can resist attacks from malicious users more effectively.
    Electronic ISSN: 2078-2489
    Topics: Computer Science
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  • 20
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    Information, Vol. 10, Pages 232: Idempotent Factorizations of Square-Free Integers (2019)
    MDPI
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    Publication Date: 2019
    Description: We explore the class of positive integers n that admit idempotent factorizations n = p ¯ q ¯ such that λ ( n ) ∣ ( p ¯ − 1 ) ( q ¯ − 1 ) , where λ is the Carmichael lambda function. Idempotent factorizations with p ¯ and q ¯ prime have received the most attention due to their cryptographic advantages, but there are an infinite number of n with idempotent factorizations containing composite p ¯ and/or q ¯ . Idempotent factorizations are exactly those p ¯ and q ¯ that generate correctly functioning keys in the Rivest–Shamir–Adleman (RSA) 2-prime protocol with n as the modulus. While the resulting p ¯ and q ¯ have no cryptographic utility and therefore should never be employed in that capacity, idempotent factorizations warrant study in their own right as they live at the intersection of multiple hard problems in computer science and number theory. We present some analytical results here. We also demonstrate the existence of maximally idempotent integers, those n for which all bipartite factorizations are idempotent. We show how to construct them, and present preliminary results on their distribution.
    Electronic ISSN: 2078-2489
    Topics: Computer Science
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  • 21
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    Numerical study of tsunami wave run-up and land inundation on coastal vegetated beaches (2019)
    Elsevier
    Details
    Publication Date: 2019
    Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Computers & Geosciences, Volume 132〈/p〉 〈p〉Author(s): Hongxing Zhang, Mingliang Zhang, Yongpeng Ji, Yini Wang, Tianping Xu〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Vegetation exerts a significant damping effect on tsunami wave run-up on coastal beaches, thus effectively mitigating the tsunami hazard. A depth-integrated two-dimensional numerical model (HydroSed2D, Liu et al., 2008; Liu et al., 2010) is developed to investigate tsunami wave run-up and land inundation on coastal beaches covered with 〈em〉Pandanus odoratissimus〈/em〉 (〈em〉P. odoratissimus〈/em〉). The present model is based on a finite volume Roe-type scheme, that solves the non-linear shallow water equations with the capacity of treating the wet or dry boundary at the wave front. The momentum equations in this model are modified by adding a drag force term, thus considering the resistance effects of vegetation on tsunami waves. The accuracy of the numerical scheme and the vegetation drag force are validated by three experimental cases of dam-break flow propagation in a dry channel, solitary wave propagation in a vegetated flume, and tsunami run-up over an uneven bed. Subsequently, a numerical model is applied to simulate tsunami run-up and land inundation on actual-scale vegetated beaches and a series of sensitive analyses are conducted by comparing numerical results. The obtained numerical results suggest that 〈em〉P. odoratissimus〈/em〉 can effectively attenuate tsunami run-up and land inundation distance on coastal beaches, and a higher attenuation rate for tsunami wave can be achieved by increasing both vegetation width and vegetation density. The tsunami wave height is also an important factor that impacts the tsunami wave run-up and land inundation on vegetated beaches.〈/p〉〈/div〉 〈/div〉
    Print ISSN: 0098-3004
    Electronic ISSN: 1873-7803
    Topics: Geosciences , Computer Science
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  • 22
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    QExpy: A python-3 module to support undergraduate physics laboratories (2019)
    Elsevier
    In: SoftwareX
    Details
    Publication Date: 2019
    Description: 〈p〉Publication date: July–December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 SoftwareX, Volume 10〈/p〉 〈p〉Author(s): R.D. Martin, Q. Cai, T. Garrow, C. Kapahi〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉QExpy is an open source python-3 module that was developed in order to simplify the analysis of data in undergraduate physics laboratories. Through the use of this module, students can focus their time on understanding the science and the data from their experiments, rather than on processing their data. In particular, the module allows users to easily propagate uncertainties from measured quantities using a variety of techniques (derivatives, Monte Carlo), as well as to plot and fit functions to data. The interface is designed to be pedagogical so that students with no prior programming experience can be eased into using python in their introductory physics laboratories.〈/p〉〈/div〉
    Electronic ISSN: 2352-7110
    Topics: Computer Science
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  • 23
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    RTransferEntropy — Quantifying information flow between different time series using effective transfer entropy (2019)
    Elsevier
    In: SoftwareX
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    Publication Date: 2019
    Description: 〈p〉Publication date: July–December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 SoftwareX, Volume 10〈/p〉 〈p〉Author(s): Simon Behrendt, Thomas Dimpfl, Franziska J. Peter, David J. Zimmermann〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper shows how to quantify and test for the information flow between two time series with Shannon transfer entropy and Rényi transfer entropy using the 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" altimg="si1.svg"〉〈mi〉R〈/mi〉〈/math〉 package 〈em〉RTransferEntropy〈/em〉. We discuss the methodology, the bias correction applied to calculate effective transfer entropy and outline how to conduct statistical inference. Furthermore, we describe the package in detail and demonstrate its functionality by means of several simulated processes and present an application to financial time series.〈/p〉〈/div〉
    Electronic ISSN: 2352-7110
    Topics: Computer Science
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  • 24
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    ELEMENTS: A high-order finite element library in C++ (2019)
    Elsevier
    In: SoftwareX
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    Publication Date: 2019
    Description: 〈p〉Publication date: July–December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 SoftwareX, Volume 10〈/p〉 〈p〉Author(s): Jacob L. Moore, Nathaniel R. Morgan, Mark F. Horstemeyer〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We discuss the creation and implementation of a generalized library, named ELEMENTS, of mathematical functions for supporting a very broad range of element types including: linear, quadratic, and cubic serendipity elements in 2D and 3D; high-order spectral elements; and a linear 4D element. The ELEMENTS library can be used for research and development of both continuous and discontinuous finite element methods for solving a diverse range of partial differential equations. The library has functions for calculating quantities that are commonly used in finite element methods such as the gradient of a basis function, the Jacobi matrix, the inverse Jacobi matrix, the determinant of the Jacobi matrix, and a physical position inside the element, to name a few examples. The library also supports both Gauss–Legendre and Gauss–Lobatto quadrature rules up to 8 quadrature points in each coordinate direction. The examples and discussions in this paper will focus on Lagrangian solid mechanics and dynamics, but ELEMENTS can be used for many other applications.〈/p〉〈/div〉
    Electronic ISSN: 2352-7110
    Topics: Computer Science
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  • 25
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    Future Internet, Vol. 11, Pages 148: The Effects of the Floating Action Button on Quality of Experience (2019)
    MDPI
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    Publication Date: 2019
    Description: Google’s Material Design, created in 2014, led to the extended application of floating action buttons (FAB) in user interfaces of web pages and mobile applications. FAB’s roll is to trigger an activity either on the present screen, or it can play out an activity that makes another screen. A few specialists in user experience (UX) and user interface (UI) design are sceptical regarding the usability of FAB in the interfaces of both web pages and mobile applications. They claim that the use of FAB easily distracts users and that it interferes with using other important functions of the applications, and it is unusable in applications designed for iOS systems. The aim of this paper is to investigate by an experiment the quality of experience (QoE) of a static and animated FAB and compare it to the toolbar alternative. The experimental results of different testing methods rejected the hypothesis that the usage and animation of this UI element has a positive influence on the application usability. However, its static and animated utilization enhanced the ratings of hedonic and aesthetic features of the user experience, justifying the usage of this type of button.
    Electronic ISSN: 1999-5903
    Topics: Computer Science
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  • 26
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    Information, Vol. 10, Pages 230: Editorial for the Special Issue on “Modern Recommender Systems: Approaches, Challenges and Applications” (2019)
    MDPI
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    Publication Date: 2019
    Description: Recommender systems are nowadays an indispensable part of most personalized systems implementing information access and content delivery, supporting a great variety of user activities [...]
    Electronic ISSN: 2078-2489
    Topics: Computer Science
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  • 27
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    High-order energy-conserving Line Integral Methods for charged particle dynamics (2019)
    Elsevier
    Details
    Publication Date: 2019
    Description: 〈p〉Publication date: 1 November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Journal of Computational Physics, Volume 396〈/p〉 〈p〉Author(s): Luigi Brugnano, Juan I. Montijano, Luis Rández〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this paper we study arbitrarily high-order energy-conserving methods for simulating the dynamics of a charged particle. They are derived and studied within the framework of 〈em〉Line Integral Methods (LIMs)〈/em〉, previously used for defining 〈em〉Hamiltonian Boundary Value Methods (HBVMs)〈/em〉, a class of energy-conserving Runge-Kutta methods for Hamiltonian problems. A complete analysis of the new methods is provided, which is confirmed by a few numerical tests.〈/p〉〈/div〉
    Print ISSN: 0021-9991
    Electronic ISSN: 1090-2716
    Topics: Computer Science , Physics
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    Information, Vol. 10, Pages 229: Visualization Method for Arbitrary Cutting of Finite Element Data Based on Radial-Basis Functions (2019)
    MDPI
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    Publication Date: 2019
    Description: Finite element data form an important basis for engineers to undertake analysis and research. In most cases, it is difficult to generate the internal sections of finite element data and professional operations are required. To display the internal data of entities, a method for generating the arbitrary sections of finite element data based on radial basis function (RBF) interpolation is proposed in this paper. The RBF interpolation function is used to realize arbitrary surface cutting of the entity, and the section can be generated by the triangulation of discrete tangent points. Experimental studies have proved that the method is very convenient for allowing users to obtain visualization results for an arbitrary section through simple and intuitive interactions.
