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By
Abdelmalek, Salem; Bendoukha, Samir
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The Lengyel–Epstein model of the chlorite iodide malonic acid (CIMA) chemical reaction has received considerable attention from the research community particularly because it represented the first ever realization of the revolutionary work presented by Alan Turing in 1952. In this chapter, we review the most relevant and important studies related to the Lengyel–Epstein system. We restrict our review to those studies containing novel findings related to the dynamics of the model. In order to assist the user in linking things together and comprehending the work presented, we have made it our purpose in this chapter to start with a summary of the necessary theory behind each of the findings. The literature is classified into sections examining different characteristics of the model including the local and global asymptotic stability, the existence of Turing patterns, and the Hopf–bifurcation. In addition, we list a number of modifications made to the original system with particular focus being paid to the CDIMA reaction model, which adds a new term denoting the illumination intensity referring to the photosensitive nature of the reaction. Some simple numerical examples are also presented to illustrate the behavior of the model and the types of patterns that may arise.
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By
Mustafa, Nizami; Nezir, Veysel; Dutta, Hemen
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In this chapter, we investigate some subclasses of analytic functions in the open unit disk in the complex plane. We derive characteristic properties of the normalized Wright functions belonging to these classes and we find upper bound estimate for these functions belonging to the subclasses studied. Several sufficient conditions were obtained for the parameters of the normalized form of the Wright functions to be in this class. Some geometric properties of the integral transforms represented with the normalized Wright functions are also studied. We give some sufficient conditions for the integral operators involving normalized Wright functions to be univalent in the open unit disk. The key tools in our proofs are the Becker’s and the generalized version of the wellknown Ahlfor’s and Becker’s univalence criteria. In the final section, we introduce a Poisson distribution series, whose construction is alike Wright functions, and obtain necessary and sufficient conditions for this series belonging to the class
$$S^{*} C(\alpha ,\beta ;\gamma )$$
, and necessary and sufficient conditions for those belonging to the class
$$TS^{*} C(\alpha ,\beta ;\gamma )$$
. We also introduce two integral operators related to this series and investigate various geometric properties of these integral operators.
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By
Cho, Ilwoo
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In this paper, we study weightedcircular, and circular elements in a certain free product Banach
$$*$$
probability space
$$(\mathfrak {LS}$$
,
$$\tau ^{0})$$
induced by measurable functions on padic number fields
$$\mathbb {Q}_{p},$$
for primes p. To do that, we first constructandconsider weightedsemicircular, and semicircular elements in
$$(\mathfrak {LS},$$
$$\tau ^{0}) $$
. From our (weighted)semicircular elements, we establish (weighted)circular elements and study their free distributions by computing joint free moments of them and their adjoints. The circular law is recharacterized by joint free moments of our circular elements and their adjoints. More interestingly, our weightedcircularity dictated by padic analysis is fully characterized by weights of weightedsemicircular elements containing numbertheoretic data obtained from fixed primes p.
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By
Yang, Bicheng
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By the use of the way of real analysis and weight functions, we study some equivalent statements of Hardytype integral inequality with the general nonhomogeneous kernel, related to another inequalities, as well as the parameters and the integrals of the kernel. As applications, a few equivalent stayements of Hardytype integral inequality with the general homogeneous kernel are deduced. We also consider the other kind of integral inequality, the operator expressions, some corollaries and a few particular examples.
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By
Kaur, Deepti; Kumar, Vivek
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The objective of this chapter is to present a comparative study of simple upwind finite difference method on various nonuniform meshes existing in the literature for resolving the boundary layer of twopoint singularly perturbed problems. Our exposition begins with the Bakhvalov mesh and its modification using Padé approximation, then continues with the piecewise uniform Shishkintype meshes and to the most recent Wgrid using Lambert Wfunction. A new kind of mesh of Shishkin type that incorporates an idea by Roos et al. ( Roos, Teofanov and Uzelac, Appl. Math. Lett. 31, 7–11 (2014) [1]) using Lambert Wfunction has also been proposed and using this mesh, the method gives better results as compared to the results using the standard Shishkin mesh. For various meshes, the computed solution is uniformly convergent with respect to the small perturbation parameter. Numerical results on a test problem are presented which validate the theoretical considerations.
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By
Lahiri, Indrajit
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In 1996 Rainer Brück considered the uniqueness problem of an entire function that shares one value with its derivative. He proposed a conjecture on the single value sharing by an entire function with its first derivative[14]. Till date the conjecture of Brück is not completely resolved in its full generality. However it initiated a stream of research on a new branch of uniqueness theory. In the survey we intend to present the development of works done by several authors on the conjecture.
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By
Dutta, Hemen; Baliarsingh, P.
