ALBERT

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.

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〉

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〉

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〉

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〉

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.

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〉

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.

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 World Development, Volume 123〈/p〉 〈p〉Author(s): Adriana Camacho, Emily Conover〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Small-scale farmers in developing countries often make production and sale decisions based on imprecise, informal, and out-of-date sources of information, such as family, neighbors, or tradition. Lack of timely and accurate information on climate and prices can lead to inefficiencies in the production, harvesting, and commercialization of agricultural products, which in turn can affect farmers’ revenues and well-being. We did a Randomized Controlled Trial (RCT) experiment with 500 small-scale farmers in a rural area of Colombia where there is nearly full mobile phone usage and coverage. Treated farmers received around 8 text messages per week with prices in the main markets for crops grown in the region, and customized weather forecasts. Compared to a control group, we find that treated farmers were more likely to report that text messages provide useful information for planting and selling, and more likely to always read their messages, indicating an increase in appreciation and use of this type of technology. We also found heterogeneous effects by farmer size. Smaller farmers try to make use of the intervention by planting more crops for which they have price information. Larger farmers seek new markets and increase conversations with other producers. Despite these positive effects, we do not find a significant difference in farmers reporting a price, price differential with the market price, or sale prices received. Our results indicate that farmers are amenable to learning and using new technologies, but that the introduction of these technologies do not always translate into short-run welfare improvements for them. Given the increased interest in incorporating information and communication technologies into agriculture, our findings indicate that prior to a large-scale implementation it is necessary to better understand what prevents farmers from more directly profiting from this new information.〈/p〉〈/div〉 〈/div〉

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.

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.