    Electronic ISSN: 2078-2489
    Topics: Computer Science
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    Evidential positive opinion influence measures for viral marketing (2019)
    Springer
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉The viral marketing is a relatively new form of marketing that exploits social networks to promote a brand, a product, etc. The idea behind it is to find a set of influencers on the network that can trigger a large cascade of propagation and adoptions. In this paper, we will introduce an evidential opinion-based influence maximization model for viral marketing. Besides, our approach tackles three opinion-based scenarios for viral marketing in the real world. The first scenario concerns influencers who have a positive opinion about the product. The second scenario deals with influencers who have a positive opinion about the product and produces effects on users who also have a positive opinion. The third scenario involves influence users who have a positive opinion about the product and produce effects on the negative opinion of other users concerning the product in question. Next, we proposed six influence measures, two for each scenario. We also use an influence maximization model that the set of detected influencers for each scenario. Finally, we show the performance of the proposed model with each influence measure through some experiments conducted on a generated dataset and a real-world dataset collected from Twitter.〈/p〉
    Print ISSN: 0219-1377
    Electronic ISSN: 0219-3116
    Topics: Computer Science
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    Efficient parallel random field generator for large 3-D geophysical problems (2019)
    Elsevier
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    Publication Date: 2019
    Description: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Computers & Geosciences, Volume 131〈/p〉 〈p〉Author(s): Ludovic Räss, Dmitriy Kolyukhin, Alexander Minakov〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We present an efficient implementation of the method for sampling spatial realisations of a 3-D random fields with given power spectrum. The method allows for a multi-scale resolution and approaches well for parallel implementations, overcoming the physical limitation of computer memory when dealing with large 3-D problems. We implement the random field generator to execute on graphical processing units (GPU) using the CUDA C programming language. We compare the memory footprint and the wall-time of our implementation to FFT-based solutions. We illustrate the efficiency of the proposed numerical method using examples of an acoustic scattering problem which can be encountered both in controlled-source and earthquake seismology. In particular, we apply our method to study the scattering of seismic waves in 3-D anisotropic random media with a particular focus on P-wave coda observations and seismic monitoring of hydrocarbon reservoirs.〈/p〉〈/div〉
    Print ISSN: 0098-3004
    Electronic ISSN: 1873-7803
    Topics: Geosciences , Computer Science
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    Science for everyone (ScifE): A proposed framework for science as a service using interactive web technologies (2019)
    Elsevier
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    Publication Date: 2019
    Description: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Computers & Geosciences, Volume 131〈/p〉 〈p〉Author(s): R. Moreno, F.J. Pérez-Gil, J.J. Pardo, A. Navarro, F.J. Tapiador〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Current computing platforms, including HPC, Grid and Cloud computing, offer many computational resources. These platforms can provide the scientific world with on-demand and scalable computing power. However, their use requires advanced computer skills. In our work, we summarize the main problems found by research scientists when they attempt to execute complex software on one or several of these platforms. As a solution to these problems, we propose the 〈em〉Science for Everyone〈/em〉 (ScifE) framework, and an intuitive and easy-to-use web interface, to perform scientific experiments on HPC or Cloud platforms. The Community Earth System Model (CESM) has been used to test our proof-of-concept framework as it is a complex scientific software used by many climate researchers. Using our framework, a user can execute CESM with custom parameters with only a few clicks on the web interface. As ScifE is generic, other software can be imported relatively easy to our framework. Additional collaborative tools are being developed for successive versions of ScifE, enhancing the practice of science.〈/p〉〈/div〉
    Print ISSN: 0098-3004
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    Topics: Geosciences , Computer Science
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    Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage (2019)
    Springer
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    Publication Date: 2019
    Description: Due to the increasing popularity of recent advanced features and context-awareness in smart mobile phones, the contextual data relevant to users’ diverse activities with their phones are recorded through the d...
    Electronic ISSN: 2196-1115
    Topics: Computer Science
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    Information, Vol. 10, Pages 228: Multilingual Open Information Extraction: Challenges and Opportunities (2019)
    MDPI
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    Publication Date: 2019
    Description: The number of documents published on the Web in languages other than English grows every year. As a consequence, the need to extract useful information from different languages increases, highlighting the importance of research into Open Information Extraction (OIE) techniques. Different OIE methods have dealt with features from a unique language; however, few approaches tackle multilingual aspects. In those approaches, multilingualism is restricted to processing text in different languages, rather than exploring cross-linguistic resources, which results in low precision due to the use of general rules. Multilingual methods have been applied to numerous problems in Natural Language Processing, achieving satisfactory results and demonstrating that knowledge acquisition for a language can be transferred to other languages to improve the quality of the facts extracted. We argue that a multilingual approach can enhance OIE methods as it is ideal to evaluate and compare OIE systems, and therefore can be applied to the collected facts. In this work, we discuss how the transfer knowledge between languages can increase acquisition from multilingual approaches. We provide a roadmap of the Multilingual Open IE area concerning state of the art studies. Additionally, we evaluate the transfer of knowledge to improve the quality of the facts extracted in each language. Moreover, we discuss the importance of a parallel corpus to evaluate and compare multilingual systems.
    Electronic ISSN: 2078-2489
    Topics: Computer Science
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    Information, Vol. 10, Pages 227: Visualizing the Knowledge Structure and Research Evolution of Infrared Detection Technology Studies (2019)
    MDPI
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    Publication Date: 2019
    Description: This paper aims to explore the current status, research trends and hotspots related to the field of infrared detection technology through bibliometric analysis and visualization techniques based on the Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI) articles published between 1990 and 2018 using the VOSviewer and Citespace software tools. Based on our analysis, we first present the spatiotemporal distribution of the literature related to infrared detection technology, including annual publications, origin country/region, main research organization, and source publications. Then, we report the main subject categories involved in infrared detection technology. Furthermore, we adopt literature cocitation, author cocitation, keyword co-occurrence and timeline visualization analyses to visually explore the research fronts and trends, and present the evolution of infrared detection technology research. The results show that China, the USA and Italy are the three most active countries in infrared detection technology research and that the Centre National de la Recherche Scientifique has the largest number of publications among related organizations. The most prominent research hotspots in the past five years are vibration thermal imaging, pulse thermal imaging, photonic crystals, skin temperature, remote sensing technology, and detection of delamination defects in concrete. The trend of future research on infrared detection technology is from qualitative to quantitative research development, engineering application research and infrared detection technology combined with other detection techniques. The proposed approach based on the scientific knowledge graph analysis can be used to establish reference information and a research basis for application and development of methods in the domain of infrared detection technology studies.
    Electronic ISSN: 2078-2489
    Topics: Computer Science
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    Information, Vol. 10, Pages 226: Big Data Analytics and Firm Performance: A Systematic Review (2019)
    MDPI
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    Publication Date: 2019
    Description: The literature on big data analytics and firm performance is still fragmented and lacking in attempts to integrate the current studies’ results. This study aims to provide a systematic review of contributions related to big data analytics and firm performance. The authors assess papers listed in the Web of Science index. This study identifies the factors that may influence the adoption of big data analytics in various parts of an organization and categorizes the diverse types of performance that big data analytics can address. Directions for future research are developed from the results. This systematic review proposes to create avenues for both conceptual and empirical research streams by emphasizing the importance of big data analytics in improving firm performance. In addition, this review offers both scholars and practitioners an increased understanding of the link between big data analytics and firm performance.