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The primary objective of this chapter is to provide a literature review on spectral subdivisions of difference operators and compute the spectrum and the fine spectrum of third order difference operator
$$\Delta ^3$$
over the Banach space c. The generalized difference operator
$$\Delta ^3:c\rightarrow c$$
is defined by
$$(\Delta ^3 x)_k=\sum _{i=0}^3(1)^i\left( {\begin{array}{c}3\\ i\end{array}}\right) x_{ki}=x_k3x_{k1}+3x_{k2}x_{k3}, ~(k\in \mathbb {N}_0)$$
It is presumed that
$$x_n=0$$
if
$$n<0.$$
The operator
$$\Delta ^3$$
represents a lower forth band infinite matrix. Finally, we find the estimates for the spectrum, the point spectrum, the residual spectrum and the continuous spectrum of the above operator over the Banach spaces
$$c,c_0$$
and
$$\ell _1$$
.
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By
Ghosh Roy, D. N.
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Wave propagation and scattering occupy a large part of physical, mathematical and engineering sciences. The purpose of this chapter is to present the basic mathematical theory of certain aspects of wavefields, that is, waves and fields, as they occur under various physical situations. These are considered in both scalar or acoustical and vector or electromagnetic media, that is, in the context of Helmholtz’s and Maxwell’s equations. The major emphasis is on the mathematical aspects of Green’s functions, tensors and operators. In particular, the singularities involved are discussed at length. The basic mathematical concepts, tools and techniques, necessary for the presentation, are summarized in the beginning. It is shown that mathematical analyses reveal many subtleties hidden in the wavefields that would otherwise have gone unnoticed. Detailed derivations of the equations are provided whenever possible and necessary. Also, if there are alternative ways of solving a problem, these have been presented. Finally, copious remarks and notes are included for better explaining certain points.
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By
Mandal, B. N.; De, Soumen
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The explicit solutions exist for normal incidence of the surface wave train or a single thin plane vertical barrier partially immersed or completely submerged in deep water. However, for oblique incidence of the wave train and/or for finite depth water, no such explicit solution is possible to obtain. Some approximate mathematical techniques are generally employed to solve them approximately in the sense that quantities of physical interest associated with each problem, namely the reflection and transmission coefficients, can be obtained approximately either analytically or numerically. The method of Galerkin approximations has been widely used to investigate such water wave scattering problems involving thin vertical barriers. Use of Galerkin method with basis functions involving somewhat complicated functions in solving these problems has been carried out in the literature. Choice of basis functions as simple polynomials multiplied by appropriate weights dictated by the edge conditions at the submerged end points of the barrier providing fairly good numerical estimates for the reflection and transmission coefficients have been demonstrated in this article.
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By
RiveraEstay, Viviana; GonzálezOlivares, Eduardo; RojasPalma, Alejandro; VilchesPonce, Karina
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The main peculiarity of the Leslie–Gower type models is the predator growth equation is the logistic type, in which the environmental carrying capacity is proportional to the prey population size. This assumption implies the predators are specialists. Considering that the predator is generalist, the environmental carrying capacity is modified adding a positive constant. In this work, the two simple classes of Leslie–Gower type predatorprey models are analyzed, considering a nonusual functional response, called Rosenzweig or power functional responses, being its main feature that is nondifferentiable over the vertical axis. Just as Volterra predatorprey model, when the Rosenzweig functional response is incorporated, the systems describing the models have distinctive properties from the original one; moreover, differences between them are established. One of the main properties proved is the existence of a wide set of parameter values for which a separatrix curve, dividing the phase plane in two complementary sectors. Trajectories with initial conditions upper this curve have the origin or a point over the vertical axis as their
$$\omega $$
limit. Meanwhile those trajectories with initial conditions under this curve can have a positive equilibrium point, or a limit cycle or a heteroclinic curve as their
$$\omega $$
limit. The marked differences between the two cases studied shows as a little change in the mathematical expressions to describe the models can produce rich dynamics. In other words, little perturbations over the functions representing predator interactions have significant consequences on the behavior of the solutions, without change the general structure in the classical systems.
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By
Kadak, Uğur
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The goal of this chapter is to extend various summability concepts and summability techniques by the weighted mean method with respect to the generalized difference operator involving (p, q)Gamma function. We also obtain some inclusion relations between newly proposed methods and present some illustrative examples to show that these nontrivial generalizations are more powerfull than the existing literature on this topic. Furthermore, some approximation theorems and their weighted statistical forms for functions of two variables are proved. As application, related approximation results associated with the (p, q)analogue of generalized bivariate BleimannButzerHahn operators are derived. Finally, we estimate the rate of convergence of approximating positive linear operators in terms of the modulus of continuity.