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 World Development, Volume 123〈/p〉 〈p〉Author(s): Alwin Keil, Archisman Mitra, Amit K. Srivastava, Andrew McDonald〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Sustainable intensification (SI) approaches to agricultural development are urgently needed to meet the growing demand for crop staples while protecting ecosystem services and environmental quality. However, SI initiatives have been criticized for neglecting social welfare outcomes. A recent review found that better-off farmers benefitted disproportionately from SI and highlighted the dearth of studies assessing the equity of outcomes. In this study, we explore the social inclusiveness of zero-tillage (ZT) wheat adoption in Bihar, India. ZT is a proven SI technology for enhancing wheat productivity while boosting profitability and reducing greenhouse gas emissions from agricultural machinery in the densely populated Indo-Gangetic Plains. With an average landholding size of 0.39 ha, most farmers in Bihar depend on custom-hiring services to access the technology. While service provision models should foster inclusive growth by reducing financial barriers to technology adoption, early evidence suggested that smallholders remained at a disadvantage. Building on this previous research, we use a panel dataset from 961 wheat-growing households that spans a six-year period to analyze ZT adoption dynamics over time while accounting for the role of social networks and access to service provision. Using a heckprobit approach to correct for non-exposure bias, we compare determinants of ZT awareness and use in 2012 and 2015. We apply a multinomial logit model to identify determinants of early adoption, recent adoption, non-adoption, and dis-adoption. Furthermore, we explore the quality of ZT services as an additional dimension of socially-inclusive technology access. We find that the strong initial scale bias in ZT use declined substantially as awareness of the technology increased and the service economy expanded. Land fragmentation replaced total landholding size as a significant adoption determinant, which also affected the quality of ZT services received. Hence, farmers with small but contiguous landholdings appear to have gained a significant degree of access over time. We conclude that early-stage assessments of SI may be misleading, and that private sector-based service provision can contribute to socially inclusive development outcomes as markets mature.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 World Development, Volume 123〈/p〉 〈p〉Author(s): Chiara Ravetti, Mare Sarr, Daniel Munene, Tim Swanson〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper analyses the ways in which ethnic identity and labour institutions shape favouritism and discrimination among workers. We conduct a lab experiment in the field with South African coal miners from various ethnic groups and with different trade union membership status. Our analysis suggests that union identity and ethnic identity are two social constructs that operate in a distinct and opposite fashion. Unionization acts as a factor of workers solidarity beyond the confine of union membership. Conversely, ethnicity operates as the linchpin through which discrimination among workers is infused not only between ethnic majority and minorities, but also within the majority group itself. We find that the widespread practice of subcontracting in the mining sector exacerbates ethnic discrimination among workers both between and within ethnic groupings.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 World Development, Volume 123〈/p〉 〈p〉Author(s): Vanesa Jordá, Miguel Niño-Zarazúa〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Despite the growing interest in global inequality, assessing inequality trends is a major challenge because individual data on income or consumption is not often available. Nevertheless, the periodic release of certain summary statistics of the income distribution has become increasingly common. Hence, grouped data in form of income shares have been conventionally used to construct inequality trends based on lower bound approximations of inequality measures. This approach introduces two potential sources of measurement error: first, these estimates are constructed under the assumption of equality of incomes within income shares; second, the highest income earners are not included in the household surveys from which grouped data is obtained. In this paper, we propose to deploy a flexible parametric model, which addresses these two issues in order to obtain a reliable representation of the income distribution and accurate estimates of inequality measures. This methodology is used to estimate the recent evolution of global interpersonal inequality from 1990 to 2015 and to examine the effect of survey under-coverage of top incomes on the level and direction of global inequality. Overall, we find that item non-response at the top of the distribution substantially biases global inequality estimates, but, more importantly, it might also affect the direction of the trends.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 World Development, Volume 123〈/p〉 〈p〉Author(s): Ben Siegelman, Nora Haenn, Xavier Basurto〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper relates how fishermen in San Evaristo on Mexico’s Baja peninsula employ fabrications to strengthen bonds of trust and navigate the complexities of common pool resource extraction. We argue this trickery complicates notions of social capital in community-based natural resource management, which emphasize communitarianism in the form of trust. Trust, defined as a mutual dependability often rooted in honesty, reliable information, or shared expectations, has long been recognized as essential to common pool resource management. Despite this, research that takes a critical approach to social capital places attention on the activities that foster social networks and their norms by arguing that social capital is a process. A critical approach illuminates San Evaristeño practices of lying and joking across social settings and contextualizes these practices within cultural values of harmony. As San Evaristeños assert somewhat paradoxically, for them “lies build trust.” Importantly, a critical approach to this case study forces consideration of gender, an overlooked topic in social capital research. San Evaristeña women are excluded from the verbal jousting through which men maintain ties supporting their primacy in fishery management. Both men’s joke-telling and San Evaristeños’ aversion to conflict have implications for conservation outcomes. As a result, we use these findings to help explain local resistance to outsiders and external management strategies including land trusts, fishing cooperatives, and marine protected areas.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: Available online 5 July 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Space Policy〈/p〉 〈p〉Author(s): Bergit Uhran, Catharine Conley, J. Andy Spry〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Planetary protection, or the protection of celestial bodies from potentially harmful contamination, has been required by international law since the ratification of the Treaty on Principles Governing the Activities of States in the Exploration and Use of Outer Space in 1967. At that time, scientists were concerned about the potential of contaminating the Moon or bringing back a life form of lunar origin when astronauts returned to Earth with samples. The evidence is that the Moon does not contain indigenous life, but other celestial bodies may. Mars is of particular concern because of its habitability and plans to carry out a robotic sample return mission and even potentially human crewed missions in the near future (next 20–30 years). This article (recognizing that other national and international tasks are ongoing with detailed studies of technical aspects of containment and sample analysis) provides an overview of the policy aspects of planetary protection and sample return, discussing the history of planetary protection, lessons learned from the Apollo missions, and a potential strategy to ensure prevention contamination if a robotic sample return mission is carried out. It is recommended that the capability performed in the 1960s by the Interagency Committee on Back Contamination be reestablished, that regulations be developed to allow quarantine of anyone exposed to the samples, that scientific and technical personnel be trained in a receiving laboratory for at least 2 years prior to sample return, and that a test protocol be established for examining the samples. This will protect our biosphere while enabling groundbreaking research into conditions on Mars and any possible signs of life.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: Available online 5 July 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Space Policy〈/p〉 〈p〉Author(s): Harald Köpping Athanasopoulos〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This article discusses the Moon Village concept within the context of the increasing momentum to return human beings to the surface of the Moon. The article follows a dual objective. Firstly, the Moon Village is probably the most misunderstood among the plethora of ideas to explore the Moon scientifically and commercially. The article is therefore intended to explore what the Moon Village actually is. Secondly, the article explores the strengths and weaknesses of the Moon Village. The article is divided into three sections. The first section reviews the current Moon projects of public and private space actors, including the United States, Russia, China, SpaceX and Blue Origin. It sets the scene for the argument that the Moon Village has entered global discourse on space within a positive political environment. The second section discusses the premises of ESA chief Jan Wörner’s Moon Village concept, putting forward that the Moon Village is intended as an open concept rather than as a concrete plan. It is a process rather than a project and it is meant to initiate a global conversation on humanity’s future on the Moon. As such, the world café is an appropriate metaphor to illustrate the idea behind the Moon Village. In the third and final section a SWAT/PEST analysis is conducted to assess the feasibility of the Moon Village, highlighting that one of the major opportunities of the project is to promote international cooperation. The article concludes by supporting the institutionalist hypothesis that the Moon Village is arguably a translation of the ESA system to the global level, as participation is voluntary and as it builds on the strengths of each cooperation partner. As such, the Moon Village in indeed a new way of doing space on the global level.〈/p〉〈/div〉 〈/div〉