    Electronic ISSN: 2078-2489
    Topics: Computer Science
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    Identifying influential spreaders based on edge ratio and neighborhood diversity measures in complex networks (2019)
    Springer
    In: Computing
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉In recent years, notable number of research studies have been conducted on the analysis of diffusion process in complex networks. One fundamental problem in this domain is to find the most influential spreader nodes. For achieving a successful spreading process, nodes having high spreading ability should be selected as spreaders. Many centrality measures have been proposed for determining and ranking the significance of nodes and detecting the best spreaders. The majority of proposed centrality measures require network global information which leads to high time complexity. Moreover, with the advent of large-scale complex networks, there is a critical need for improving accurate measures through using nodes’ local information. On the other hand, most of the formerly proposed centrality measures have attempted to select core nodes as spreaders but global bridge nodes have the highest spreading ability since they are located among several giant communities of the network. In this study, a new local and parameter-free centrality measure is proposed which is aimed at finding global bridge nodes in the network. Hence, two new local metrics, namely 〈em〉edge ratio and neighborhood diversity〈/em〉, are firstly defined which are used in the proposed method. Considering edge ratio of neighbors ensures that the selected node be not in the periphery location of the network. Furthermore, a node with high neighborhood diversity is likely a connector between some modules (dense parts) of the network. Therefore, a node with a high edge ratio and more diverse neighborhood has high spreading ability. The major merits of the proposed measure are near-linear time complexity, using local information and being parameter-free. For evaluating the proposed method, we conducted experiments on real-world networks. The results of comparing the proposed centrality measure with other measures in terms of epidemic models (SIR and SI), Kendall’s tau correlation coefficient and Rank-Frequency measures indicated that the proposed method outperforms the other compared centrality measures.〈/p〉
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    Edge-based personal computing services: fall detection as a pilot study (2019)
    Springer
    In: Computing
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉Current developments in information and electronic technologies have pushed a tremendous amount of applications to meet the demands of personal computing services. Various kinds of smart devices have been launched and applied in our daily lives to provide services for individuals; however, the existing computing frameworks including local silo-based and cloud-based architectures, are not quite fit for personal computing services. Meanwhile, personal computing applications exhibit special features, they are latency-sensitive, energy efficient, highly reliable, mobile, etc, which further indicates that a new computing architecture is urgently needed to support such services. Thanks to the emerging edge computing paradigm, we were inspired to apply the distributed cooperative computing idea at the data source, which perfectly solves issues occurring among existing computing paradigms while meeting the requirements of personal computing services. Therefore, we explore personal computing services utilizing the edge computing paradigm, discuss the overall edge-based system architecture for personal computing services, and design the conceptual framework for an edge-based personal computing system. We analyze the functionalities in detail. To validate the feasibility of the proposed architecture, a fall detection application is simulated in our preliminary evaluation as an example service in which three Support Vector Machine based fall detection algorithms with different kernel functions are implemented. Experimental results show edge computing architecture can improve the performance of the system in terms of total latency, with about 22.75% reduction on average in the case of applying 4G at the second hop even when the data and computing stream of the application is small.〈/p〉
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    Peptide-Protein Interaction Studies of Antimicrobial Peptides Targeting Middle East Respiratory Syndrome Coronavirus Spike Protein: An In Silico Approach (2019)
    Hindawi
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    Publication Date: 2019
    Description: There is no effective therapeutic or vaccine for Middle East Respiratory Syndrome and this study attempts to find therapy using peptide by establishing a basis for the peptide-protein interactions through in silico docking studies for the spike protein of MERS-CoV. The antimicrobial peptides (AMPs) were retrieved from the antimicrobial peptide database (APD3) and shortlisted based on certain important physicochemical properties. The binding mode of the shortlisted peptides was measured based on the number of clusters which forms in a protein-peptide docking using Piper. As a result, we identified a list of putative AMPs which binds to the spike protein of MERS-CoV, which may be crucial in providing the inhibitory action. It is observed that seven putative peptides have good binding score based on cluster size cutoff of 208. We conclude that seven peptides, namely, AP00225, AP00180, AP00549, AP00744, AP00729, AP00764, and AP00223, could possibly have binding with the active site of the MERS-CoV spike protein. These seven AMPs could serve as a therapeutic option for MERS and enhance its treatment outcome.
    Print ISSN: 1687-8027
    Electronic ISSN: 1687-8035
    Topics: Biology , Computer Science
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    Multi-access edge computing aided mobility for privacy protection in Internet of Things (2019)
    Springer
    In: Computing
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉Communications among IoT devices and backend servers are among the most critical aspects of IoT system, as it not only governs hardware requirement and system performance but also impacts security and privacy of the system. Multi-access edge computing (MEC) is an enabler for service development in 5G. Furthermore, MEC will reduce IoT system communication cost such as latency and bandwidth thanks to its ubiquitously nearby processing and storage capability. Also, MEC will provide opportunities to enhance IoT system’s network privacy, which cannot be solved by IoT system itself easily. In this paper, we present MEC enhanced mobility support system for IoT system: with MEC it can be implemented with low operation cost and overhead and can protect IoT system’s user from being exploited due to the exposed network location of data uploading and the control signals.〈/p〉
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    Detect and correlate information system events through verbose logging messages analysis (2019)
    Springer
    In: Computing
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉Detecting and tracking events from logging data is a critical element for security and system administrators and thus attracts more and more research efforts. However, there exists a major limitation in current processes of Event Logging analysis, related to the verbosity and language-dependence of messages produced by many logging systems. In this paper, a novel methodology was proposed to tackle this limitation by analysing event messages through a Natural Language Processing task in order to annotate them with semantic metadata. These metadata are further used to enable semantic searches or domain ontology population that help administrator to filter only relevant event and to correlate them for a prompt and efficient response and incident analysis.〈/p〉
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    Virtual network mapping considering energy consumption and availability (2019)
    Springer
    In: Computing
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉Network virtualization is widely considered as a mainstay for overcoming the Internet’s ossification problem, and virtual network embedding (VNE) is a critical issue. Over recent years, growing energy costs and increased ecological awareness have stimulated the interest in reducing energy consumption by Internet service providers (ISP). Dependability is also an important requirement, as it involves metrics such as reliability and availability, which directly impact quality of service (QoS). Prior works on virtual network embedding have focused mainly on maximizing revenue for Internet service providers (ISPs), and they did not consider energy consumption and dependability metrics jointly in the mapping. This paper presents an energy-efficient mapping of dependable virtual networks. The approach considers a problem formulation that concomitantly takes into account energy consumption and availability constraints for virtual network embedding problem, and an algorithm based on Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic is adopted. The algorithm utilizes a sensitivity analysis based on availability importance to achieve the QoS required by each virtual network, and models based on reliability block diagrams (RBD) and stochastic Petri nets (SPN) are utilized to estimate availability. Results demonstrate the feasibility of the proposed approach, and they show the trade-off between availability, energy consumption, cost and revenue.〈/p〉
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    Implementation and comparison of binary thinning algorithms on GPU (2019)
    Springer
    In: Computing
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉Thinning algorithms have been widely applied in many applications such as computer graphics, computer vision and medical imaging. The performances of thinning algorithms are hampered by the increase of image’s size and image’s content. By employing graphics processing units (GPUs), the calculation of thinning algorithms can be accelerated. In this paper, we present GPU parallel versions of well-known binary thinning algorithms namely the ZS algorithm, the GH algorithm, the AW algorithm and the hybrid algorithm belonging to different classes for an efficient implementation using CUDA as a parallel programming model. These thinning algorithms are then evaluated and compared according to quality and runtime measures. The obtained results show that the GPU multithreaded implementations of the selected algorithms present high computational speed outperforming the central processing unit sequential implementations achieving the best average speedup when executing the AW algorithm.〈/p〉
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    Dynamic behaviour of competing memes’ spread with alert influence in multiplex social-networks (2019)
    Springer
    In: Computing
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉This study elucidates the dynamic behaviour of the two competing mutually exclusive epidemic (meme) spreading model with the alert of memes over multiplex social networks. Each meme spreads over a distinct contact networks 〈span〉 〈span〉\((CN_1,CN_2)\)〈/span〉 〈/span〉 of an undirected multiplex social network. The behavioural responses of agents (alerts) to the spread of competing memes is disseminated through information dissemination network (IDN). Here, IDN has the same nodes but different links with respect to the respective 〈span〉 〈span〉\(CN_i(i=1,2)\)〈/span〉 〈/span〉. The analytical treatment of this model is analysed through the mean field approximation of the epidemic process. Also, it has been shown through numerical illustrations that a node in the alert state is less probable to become infected than a node in the susceptible state. Moreover, co-existence of both the memes, the survival threshold, the absolute dominance threshold of the two competitive memes and the alert threshold for minimizing the severity of meme spread are analytically explored and numerically illustrated.〈/p〉
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    A fuzzy security protocol for trust management in the internet of things (Fuzzy-IoT) (2019)
    Springer
    In: Computing
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉Recently, the Internet of things (IoT) has received a lot of attention from both industry and academia. A reliable and secure IoT connection and communication is essential for the proper working of the IoT network as a whole. One of the ways to achieve robust security in an IoT network is to enable and build trusted communication among the things (nodes). In this area, the existing IoT literature faces many critical issues, such as the lack of intelligent cluster-based trust approaches for IoT networks and the detection of attacks on the IoT trust system from malicious nodes, such as bad service providers. The existing literature either does not address these issues or only addresses them partially. Our proposed solution can firstly detect on-off attacks using the proposed fuzzy-logic based approach, and it can detect contradictory behaviour attacks and other malicious nodes. Secondly, we develop a fuzzy logic-based approach to detect malicious nodes involved in bad service provisioning. Finally, to maintain the security of the IoT network, we develop a secure messaging system that enables secure communication between nodes. This messaging system uses hexadecimal values with a structure similar to serial communication. We carried out extensive experimentation under varying network sizes to validate the working of our proposed solution and also to test the efficiency of the proposed methods in relation to various types of malicious behavior. The experiment results demonstrate the effectiveness of our approach under various conditions.〈/p〉
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    A motion-based waveform for the detection of breathing difficulties during sleep (2019)
    Springer
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉Individuals who suffer from different sleep breathing disorders suffer from a wide range of serious health problems. Unfortunately, the rate of diagnosis is very low, and the existing breathing monitoring techniques are expensive, uncomfortable and time- and labor-intensive. The gold standard PSG is invasive, costly, technically complex and time-consuming. Toward developing a non-contact sleep breathing monitoring system, this study presents a motion-based computer vision approach that aims to detect breathing movements of the sleeping patient from infrared videos and map them into a waveform. The proposed waveform illustrates that each type of breathing difficulty has a specific pattern and hence can be easily distinguished. This facilitates identifying only suspicious periods during which physiological signals will be scored, instead of analyzing the whole signals of 8 h of sleep.〈/p〉
    Print ISSN: 0932-8092
    Electronic ISSN: 1432-1769
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    Dual-channel CNN for efficient abnormal behavior identification through crowd feature engineering (2019)
    Springer
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉This research has been investigating an automatic and online crowd anomaly detection model by exploring a novel compound image descriptor generated from live video streams. A dual-channel convolutional neural network (DCCNN) has been set up for efficiently processing scene-related and motion-related crowd information inherited from raw frames and the compound descriptor instances. The novelty of the work stemmed from the creation of the spatio-temporal cuboids in online (or near real-time) manner through dynamically extracting local feature tracklets within the temporal space while handling the foreground region-of-interests (i.e., moving targets) through the exploration of Gaussian Mixture Model in the spatial space. Hence, the extracted foreground blocks can effectively eliminate irrelevant backgrounds and noises from the live streams for reducing the computational costs in the subsequent detecting phases. The devised compound feature descriptor, named as spatio-temporal feature descriptor (STFD), is capable of characterizing the crowd attributes through the measures such as collectiveness, stability, conflict and density in each online generated spatio-temporal cuboid. A STFD instance registers not only the dynamic variation of the targeted crowd over time based on local feature tracklets, but also the interaction information of neighborhoods within a crowd, e.g., the interaction force through the K-nearest neighbor (K-NN) analysis. The DCCNN developed in this research enables online identification of suspicious crowd behaviors based on analyzing the live-feed images and their STFD instances. The proposed model has been developed and evaluated against benchmarking techniques and databases. Experimental results have shown substantial improvements in terms of detection accuracy and efficiency for online crowd abnormal behavior identification. 〈/p〉
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    Detection and classification of social media-based extremist affiliations using sentiment analysis techniques (2019)
    Springer
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉Identification and classification of extremist-related tweets is a hot issue. Extremist gangs have been involved in using social media sites like Facebook and Twitter for propagating their ideology and recruitment of individuals. This work aims at proposing a terrorism-related content analysis framework with the focus on classifying tweets into extremist and non-extremist classes. Based on user-generated social media posts on Twitter, we develop a tweet classification system using deep learning-based sentiment analysis techniques to classify the tweets as extremist or non-extremist. The experimental results are encouraging and provide a gateway for future researchers.〈/p〉
    Electronic ISSN: 2192-1962
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    Detection of abnormal behavior in narrow scene with perspective distortion (2019)
    Springer
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉Automatically and efficiently detecting abnormal events that occur in dynamic surveillance video is one of the important tasks in real-time video streaming analysis. However, in some typical application scenarios with narrow areas, the perspective distortion caused by the large depth-of-field has a tremendous negative impact on the accuracy of detection, thereby increasing the difficulty of identifying abnormal behavior. Taking the real-time violence occurring in the metro platform as an example, the article introduces a more effective algorithm for detecting abnormal behaviors in narrow areas with perspective distortion. The algorithm firstly uses the adaptive transformation mechanism to make up for the distorting effect in the region of interest extraction. Then, an improved pyramid L–K optical flow method with perspective weight and disorder coefficient is proposed to extract the abnormal behavior feature occurred in historical moving images. The side-by-side comparison of the experimental results proves that this algorithm can effectively compensate for the distortion effect and obviously improve the accuracy of abnormal behavior detection in narrow area scenes.〈/p〉
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    Efficient routing for corona based underwater wireless sensor networks (2019)
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    In: Computing
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉The imbalance energy consumption and high data traffic at intermediate nodes degrade the network performance. In this paper, we propose: energy grade and balance load distribution corona, EG without corona and DA without corona based schemes to distribute data traffic across the network nodes for efficient energy consumption. The dynamic adjustment of transmission range in first scheme helps in reducing data load. Additionally, the transmission range is purely based on distance, energy and data load of the receiver node for achieving maximum network lifetime. Second scheme divides a data packet into three fractions; small, medium and large for transmitting from various paths to evenly distribute the data load on the network nodes. In third scheme, depth adjustment of void node is performed to resume network operations, whereas, the load distribution and transmission range mechanisms are the same. The extensive simulations are carried out to show the effectiveness of proposed schemes in terms of PDR, energy consumption, and load distribution against the baseline scheme.〈/p〉
    Print ISSN: 0010-485X
    Electronic ISSN: 1436-5057
    Topics: Computer Science
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    Understanding continuance intention to use online to offline (O2O) apps (2019)
    Springer
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉O2O commerce is a new business model combining online shopping and offline transactions. While many reports indicate the large potential size of the O2O market, little is known about users’ continuance intention to use. This study applies an expectation confirmation model (ECM) that incorporates perceived hedonic benefits, product information intensity and transaction costs as belief-related constructs to predict users’ continuance intention to use O2O apps. The proposed model was empirically evaluated using survey data collected from 333 users concerning their perceptions of O2O apps. The results indicated that confirmation of O2O app usage experience was positively related to both perceived benefits such as utilitarian and hedonic benefits and satisfaction. Perceived benefits, satisfaction and transaction costs were found to have a direct impact on continuance intention. Specifically, there was a significant difference between task-oriented O2O apps users and entertainment-oriented O2O apps users. The results may provide further insights into O2O app marketing strategies.〈/p〉
    Print ISSN: 1019-6781
    Electronic ISSN: 1422-8890
    Topics: Computer Science , Economics
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    Distribution-free uncertainty quantification for kernel methods by gradient perturbations (2019)
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉We propose a data-driven approach to quantify the uncertainty of models constructed by kernel methods. Our approach minimizes the needed distributional assumptions, hence, instead of working with, for example, Gaussian processes or exponential families, it only requires knowledge about some mild regularity of the measurement noise, such as it is being symmetric or exchangeable. We show, by building on recent results from finite-sample system identification, that by perturbing the residuals in the gradient of the objective function, information can be extracted about the amount of uncertainty our model has. Particularly, we provide an algorithm to build exact, non-asymptotically guaranteed, distribution-free confidence regions for ideal, noise-free representations of the function we try to estimate. For the typical convex quadratic problems and symmetric noises, the regions are star convex centered around a given nominal estimate, and have efficient ellipsoidal outer approximations. Finally, we illustrate the ideas on typical kernel methods, such as LS-SVC, KRR, 〈span〉 〈span〉\(\varepsilon \)〈/span〉 〈/span〉-SVR and kernelized LASSO.〈/p〉
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    Trajectory prediction of vehicles turning at intersections using deep neural networks (2019)
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉In this paper, an early prediction of vehicle trajectories and turning movements are investigated using traffic cameras. A vision-based tracking system is developed to monitor intersection videos and collect vehicle trajectories with their labels known as turning movements. Firstly, two intersection videos are monitored for 2 h, and collected trajectories with their labels are used to train deep neural networks and obtain the turning models for the prediction task. Deep neural networks are further investigated on a third intersection with different video settings. The future 2 s evaluation of trajectories shows the success of long short-term memory networks to early predict the turning movements with more than 92% accuracy.〈/p〉
    Print ISSN: 0932-8092
    Electronic ISSN: 1432-1769
    Topics: Computer Science
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    MAKE, Vol. 1, Pages 756-767: Semi-Supervised Faster RCNN-Based Person Detection and Load Classification for Far Field Video Surveillance (2019)
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    Publication Date: 2019
    Description: This paper presents a semi-supervised faster region-based convolutional neural network (SF-RCNN) approach to detect persons and to classify the load carried by them in video data captured from distances several miles away via high-power lens video cameras. For detection, a set of computationally efficient image processing steps are considered to identify moving areas that may contain a person. These areas are then passed onto a faster RCNN classifier whose convolutional layers consist of ResNet50 transfer learning. Frame labels are obtained in a semi-supervised manner for the training of the faster RCNN classifier. For load classification, another convolutional neural network classifier whose convolutional layers consist of GoogleNet transfer learning is used to distinguish a person carrying a bundle from a person carrying a long arm. Despite the challenges associated with the video dataset examined in terms of the low resolution of persons, the presence of heat haze, and the shaking of the camera, it is shown that the developed approach outperforms the faster RCNN approach.
    Electronic ISSN: 2504-4990
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    Information processing in the LGN: a comparison of neural codes and cell types (2019)
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉To understand how anatomy and physiology allow an organism to perform its function, it is important to know how information that is transmitted by spikes in the brain is received and encoded. A natural question is whether the spike rate alone encodes the information about a stimulus (〈em〉rate code〈/em〉), or additional information is contained in the temporal pattern of the spikes (〈em〉temporal code〈/em〉). Here we address this question using data from the cat Lateral Geniculate Nucleus (LGN), which is the visual portion of the thalamus, through which visual information from the retina is communicated to the visual cortex. We analyzed the responses of LGN neurons to spatially homogeneous spots of various sizes with temporally random luminance modulation. We compared the Firing Rate with the Shannon Information Transmission Rate , which quantifies the information contained in the temporal relationships between spikes. We found that the behavior of these two rates can differ quantitatively. This suggests that the energy used for spiking does not translate directly into the information to be transmitted. We also compared Firing Rates with Information Rates for X-ON and X-OFF cells. We found that, for X-ON cells the Firing Rate and Information Rate often behave in a completely different way, while for X-OFF cells these rates are much more highly correlated. Our results suggest that for X-ON cells a more efficient 〈em〉“temporal code”〈/em〉 is employed, while for X-OFF cells a straightforward 〈em〉“rate code”〈/em〉 is used, which is more reliable and is correlated with energy consumption. 〈/p〉
    Print ISSN: 0340-1200
    Electronic ISSN: 1432-0770
    Topics: Biology , Computer Science , Physics
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    Improved LSH for privacy-aware and robust recommender system with sparse data in edge environment (2019)
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    Publication Date: 2019
    Description: Recommender systems have become a popular and effective way to quickly discover new service items that are probably preferred by prospective users. Through analyzing the historical service usage data produced ...