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〉

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〉

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〉

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〉

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.

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 [...]

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 World Development, Volume 123〈/p〉 〈p〉Author(s): Sara Geenen〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉By zooming in on the concept of ‘local content’, this article speaks to the debate on extractive industries and development. It challenges two fundamental assumptions of the mainstream local content literature: that production linkages will develop if an enabling environment is created, and that local content is beneficial for local people. Based on almost 600 interviews and focus groups in four mining concessions in Ghana and the Democratic Republic of Congo (DRC) it focuses on how local content policies are translated into concrete practices – more particularly around the granting of contracts and employment. In doing so it unravels the 〈em〉political〈/em〉 dimensions of local content policies and their 〈em〉structural embeddedness〈/em〉 in large-scale extractivist projects. It is argued that local content policies are implemented in complex political arenas, where the power holders use them as political instruments to enhance profit accumulation and control rents. Moreover they are embedded in the structural dynamics that permeate large-scale extractivist projects, producing (new) patterns of exclusion.〈/p〉〈/div〉 〈/div〉

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〉

Description: 〈p〉Publication date: September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Energy Policy, Volume 132〈/p〉 〈p〉Author(s): Jeoung-Sik Min, Seul-Ye Lim, Seung-Hoon Yoo〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The combined heat and power (CHP) generation, which can produce both heat and electricity at the same time, is so efficient that it can reduce energy use and even mitigate CO〈sub〉2〈/sub〉 and air pollutants emissions. For this reason, the share of CHP generation worldwide is increasing and the optimal share of CHP generation in electric power generation is a serious concern in South Korea. This article attempts to estimate the optimal share by modeling the plausible relationship between CHP generation and national output in the Cobb-Douglas production function setting and then deriving the output-maximizing share of CHP generation. The production function is estimated for annual data from 1995 to 2017 using several quantile regressions. The optimal share is computed to be 13.8%. Therefore, it is recommended that South Korea should increase the share of CHP generation slightly, given that the share was 8.6% in 2017. If the optimal CHP share instead of the actual CHP share had been achieved in 2017, GDP in 2017 could have increased by 0.82%.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Marine Policy, Volume 107〈/p〉 〈p〉Author(s): Alicia Said, Ratana Chuenpagdee〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Since the launch of the Sustainable Development Goals (SDGs) in 2015, several countries, funding organizations, environmental groups and research communities have pledged support and made commitment to help achieve these goals. SDG14: Life Below Water, for instance, has been embraced as the global goal for conservation and sustainable uses of the oceans, seas and marine resources. Among its many targets, SDG14b speaks directly to small-scale fisheries, calling for secured access to resources and markets for this sector. We argue that achieving SDG 14b requires a holistic approach encompassing several SDGs, including livelihoods, economic growth, community sustainability, strong institutions and partnerships. It is also important to align the SDG targets with the Voluntary Guidelines for Securing Sustainable Small-Scale Fisheries (SSF Guidelines), as the mutuality that exists between the scope and nature of the two instruments can help guide the formulation of appropriate governance tools. Yet, the alignment of these two instruments alone does not guarantee sustainability of small-scale fisheries, especially without an official mandate from the governments. The case in point is the European Union where small-scale fisheries are not sufficiently recognized within the Common Fisheries Policy (CFP), despite being the largest sector (75% of the fleet). Through an examination of the CFP in the context of the SSF Guidelines and the SDGs, we discuss options and possibilities for inclusive consideration of small-scale fisheries in the upcoming policy reform, which might then lead to both achieving fisheries sustainability and the SDGs in the EU.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Energy Policy, Volume 132〈/p〉 〈p〉Author(s): Ekundayo Shittu, Bruno G. Kamdem, Carmen Weigelt〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉While the role of organizational learning in improving firm performance is well documented, there are still questions on what drives technological learning. This is evident in the electricity industry where the growth of renewable energy technologies has been pervasive. Vicarious learning contributes to the adoption of emerging technologies through successful inter-firm knowledge sharing and transfer. However, there is hesitation to adoption that characterizes vicarious learning especially in the context of intra-firm learning. This paper investigates the differences in knowledge acquisition within and across electricity firms in the U.S. The learning curve model is applied to a longitudinal study of 5573 plants belonging to 1542 U.S. electricity firms between 1998 and 2010. This study finds: (i) The capacity growth of the solar photovoltaic technology is positively associated with intra-firm knowledge acquisition; (ii) The effect of financial incentives on the adoption of solar and wind technologies is higher under inter-firm learning; (iii) The higher the stringency of policy mandates, the more varied is the progress on technological change across technologies; (iv) Knowledge sharing between firms are higher for wind technology than for solar technology. These findings combine to show disparities in the learning trends of technologies across and within firms’ boundaries.〈/p〉〈/div〉 〈/div〉

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.

Description: 〈p〉Publication date: September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Energy Policy, Volume 132〈/p〉 〈p〉Author(s): Mette Talseth Solnørdal, Sverre Braathen Thyholdt〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Increased energy efficiency (EE) in manufacturing firms is important for confronting climate challenges. However, the information barrier is considered a major restriction on EE innovation. Building on the theory of absorptive capacity and the current EE literature, we argue that this barrier relates to firms' ability to assimilate and exploit information. Thus, this study's objective is to analyse firms' knowledge characteristics as determinants of EE innovation. We perform logit regressions using a Norwegian panel dataset for the period 2010–2014. The results are based on statistical correlations between data points that have potential uncertainties. Still, the main implications from our study are that prior knowledge, in terms of higher educated workforce, knowledge development, in terms of R&D capacity, and external knowledge cooperation, such as cooperation with universities and competitors, increase firms' pursuit of EE innovation. Further, the results also imply that there is an interaction effect between higher educated workforce and collaboration with universities. These results suggest that policy makers should consider firms' ability to assimilate and exploit information. This can be done by providing information according to firms' needs and absorptive capacity, and offering possibilities for firms to increase this capacity.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Energy Policy, Volume 132〈/p〉 〈p〉Author(s): Paul Neetzow, Roman Mendelevitch, Sauleh Siddiqui〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Distributed photo-voltaic (PV) generation is one of the pillars of energy transitions around the world, but its deployment in the distribution grid requires costly reinforcements and expansions. Prosumage – consisting of a household-level PV unit coupled with a battery storage system – has been proposed as an effective means to facilitate the integration of renewable energy sources and reduce distribution grid stress. However, tapping its full potential requires regulatory interventions; otherwise, system costs could rise despite increasing flexibility. We analyze the effectiveness of different policy schemes to mitigate the need for distribution capacity expansion by incentivizing beneficial storage operation. Our novel top-down modeling approach allows analyzing effects on market prices, storage dispatch, induced distribution grid requirements, system costs, and distributional implications. Based on German power system data, numerical results indicate that distribution grid requirements can be reduced through simple feed-in policies. A uniform limit on maximum grid feed-in can leave distribution system operators better off, even if they fully compensate prosumage households for foregone revenue. Policies imposing more differentiated limits at the regional level result in only marginal efficiency improvements. Complete self-sufficiency (autarky) is socially undesirable, as it confines important balancing potential and can increase system costs despite adding storage.〈/p〉〈/div〉 〈/div〉

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〉

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〉

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〉

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...

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.