    Print ISSN: 1687-1472
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    Topics: Electrical Engineering, Measurement and Control Technology , Computer Science
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    Nuclear discrepancy for single-shot batch active learning (2019)
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉Active learning algorithms propose what data should be labeled given a pool of unlabeled data. Instead of selecting randomly what data to annotate, active learning strategies aim to select data so as to get a good predictive model with as little labeled samples as possible. Single-shot batch active learners select all samples to be labeled in a single step, before any labels are observed. We study single-shot active learners that minimize generalization bounds to select a representative sample, such as the maximum mean discrepancy (MMD) active learner. We prove that a related bound, the discrepancy, provides a tighter worst-case bound. We study these bounds probabilistically, which inspires us to introduce a novel bound, the nuclear discrepancy (ND). The ND bound is tighter for the expected loss under optimistic probabilistic assumptions. Our experiments show that the MMD active learner performs better than the discrepancy in terms of the mean squared error, indicating that tighter worst case bounds do not imply better active learning performance. The proposed active learner improves significantly upon the MMD and discrepancy in the realizable setting and a similar trend is observed in the agnostic setting, showing the benefits of a probabilistic approach to active learning. Our study highlights that assumptions underlying generalization bounds can be equally important as bound-tightness, when it comes to active learning performance. Code for reproducing our experimental results can be found at 〈a href="https://github.com/tomviering/NuclearDiscrepancy"〉https://github.com/tomviering/NuclearDiscrepancy〈/a〉.〈/p〉
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    Automated testing of NFV orchestrators against carrier-grade multi-PoP scenarios using emulation-based smoke testing (2019)
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    Publication Date: 2019
    Description: Future large-scale network function virtualization (NFV) environments will be based on hundreds or even thousands of NFV infrastructure installations, the so called points of presence (PoP). All their resource...
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    Topics: Electrical Engineering, Measurement and Control Technology , Computer Science
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    Evaluation of range-based indoor tracking algorithms by merging simulation and measurements (2019)
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    Publication Date: 2019
    Description: Precise location information will play an important role in 5G networks, their applications and services, especially in indoor environments. Ultra-wideband (UWB) technology offers exceptional temporal resoluti...
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    Topics: Electrical Engineering, Measurement and Control Technology , Computer Science
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    CaDET : interpretable parametric conditional density estimation with decision trees and forests (2019)
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉We introduce 〈span〉CaDET〈/span〉, an algorithm for 〈em〉parametric Conditional Density Estimation〈/em〉 (CDE) based on decision trees and random forests. 〈span〉CaDET〈/span〉 uses the 〈em〉empirical cross entropy〈/em〉 impurity criterion for tree growth, which incentivizes splits that improve predictive accuracy more than the regression criteria or estimated mean-integrated-square-error used in previous works. 〈span〉CaDET〈/span〉 also admits more efficient training and query procedures than existing tree-based CDE approaches, and stores only a bounded amount of information at each tree leaf, by using 〈em〉sufficient statistics〈/em〉 for all computations. Previous tree-based CDE techniques produce complicated uninterpretable distribution objects, whereas 〈span〉CaDET〈/span〉 may be instantiated with easily interpretable distribution families, making every part of the model easy to understand. Our experimental evaluation on real datasets shows that 〈span〉CaDET〈/span〉 usually learns more accurate, smaller, and more interpretable models, and is less prone to overfitting than existing tree-based CDE approaches.〈/p〉
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    Covariance-based dissimilarity measures applied to clustering wide-sense stationary ergodic processes (2019)
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉We introduce a new unsupervised learning problem: clustering wide-sense stationary ergodic stochastic processes. A covariance-based dissimilarity measure together with asymptotically consistent algorithms is designed for clustering offline and online datasets, respectively. We also suggest a formal criterion on the efficiency of dissimilarity measures, and discuss an approach to improve the efficiency of our clustering algorithms, when they are applied to cluster particular type of processes, such as self-similar processes with wide-sense stationary ergodic increments. Clustering synthetic data and real-world data are provided as examples of applications.〈/p〉
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    Provable accelerated gradient method for nonconvex low rank optimization (2019)
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉Optimization over low rank matrices has broad applications in machine learning. For large-scale problems, an attractive heuristic is to factorize the low rank matrix to a product of two much smaller matrices. In this paper, we study the nonconvex problem 〈span〉 〈span〉\(\min _{\mathbf {U}\in \mathbb {R}^{n\times r}} g(\mathbf {U})=f(\mathbf {U}\mathbf {U}^T)\)〈/span〉 〈/span〉 under the assumptions that 〈span〉 〈span〉\(f(\mathbf {X})\)〈/span〉 〈/span〉 is restricted 〈span〉 〈span〉\(\mu \)〈/span〉 〈/span〉-strongly convex and 〈em〉L〈/em〉-smooth on the set 〈span〉 〈span〉\(\{\mathbf {X}:\mathbf {X}\succeq 0,\text{ rank }(\mathbf {X})\le r\}\)〈/span〉 〈/span〉. We propose an accelerated gradient method with alternating constraint that operates directly on the 〈span〉 〈span〉\(\mathbf {U}\)〈/span〉 〈/span〉 factors and show that the method has local linear convergence rate with the optimal dependence on the condition number of 〈span〉 〈span〉\(\sqrt{L/\mu }\)〈/span〉 〈/span〉. Globally, our method converges to the critical point with zero gradient from any initializer. Our method also applies to the problem with the asymmetric factorization of 〈span〉 〈span〉\(\mathbf {X}={\widetilde{\mathbf {U}}}{\widetilde{\mathbf {V}}}^T\)〈/span〉 〈/span〉 and the same convergence result can be obtained. Extensive experimental results verify the advantage of our method.〈/p〉
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    Visualization of phase distribution in lead–bismuth eutectic during one-dimensional solidification process (2019)
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    Publication Date: 2019
    Description: 〈span〉 〈h3〉Abstract〈/h3〉 〈p〉Solidification process of lead–bismuth eutectic (LBE) is one of the key phenomena to prevent flow channel blockage accident in an LBE-cooled accelerator-driven system. However, the solidification of liquid metal cannot be observed optically and it is difficult to detect noninvasively. In this study, the one-dimensional solidification process of the LBE was visualized by pulsed neutron transmission imaging. Neutrons have higher transmittivity to the LBE than X-ray and neutron transmission spectrum of the LBE sample can be obtained by pulsed neutron imaging technique. The solid and liquid phases of the LBE were identified during the solidification process by the presence or absence of Bragg edge in the measured neutron transmission spectrum, and the transient behavior of the solid–liquid interface could be visualized. In addition, the characteristic spatial distribution of the crystalline structure was found in Bragg-edge transmission image after the solidification.〈/p〉 〈/span〉 〈span〉 〈h3〉Graphic abstract〈/h3〉 〈p〉 〈span〉 〈span〉 〈img alt="" src="https://static-content.springer.com/image/MediaObjects/12650_2019_572_Figa_HTML.png"〉 〈/span〉 〈/span〉 〈/p〉 〈/span〉
    Print ISSN: 1343-8875
    Electronic ISSN: 1875-8975
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    Digital Transformation in Health Care: Augmented Reality for Hands-Free Service Innovation (2019)
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉Health care professionals regularly require access to information systems throughout their daily work. However, existing smart devices like smartphones and tablets are difficult to use at the point of care, because health care professionals require both hands during their work. Following a design science research approach including ethnographic fieldwork and prototype tests with focus groups, we find that Augmented Reality smart glass applications offer potential for service innovation in the health care sector. Our smart glass prototype supports health care professionals during wound treatment by allowing them to document procedures hands-free while they perform them. Furthermore, we investigate the use of audio based and physical interaction with the smart glasses in a within-subjects design experiment.〈/p〉
    Print ISSN: 1387-3326
    Electronic ISSN: 1572-9419
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    News (2019)
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    Publication Date: 2019
    Print ISSN: 1618-2162
    Electronic ISSN: 1610-1995
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    Feature visualization in comic artist classification using deep neural networks (2019)
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    Publication Date: 2019
    Description: Deep neural networks have become a standard framework for image analytics. Besides the traditional applications, such as object classification and detection, the latest studies have started to expand the scope...