Description: 〈p〉Publication date: September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Marine Policy, Volume 107〈/p〉 〈p〉Author(s): Carmen Pedroza-Gutiérrez〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Lake Chapala's fisheries have been an important economic activity for hundreds of years. However, the introduction of carpe and tilapia forced a change in the market structure, including the processing-value-adding-activities. Currently, filleting-fish is a successfully growing industry, which employs mostly women from the coastal communities. This paper aims to identify the factors influencing the division of labor in fish-processing and the determinants of bargaining-power for women. Fieldwork was carried out between 2015 and 2017 in three communities bordering Lake Chapala, Mexico (Jamay, Petatán, LaPalma), using a survey strategy based on questionnaires and formal and informal interviews to obtain qualitative and quantitative data. Findings show that fish-processing as a labor market opportunity for women is a source of bargaining power, working hours are flexible, and it is a constant and reliable source of income, unlike fishing. In each community the gender division of labor is constructed according to the local culture and the gendered-social-norms. In Petatán, fish-processing is a woman dominated activity, highly valued, with the largest average income, complementary to household income. Jamay has male (20%) and female (80%) fish-processors, fish-processing is their only source of income, and is considered an activity for single mothers (35%) or those who do not have access to better jobs. In both communities, married women's contribution to household income depends on their husband's daily earnings ranging between 25 and 75%. In LaPalma, fish-processing is a male-dominated activity, where women do not dare to work among the young filleting workers, who are breadwinners.〈/p〉〈/div〉 〈/div〉

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.

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.

Description: 〈p〉Publication date: September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Energy Policy, Volume 132〈/p〉 〈p〉Author(s): Monika Papież, Sławomir Śmiech, Katarzyna Frodyma〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉The study uses the two-stage bias-corrected DEA approach of Simar and Wilson (2007) to assess the efficiency of the EU countries in terms of their wind power investment in 2015. The set of input variables includes installed wind power capacity and average wind power density, while output variables include wind-generated electricity and three additional aspects: environmental, economic and energy security. Next, the study examines the effect of renewable energy policy regarding wind energy, the energy mix, and the offshore wind power utilisation on the wind power efficiency of the analysed countries.〈/p〉 〈p〉The results obtained reveal that the United Kingdom, Sweden, Denmark, and Ireland are the most efficient countries in terms of wind power investment. The inclusion of additional aspects demonstrates the greatest improvement of efficiency in Belgium, Cyprus, the Netherlands, Estonia and Germany.〈/p〉 〈p〉The results seem to indicate that economic instruments used within renewable energy policy have a positive effect on wind power efficiency, while policy support and regulatory instruments might negatively impact. Moreover, the results show that the energy mix explains the variation of the efficiency of the EU countries when their economic and environmental aspects are considered. The analysis of the geographic location indicates that countries with a high share of offshore wind capacity are the most efficient.〈/p〉 〈/div〉 〈/div〉