    Electronic ISSN: 2196-1115
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    Dynamics reconstruction and classification via Koopman features (2019)
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉Knowledge discovery and information extraction of large and complex datasets has attracted great attention in wide-ranging areas from statistics and biology to medicine. Tools from machine learning, data mining, and neurocomputing have been extensively explored and utilized to accomplish such compelling data analytics tasks. However, for time-series data presenting active dynamic characteristics, many of the state-of-the-art techniques may not perform well in capturing the inherited temporal structures in these data. In this paper, integrating the Koopman operator and linear dynamical systems theory with support vector machines, we develop a novel dynamic data mining framework to construct low-dimensional linear models that approximate the nonlinear flow of high-dimensional time-series data generated by unknown nonlinear dynamical systems. This framework then immediately enables pattern recognition, e.g., classification, of complex time-series data to distinguish their dynamic behaviors by using the trajectories generated by the reduced linear systems. Moreover, we demonstrate the applicability and efficiency of this framework through the problems of time-series classification in bioinformatics and healthcare, including cognitive classification and seizure detection with fMRI and EEG data, respectively. The developed Koopman dynamic learning framework then lays a solid foundation for effective dynamic data mining and promises a mathematically justified method for extracting the dynamics and significant temporal structures of nonlinear dynamical systems. 〈/p〉
    Print ISSN: 1384-5810
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    Identifying at-risk students based on the phased prediction model (2019)
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉Identifying at-risk students is one of the most important issues in online education. During different stages of a semester, students display various online learning behaviors. Therefore, we propose a phased prediction model to predict at-risk students at different stages of a semester. We analyze students’ individual characteristics and online learning behaviors, extract features that are closely related to their learning performance, and propose combined feature sets based on a time window constraint strategy and a learning time threshold constraint strategy. The results of our experiments show that the precision of the proposed model in different phases is from 90.4 to 93.6%. 〈/p〉
    Print ISSN: 0219-1377
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    System design of the physical layer for Loon’s high-altitude platform (2019)
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    Description: This paper describes several aspects of the physical layer and over the air interface of Loon. Loon utilizes stratospheric balloon-based high-altitude platforms (HAPs) that use Long-Term Evolution (LTE) to con...
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    Deep and machine learnings of remotely sensed imagery and its multi-band visual features for detecting oil palm plantation (2019)
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉Characterization of oil palm plantation is a crucial step toward many geographical based management strategies, ranging from determining regional planting and appropriate species to irrigation and logistics planning. Accurate and most updated plantation identification enables well informed and effective measures for such schemes. This paper proposes a computerized method for detecting oil-palm plantation from remotely sensed imagery. Unlike other existing approaches, where imaging features were retrieved from spectral data and then trained with a machine learning box for region of interest extraction, this paper employed 2-stage detection. Firstly, a deep learning network was employed to determine a presence of oil-palm plantation in a generic Google satellite image. With irrelevant samples being disregarded and thus the problem space being so contained, the images with detected oil-palm had their plantation delineated at higher accuracy by using a support vector machine, based on Gabor texture descriptor. The proposed coupled detection-delineation was benchmarked against different feature descriptors and state-of-the-art supervised and unsupervised machine learning techniques. The validation was made by comparing the extraction results with those ground surveyed by an authority. It was shown in the experiments that it could detect and delineate the plantations with an accuracy of 92.29% and precision, recall and Kappa of 91.16%, 84.97%, and 0.81, respectively.〈/p〉
    Print ISSN: 1865-0473
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    Topics: Geosciences , Computer Science
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    Efficient learning with robust gradient descent (2019)
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    Description: 〈h3〉Abstract〈/h3〉 〈p〉Minimizing the empirical risk is a popular training strategy, but for learning tasks where the data may be noisy or heavy-tailed, one may require many observations in order to generalize well. To achieve better performance under less stringent requirements, we introduce a procedure which constructs a robust approximation of the risk gradient for use in an iterative learning routine. Using high-probability bounds on the excess risk of this algorithm, we show that our update does not deviate far from the ideal gradient-based update. Empirical tests using both controlled simulations and real-world benchmark data show that in diverse settings, the proposed procedure can learn more efficiently, using less resources (iterations and observations) while generalizing better.〈/p〉
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    A journey among Java neutral program variants (2019)
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    Description: 〈h3〉Abstract〈/h3〉 〈p〉〈em〉Neutral program variants〈/em〉 are alternative implementations of a program, yet equivalent with respect to the test suite. Techniques such as approximate computing or genetic improvement share the intuition that potential for enhancements lies in these acceptable behavioral differences (e.g., enhanced performance or reliability). Yet, the automatic synthesis of neutral program variants, through 〈em〉program transformations〈/em〉 remains a key challenge. This work aims at characterizing 〈em〉plastic code regions〈/em〉 in Java programs, i.e., the code regions that are modifiable while maintaining functional correctness, according to a test suite. Our empirical study relies on automatic variations of 6 real-world Java programs. First, we transform these programs with three state-of-the-art program transformations: add, replace and delete statements. We get a pool of 23,445 neutral variants, from which we gather the following novel insights: developers naturally write code that supports fine-grain behavioral changes; statement deletion is a surprisingly effective program transformation; high-level design decisions, such as the choice of a data structure, are natural points that can evolve while keeping functionality. Second, we design 3 novel program transformations, targeted at specific plastic regions. New experiments reveal that respectively 60%, 58% and 73% of the synthesized variants (175,688 in total) are neutral and exhibit execution traces that are different from the original.〈/p〉
    Print ISSN: 1389-2576
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    STVG: an evolutionary graph framework for analyzing fast-evolving networks (2019)
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    Description: Sequence of graph snapshots have been commonly utilized in literature to represent changes in a dynamic graph. This approach may be suitable for small-size and slowly evolving graphs; however, it is associated...
    Electronic ISSN: 2196-1115
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    Introducing Registered Reports to the Information Systems Community (2019)
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    Publication Date: 2019
    Print ISSN: 2363-7005
    Electronic ISSN: 1867-0202
    Topics: Computer Science , Economics
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    Effects of sentiment on recommendations in social network (2019)
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    Description: 〈h3〉Abstract〈/h3〉 〈p〉This study adopted a sentiment word database to extract sentiment-related data from microblog posts. These data were then used to investigate the effect of different types of sentiment-related words on product recommendations. The results indicate that posts containing strong sentiments received more clicks than posts containing neutral sentiments. Posts containing more than one positive sentiment word generate more effective recommendations than posts containing only one positive sentiment word. This study also demonstrated that posts with a negative polarity classification received more clicks than those with a positive polarity classification. Additionally, the microblog posts containing implicit sentiment words received more clicks than those containing explicit sentiment words. The findings presented here could assist product or service marketers who use Plurk or similar microblogging platforms better focus their limited financial resources on potential online customers to achieve maximum sale revenue.〈/p〉
    Print ISSN: 1019-6781
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    Topics: Computer Science , Economics
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    Pirasa : strategic protocol selection for e-commerce agents (2019)
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉We present 〈span〉Pirasa〈/span〉: an agent-based simulation environment for studying how autonomous agents can best interact with each other to exchange goods in e-commerce marketplaces. A marketplace in 〈span〉Pirasa〈/span〉 enables agents to enact buyer or seller roles and select from sales, auction, and negotiation protocols to achieve the individual goals of their users. An agent’s strategy to maximize its utility in the marketplace is guided by its user’s preferences and constraints such as ‘maximum price’ and ‘deadline’, as well as an agent’s personality attributes, e.g., how ‘eager’ or ‘late’ the agent can be for exchanging goods and whether the agent is a ‘spender’ or ‘saver’ in an exchange. To guide the agent’s actions selected by a strategy, we use the notion of electronic contracts formulated as regulatory norms. In this context, we present how 〈span〉Pirasa〈/span〉 is organized with regards to seller processes for goods submission, the inclusion of buyer preferences, and the management of transactions through specialized broker agents. Using randomized simulations, we demonstrate how a buyer agent can strategically select the most suitable protocol to satisfy its user’s preferences, goals and constraints in dynamically changing market settings. The generated simulation data can be leveraged by researchers to analyze agent behaviors, and develop additional strategies.〈/p〉
    Print ISSN: 1019-6781
    Electronic ISSN: 1422-8890
    Topics: Computer Science , Economics
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    Success factors and complex dynamics of crowdfunding: An empirical research on Taobao platform in China (2019)
    Springer
    Details
    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉In recent years, as a novel financing mechanism, crowdfunding has gained popularity worldwide. In China, Crowdfunding started in 2011 and has experienced extremely fast growth since then. Several studies have identified success factors for crowdfunding. While these studies help infer the dynamics of crowdfunding, such dynamics have not been sufficiently examined, particularly by large-scale empirical studies. In addition, the relationship between crowdfunding market characteristics and project funding success remains fuzzy. In this research, we first empirically assessed the effect of crowdfunding success factors using a large sample (〈em〉N =〈/em〉 5128) collected from 〈a href="http://taobao.com/"〉Taobao.com〈/a〉, a leading crowdfunding platform in China. Built upon these factors, we conducted a multi-period, multi-party simulation study that evaluate crowdfund raisers’ project positioning decisions and investors’ project funding decision in a massive market. In the simulation study, we varied market characteristics such as the number of projects, number of investors per project, entrepreneurs’ moral risk, and ratio of community over financial benefits, to observe their effect on participants’ decisions and consequently, project funding success rate. Results reveal that dynamics of a crowdfunding market are rather complex. These insights contribute to a more in-depth theoretical understanding of crowdfunding, as well as provide useful guidance for crowdfund raisers, investors, and administrators.〈/p〉
    Print ISSN: 1019-6781
    Electronic ISSN: 1422-8890
    Topics: Computer Science , Economics
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    Copyright authentication of digital vector maps based on spatial autocorrelation indices (2019)
    Springer
    Details
    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉Digital watermarking is increasingly being used in the copyright authentication of vector maps to prevent misuse and illegal distribution. However, it is difficult to avoid influences on watermarks when undergoing spacial projection transformations, vector data compression, and other common geographic information system (GIS) operations. In this paper, a highly robust copyright-authentication method is proposed for digital vector maps. First, we discuss the descriptive approaches to the geometric characterization of map elements by building the graphical complexity index. Next, using the Moran’s I Coefficient (MC), a type of spatial autocorrelation index (SAI), we effectively integrate the spatial topological information and the spatial geometric shape information into the meaningless zero-watermark, which is robust against common GIS operations. Finally, we generate a meaningful zero-watermark by using the exclusive OR (XOR) process to combine the meaningless zero watermark and useful copyright information, which not only embeds the copyright information but also effectively avoids authentication conflict. These results demonstrate that this method is robust to translation, scaling, compression, rotation and edit operations (robustness is 80%). The robustness suggests that this method can readily support business applications for copyright certification of digital vector maps.〈/p〉
    Print ISSN: 1865-0473
    Electronic ISSN: 1865-0481
    Topics: Geosciences , Computer Science
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    Future Internet, Vol. 11, Pages 142: Service Level Agreement Violations in Cloud Storage: Insurance and Compensation Sustainability (2019)
    MDPI
    Details
    Publication Date: 2019
    Description: Service Level Agreements are employed to set availability commitments in cloud services. When a violation occurs as in an outage, cloud providers may be called to compensate customers for the losses incurred. Such compensation may be so large as to erode cloud providers’ profit margins. Insurance may be used to protect cloud providers against such a danger. In this paper, closed formulas are provided through the expected utility paradigm to set the insurance premium under different outage models and QoS metrics (no. of outages, no. of long outages, and unavailability). When the cloud service is paid through a fixed fee, we also provide the maximum unit compensation that a cloud provider can offer so as to meet constraints on its profit loss. The unit compensation is shown to vary approximately as the inverse square of the service fee.
    Electronic ISSN: 1999-5903
    Topics: Computer Science
    Published by MDPI
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    Update (1.1) to ANDURIL — A MATLAB toolbox for ANalysis and Decisions with UnceRtaInty: Learning from expert judgments: ANDURYL (2019)
    Elsevier
    In: SoftwareX
    Details
    Publication Date: 2019
    Description: 〈p〉Publication date: July–December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 SoftwareX, Volume 10〈/p〉 〈p〉Author(s): Cornelis Marcel Pieter ’t Hart, Georgios Leontaris, Oswaldo Morales-Nápoles〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉This is an update to PII: 〈a href="https://www.sciencedirect.com/science/article/pii/S2352711018300608" target="_blank"〉S2352711018300608〈/a〉〈/p〉 〈p〉In this paper, we discuss ANDURYL, which is a Python-based open source successor of the MATLAB toolbox ANDURIL. The output of ANDURYL is in good agreement with the results obtained from ANDURIL and EXCALIBUR. Additional features available in ANDURYL, and not available in its predecessors, are discussed.〈/p〉 〈/div〉
    Electronic ISSN: 2352-7110
    Topics: Computer Science
    Published by Elsevier
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    Information, Vol. 10, Pages 273: Terminology Translation in Low-Resource Scenarios (2019)
    MDPI
    Details
    Publication Date: 2019
    Description: Term translation quality in machine translation (MT), which is usually measured by domain experts, is a time-consuming and expensive task. In fact, this is unimaginable in an industrial setting where customised MT systems often need to be updated for many reasons (e.g., availability of new training data, leading MT techniques). To the best of our knowledge, as of yet, there is no publicly-available solution to evaluate terminology translation in MT automatically. Hence, there is a genuine need to have a faster and less-expensive solution to this problem, which could help end-users to identify term translation problems in MT instantly. This study presents a faster and less expensive strategy for evaluating terminology translation in MT. High correlations of our evaluation results with human judgements demonstrate the effectiveness of the proposed solution. The paper also introduces a classification framework, TermCat, that can automatically classify term translation-related errors and expose specific problems in relation to terminology translation in MT. We carried out our experiments with a low resource language pair, English–Hindi, and found that our classifier, whose accuracy varies across the translation directions, error classes, the morphological nature of the languages, and MT models, generally performs competently in the terminology translation classification task.
    Electronic ISSN: 2078-2489
    Topics: Computer Science
    Published by MDPI
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    MAKE, Vol. 1, Pages 745-755: Generalization of Parameter Selection of SVM and LS-SVM for Regression (2019)
    Details
    Publication Date: 2019
    Description: A Support Vector Machine (SVM) for regression is a popular machine learning model that aims to solve nonlinear function approximation problems wherein explicit model equations are difficult to formulate. The performance of an SVM depends largely on the selection of its parameters. Choosing between an SVM that solves an optimization problem with inequality constrains and one that solves the least square of errors (LS-SVM) adds to the complexity. Various methods have been proposed for tuning parameters, but no article puts the SVM and LS-SVM side by side to discuss the issue using a large dataset from the real world, which could be problematic for existing parameter tuning methods. We investigated both the SVM and LS-SVM with an artificial dataset and a dataset of more than 200,000 points used for the reconstruction of the global surface ocean CO2 concentration. The results reveal that: (1) the two models are most sensitive to the parameter of the kernel function, which lies in a narrow range for scaled input data; (2) the optimal values of other parameters do not change much for different datasets; and (3) the LS-SVM performs better than the SVM in general. The LS-SVM is recommended, as it has less parameters to be tuned and yields a smaller bias. Nevertheless, the SVM has advantages of consuming less computer resources and taking less time to train. The results suggest initial parameter guesses for using the models.
    Electronic ISSN: 2504-4990
    Topics: Computer Science
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    The Benefits of Enterprise Architecture in Organizational Transformation (2019)
    Springer
    Details
    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉Today, as organizations constantly adjust their activities to meet ever-changing circumstances, continuous business transformation is taking place. However, planning and steering this transformation can be a daunting task as complexity has been built into the organization over the years. Enterprise Architecture (EA) has been widely adapted as a planning and governance approach to manage the complexity and constant change, and to align the organization toward a common goal. This article studies the EA benefit-realization process by clarifying how EA benefits are realized. Specifically, the focus is on the strategies, resources, and practices which the EA benefits stem from. The findings, derived from an in-depth case study, show that the EA benefit-realization process constitutes a long, intertwined chain of activities. Organizations benefit from EA through various means: from the initiation, when comprehensive understanding starts to form, until years later, when measurable outcomes such as cost savings materialize. Suggestions on what to incorporate into EA programs are presented.〈/p〉
    Print ISSN: 2363-7005
    Electronic ISSN: 1867-0202
    Topics: Computer Science , Economics
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    Temporal density extrapolation using a dynamic basis approach (2019)
    Springer
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉Density estimation is a versatile technique underlying many data mining tasks and techniques, ranging from exploration and presentation of static data, to probabilistic classification, or identifying changes or irregularities in streaming data. With the pervasiveness of embedded systems and digitisation, this latter type of streaming and evolving data becomes more important. Nevertheless, research in density estimation has so far focused on stationary data, leaving the task of of extrapolating and predicting density at time points outside a training window an open problem. For this task, temporal density extrapolation (TDX) is proposed. This novel method models and predicts gradual monotonous changes in a distribution. It is based on the expansion of basis functions, whose weights are modelled as functions of compositional data over time by using an isometric log-ratio transformation. Extrapolated density estimates are then obtained by extrapolating the weights to the requested time point, and querying the density from the basis functions with back-transformed weights. Our approach aims for broad applicability by neither being restricted to a specific parametric distribution, nor relying on cluster structure in the data. It requires only two additional extrapolation-specific parameters, for which reasonable defaults exist. Experimental evaluation on various data streams, synthetic as well as from the real-world domains of credit scoring and environmental health, shows that the model manages to capture monotonous drift patterns accurately and better than existing methods. Thereby, it requires not more than 1.5 times the run time of a corresponding static density estimation approach.〈/p〉
    Print ISSN: 1384-5810
    Electronic ISSN: 1573-756X
    Topics: Computer Science
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    A covariance matrix adaptation evolution strategy in reproducing kernel Hilbert space (2019)
    Springer
    Details
    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉The covariance matrix adaptation evolution strategy (CMA-ES) is an efficient derivative-free optimization algorithm. It optimizes a black-box objective function over a well-defined parameter space in which feature functions are often defined manually. Therefore, the performance of those techniques strongly depends on the quality of the chosen features or the underlying parametric function space. Hence, enabling CMA-ES to optimize on a more complex and general function class has long been desired. In this paper, we consider 〈em〉modeling〈/em〉 the input spaces in black-box optimization 〈em〉non-parametrically〈/em〉 in reproducing kernel Hilbert spaces (RKHS). This modeling leads to a 〈em〉functional optimisation〈/em〉 problem whose domain is a RKHS function space that enables optimisation in a very rich function class. We propose CMA-ES-RKHS, a generalized CMA-ES framework that is able to carry out black-box functional optimisation in RKHS. A search distribution on non-parametric function spaces, represented as a Gaussian process, is adapted by updating both its mean function and covariance operator. Adaptive and sparse representation of the mean function and the covariance operator can be retained for efficient computation in the updates and evaluations of CMA-ES-RKHS by resorting to sparsification. We will also show how to apply our new black-box framework to search for an optimum policy in reinforcement learning in which policies are represented as functions in a RKHS. CMA-ES-RKHS is evaluated on two functional optimization problems and two bench-marking reinforcement learning domains.〈/p〉
    Print ISSN: 1389-2576
    Electronic ISSN: 1573-7632
    Topics: Computer Science
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    Measuring the diversity of recommendations: a preference-aware approach for evaluating and adjusting diversity (2019)
    Springer
    Details