Description: 〈p〉Publication date: September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Energy Policy, Volume 132〈/p〉 〈p〉Author(s): Jian Chai, Zhaohao Wei, Yi Hu, Siping Su, Zhe George Zhang〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The rise of the shale gas industry and the need for clean, low-carbon energy transformation in China has meant that the relationship between domestic and foreign natural gas markets has become closer. Therefore, identifying the changing relationships between the major global natural gas markets and China's natural gas market has important practical value for the current Chinese domestic natural gas pricing system. This paper used the DCC-GARCH-NARDL-ARDL-ECM as the analytical framework to study these relationships, from which it was found that there was no unified global natural gas market and that China's natural gas market was not yet aligned with this market. It was shown that there was no significant asymmetry in the impact of international gas prices on China's gas prices as no flexible adjustment measures were found in China's imported natural gas pricing mechanism. Moreover, while international natural gas prices were found to have a significant impact on China's natural gas prices over the long term, there were obvious regional differences over the short term. Accordingly, new pricing policies should be designed to promote market-oriented natural gas pricing reforms in acknowledging features such as asymmetry, the difference between imported gas sources and the periodicity of price adjustment.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Energy Policy, Volume 132〈/p〉 〈p〉Author(s): Alexander Vinel, Ebrahim Mortaz〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Tapping into a large amount of renewable generation considering the inherent variability of renewable energy sources (RES) can greatly increase the risk of supply and demand imbalances in electric power delivery. One of the major components of this risk is the intermittency of both wind and solar power generation. In this paper, we show that by strategically planning for geographical and technological diversification of renewable generation capacity it is possible to reduce such risk in a RES-only US energy portfolio. We consider wind and solar as the sole sources of generation and use risk-averse stochastic optimization with Conditional Value-at-Risk (CVaR) to optimize energy generation locations and capacities in an idealized case study. The optimal RES portfolios demonstrate a significant improvement in generation profile compared to non-pooled or non-optimized alternatives. This confirms that with smart policy planning one can push the limits of the risk of imbalances in RES-only portfolios within continental United States, and highlights the need for system-wide thinking when designing a large-scale energy portfolio.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Energy Policy, Volume 132〈/p〉 〈p〉Author(s): Rebecca Lordan-Perret, Austin L. Wright, Peter Burgherr, Matteo Spada, Robert Rosner〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉To what extent can energy infrastructure become a tool for insurgents? Non-state, insurgent actors have increased attacks on critical energy infrastructure over time (Giroux et al., 2013). As critical energy infrastructure becomes more complex and interconnected, attacks on this infrastructure can have far reaching consequences not only for the economy but also for social institutions. Leveraging exogenously scheduled elections in Colombia and microlevel energy infrastructure attack data, we use multivariate linear regression to show that insurgent groups, 〈em〉Las Fuerzas Armadas Revolucionarias de Colombia (FARC)〈/em〉 and 〈em〉Ejército de Liberación〈/em〉 (ELN), time attacks on critical energy infrastructure in the months before an election. We find that the likelihood of an attack on electricity transmission lines and substations increased by 34% in the lead up to election months and that the number of attacks increased by 37% during election months. We further find these attacks are correlated with reduced voter turnout, indicating that infrastructure attacks may undermine participation in democratic institutions. These findings are particularly interesting as democratic societies—burgeoning and established—try to identify the unintended vulnerabilities to society that accompany the benefits of increased interconnectedness.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Energy Policy, Volume 132〈/p〉 〈p〉Author(s): Simone Tagliapietra, Georg Zachmann, Ottmar Edenhofer, Jean-Michel Glachant, Pedro Linares, Andreas Loeschel〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Over the last decade, the EU has pursued a proactive climate policy and has integrated a significant amount of renewables into the energy system. These efforts have proved successful and continuing along this pathway, increasing renewables and improving energy efficiency, would not require substantial policy shifts. But the EU now needs a deeper energy transformation to decarbonise in line with the Paris agreement and to seize the economic and industrial opportunities offered by this global transformation. A full-fledged energy transition is becoming economically and technically feasible, and its cost would be similar to that of maintaining the existing system, if appropriate policies and regulations are put in place. In short, the EU could benefit from deep decarbonisation irrespective of what other economies around the world do. The transition can also be socially acceptable, if the right policies are put in place to control and mitigate the distributional effects of deeper decarbonisation. The time to act is now, as policy choices made up to 2024 will define the shape of the EU energy system by 2050. This article outlines the key priorities that, in our view, should drive the EU energy and climate policy making for the new institutional cycle 2019–2024.〈/p〉〈/div〉 〈/div〉

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〉

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〉

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.

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〉

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〉

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〉

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〉

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〉

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〉

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〉

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〉

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〉

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〉

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〉

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〉

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〉

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〉

Description: 〈h3〉Abstract〈/h3〉 〈p〉How has ecological knowledge been applied in Norwegian management of hydropower and protected areas? By recognizing a diversity of environmental ‘knowledges’ and science as potentially subordinated to political and economic interests, we explain the link between ecological knowledge and management by the state and scale of knowledge, political conflict and international commitments. The analysis is guided by case-study methodology. We find that ecological knowledge has had weak impact in the management reform of protected areas and been reduced as a decision-making premise in hydropower management. Differing combinations of case-specific factors have produced these outcomes. In the case of protected areas, ecological knowledge was suppressed mainly by opposing economic interests. The hydropower case showed how competing environmental knowledge and international commitments related to renewable energy and climate change overshadowed nature management concerns. These observations highlight the importance of differentiating between types of environmental knowledge and between knowledge and interests in the study of nature management.〈/p〉

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.

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〉

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 ...

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〉

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...

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...

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〉

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〉

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〉

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〉

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〉

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...

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〉

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〉

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...

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〉

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〉

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〉

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...

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〉

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〉

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〉

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〉

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.

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〉

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.