    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉While recent research has highlighted the importance of identifying diverse aspects of user preferences in terms of the quality of recommendations, most of the widely used performance measures tend to consider the accuracy of the recommendations and ignore other important aspects such as preference for diversity in recommendations. This is despite the emerging consensus that improving the diversity in recommendations allows users to discover a wider variety of items and encourage them to extend their range of interests in domains such as books, movies, and music. By proposing a novel diversity evaluation metric, this paper aims to address the problem of measuring the diversity with respect to the distribution of preferences for diversity among recommendation system users. We perform several experiments in order to provide a better understanding of the diversity preferences of users and present the results of diversity evaluations of several recommendation methods. These experiments highlight the accuracy–diversity trade-off and show that higher accuracy does not lead to higher performance in terms of the diversity of the recommendations and that the users’ preferred level of diversity should be considered when designing and evaluating recommender systems. This paper also proposes our Diversity Adjustment algorithm that modifies the diversity of recommendations to suit each user’s preferences while preserving the accuracy. Our experiments suggest that diversifying the recommendations without considering the user’s preferences can lead to a dramatic decline in accuracy, while adjusting the diversity based on users’ diversity needs can support recommender systems in maintaining overall accuracy.〈/p〉
    Print ISSN: 0219-1377
    Electronic ISSN: 0219-3116
    Topics: Computer Science
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    The intrapreneurship reactor: how to enable a start-up culture in corporations (2019)
    Springer
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉Start-ups successfully deliver creative results, and their success highlights the need for corporate engagement with innovation (Weiblen and Chesbrough in California Management Review 57(2): 66–90, 〈span〉2015〈/span〉). To assess insights on corporate high-involvement innovation activities, we developed and applied an intrapreneurship model within a multinational financial services corporation in Germany. Successful implementation requires that individuals have the autonomy to act as corporate entrepreneurs (i.e., intrapreneurs). To this end, we introduced a model for implementing and analysing intrapreneurship so corporations could develop a start-up culture. The corporation studied hosted an innovation competition called the Appathon, where competing teams developed app prototypes, and the winning team received funding for continued development. Afterwards, we completed qualitative and quantitative research by interviewing and surveying the competition participants to examine practical intrapreneurship and corporate and individual innovation. Our findings highlight the challenges surrounding the development of high-involvement innovation management routines (Bessant in 〈em〉High involvement innovation〈/em〉, 〈span〉2003〈/span〉).〈/p〉
    Print ISSN: 0932-383X
    Electronic ISSN: 1613-7620
    Topics: Electrical Engineering, Measurement and Control Technology , Computer Science
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    Innovation durch Kultur und Kommunikation (2019)
    Springer
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    Publication Date: 2019
    Print ISSN: 0932-383X
    Electronic ISSN: 1613-7620
    Topics: Electrical Engineering, Measurement and Control Technology , Computer Science
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    Research and innovation futures: challenging the dominant innovation paradigm (2019)
    Springer
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉While new ways of doing research and innovation such as open science, Science 2.0, open innovation, user innovation or crowdsourcing have been intensively discussed in the past few years, little systematic analysis and exploration of their wider positive, but also negative effects for economy, society and environment has been conducted. Based on the findings from three European foresight projects we discuss critical aspects of changing research and innovation patterns and their challenges for innovation policy and management.〈/p〉
    Print ISSN: 0932-383X
    Electronic ISSN: 1613-7620
    Topics: Electrical Engineering, Measurement and Control Technology , Computer Science
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    Speculate-correct error bounds for k -nearest neighbor classifiers (2019)
    Springer
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉We introduce the speculate-correct method to derive error bounds for local classifiers. Using it, we show that 〈em〉k〈/em〉-nearest neighbor classifiers, in spite of their famously fractured decision boundaries, have exponential error bounds with 〈span〉 〈span〉\(\hbox {O} \left( \sqrt{(k + \ln n)/n} \right) \)〈/span〉 〈/span〉 range around an estimate of generalization error for 〈em〉n〈/em〉 in-sample examples. 〈/p〉
    Print ISSN: 0885-6125
    Electronic ISSN: 1573-0565
    Topics: Computer Science
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    The relationship between platform choice and supplier’s efficiency- evidence from China’s online to offline (O2O)e-commerce platforms (2019)
    Springer
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉Online to offline (O2O) is defined as e-commerce that combines searching and booking products or services online, and consumption in brick-and-mortar stores. Both China and other countries have witnessed the rise of O2O e-commerce platforms in recent years. This paper explores this rapid development from the supply side of O2O e-commerce platforms. Drawing upon the utilities roles of digital platforms and resource dependence theory, we propose and validate hypotheses on the relationships between platform choice and suppliers’ efficiency, using cinemas as an example. The findings support the significant and positive impact of platform choice on cinemas’ efficiency. Furthermore, we find that the more platforms a cinema chooses to link with, the higher efficiency the cinema will achieve. Our empirical results partially support that vertical integration has a negative impact on the relationship between platform choice and suppliers’ efficiency. This study enhances the understanding of the utilities roles of digital platforms and interaction between platform ecosystems and vertical integration.〈/p〉
    Print ISSN: 1019-6781
    Electronic ISSN: 1422-8890
    Topics: Computer Science , Economics
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    Barriers to the Development of Smart Cities in Indian Context (2019)
    Springer
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    Publication Date: 2019
    Description: 〈h3〉Abstract〈/h3〉 〈p〉Smart city development is gaining considerable recognition in the systematic literature and international policies throughout the world. The study aims to identify the key barriers of smart cities from a review of existing literature and views of experts in this area. This work further makes an attempt on the prioritisation of barriers to recognise the most important barrier category and ranking of specific barriers within the categories to the development of smart cities in India. Through the existing literature, this work explored 31 barriers of smart cities development and divided them into six categories. This research work employed fuzzy Analytic Hierarchy Process (AHP) technique to prioritise the selected barriers. Findings reveal that ‘Governance’ is documented as the most significant category of barriers for smart city development followed by ‘Economic; ‘Technology’; ‘Social’; ‘Environmental’ and ‘Legal and Ethical’. In this work, authors also performed sensitivity analysis to validate the findings of study. This research is useful to the government and policymakers for eradicating the potential interferences in smart city development initiatives in developing countries like India.〈/p〉
    Print ISSN: 1387-3326
    Electronic ISSN: 1572-9419
    Topics: Computer Science
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    Information, Vol. 10, Pages 271: Waveform Optimization of Compressed Sensing Radar without Signal Recovery (2019)
    MDPI
    Details
    Publication Date: 2019
    Description: Radar signal processing mainly focuses on target detection, classification, estimation, filtering, and so on. Compressed sensing radar (CSR) technology can potentially provide additional tools to simultaneously reduce computational complexity and effectively solve inference problems. CSR allows direct compressive signal processing without the need to reconstruct the signal. This study aimed to solve the problem of CSR detection without signal recovery by optimizing the transmit waveform. Therefore, a waveform optimization method was introduced to improve the output signal-to-interference-plus-noise ratio (SINR) in the case where the target signal is corrupted by colored interference and noise having known statistical characteristics. Two different target models are discussed: deterministic and random. In the case of a deterministic target, the optimum transmit waveform is derived by maximizing the SINR and a suboptimum solution is also presented. In the case of random target, an iterative waveform optimization method is proposed to maximize the output SINR. This approach ensures that SINR performance is improved in each iteration step. The performance of these methods is illustrated by computer simulation.
    Electronic ISSN: 2078-2489
    Topics: Computer Science
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    Conditions for void formation in friction stir welding from machine learning (2019)
    Details
    Publication Date: 2019
    Electronic ISSN: 2057-3960
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
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    An interacting topological phase (2019)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2019
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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    CRISPR propels a smart hydrogel (2019)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2019
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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    NAD depletion as pathogen response (2019)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2019
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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    Geography of loss (2019)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2019
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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    Reducing tropical deforestation (2019)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2019
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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    Warning signs (2019)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2019
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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    Italian ministry aids animal rights groups (2019)
    American Association for the Advancement of Science (AAAS)
    In: Science
    Details
    Publication Date: 2019
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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