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By
Covei, DragoşPătru
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In this chapter, a diffusion model system of equations is analyzed, and entire solutions are established under some conditions on its nonlinearity. The starting point of this work is raised by the following question: can one establish new results related to the existence and asymptotic behaviour of solutions for such systems as the one considered? We believe that this question deserves investigation, which can be structured in several scientific research objectives. The results achieved in the chapter, generated by the above question, are of high interest in the academic society and industry and want to convey a great variety of applications.
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By
Fečkan, Michal
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Several results are presented on the existence or nonexistence of periodic solutions for fractional differential equations (FDEs for short) on arbitrary dimensional spaces involving Caputo fractional derivatives. The existence of Sasymptotically periodic solutions as well as periodic boundary value problems are also investigated. A rather broad variety of FDEs is considered by covering both finite dimensional FDEs and evolution FDEs in infinite dimensional spaces containing either single order or mixed orders of Caputo fractional derivatives with either finite or infinite lower limits of Caputo fractional derivatives. Different qualitative results are derived for particular types of studied FDEs, for instance, a uniform upper bound for Lyapunov exponents of solutions. Several examples are presented to illustrate theoretical results, such as fractional Duffing equations or periodically forced nonlinear fractional wave equations.
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By
Solodun, A. V.; Timokha, A. N.
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The chapter reports mathematical aspects of the Narimanov–Moiseev multimodal modelling for the liquid sloshing in rigid circular conical tanks, which perform smallmagnitude oscillatory motions with the forcing frequency close to the lowest natural sloshing frequency. To derive the corresponding nonlinear modal system (of ordinary differential equations), we introduce an infinite set of the sloshingrelated generalised coordinates governing the freesurface elevation but the velocity potential is posed as a Fourier series by the natural sloshing modes where the timedepending coefficients are treated as the generalised velocities. The employed approximate natural sloshing modes exactly satisfy both the Laplace equation and the zeroNeumann boundary condition on the wetted tank walls. The Lukovsky nonconformal mapping technique transforms the inner (conical) tank (physical) domain to an artificial upright circular cylinder, for which the singlevalued representation of the free surface is possible. Occurrence of secondary resonances for the Vshaped truncated conical tanks is evaluated. The Narimanov–Moiseev modal equations allow for deriving an analytical steadystate (periodic) solution, whose stability is studied. The latter procedure is illustrated for the case of longitudinal harmonic excitations. Standing (planar) waves and swirling as well as irregular sloshing (chaos) are established in certain frequency ranges. The corresponding amplitude response curves are drawn and extensively discussed.
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By
Colapinto, Cinzia; Jayaraman, Raja; Torre, Davide
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The Goal Programming (GP) model is an important Multiple Objective Programming (MOP) technique that has been widely utilized for strategic decision making in presence of competing and conflicting objectives. The GP model aggregates multiple objectives and allows obtaining satisfying solutions where the deviations between achievement and the aspirations levels of the attributes are to be minimized. The GP model is easy to understand and to apply: it is based on mathematical programming techniques and can be easily solved using software packages such as LINGO, MATLAB, and AMPL. The GP describes the spectrum of the Decision Maker’s preferences through a userfriendly and learning decisionmaking process. This chapter aims to present the stateoftheart of GP models and highlight its applications to strategic decision making in portfolio investments, marketing decisions and media campaign.
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By
Senthil Kumar, Beri Venkatachalapathy; Dutta, Hemen
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The study of Ulam stability of several real valued functional equations is an intensifying and an active research work going on in the present scenario. But this chapter contains the solution and examination of fundamental stabilities of various forms of complex valued additive, quadratic, cubic and quartic functional equations in the vicinity of complex Banach spaces using direct and fixed point methods.
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By
Paikray, S. K.; Dutta, Hemen
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This chapter consists of five sections. The first section is introductory in which a brief description of the development of the topic has been presented. In the second section some basic definitions related to deferred weighted
$$\mathcal {B}$$
mean method together with the regularity condition has been discussed. Moreover, based on the regular methods a theorem has been proved showing the relation between convergence and summability via our proposed summability mean. In the third section, based on our proposed method, we have established a Korovkintype approximation theorem for the functions of two variables defined on a Banach space and presented an illustrative example to show that our method is a nontrivial extension of some traditional and statistical versions of certain approximation theorems which were demonstrated in earlier works. In section four, we have established another result for the rate of the deferred weighted
$$\mathcal {B}$$
statistical convergence for the same set of functions via the modulus of continuity. Finally, in the concluding section, we have considered a number of fascinating special cases (remarks) and observations in support of our definitions and of the outcomes presented in this chapter.
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By
Wanduku, Divine
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Random dynamical processes are ubiquitous in all areas of life: in the arts, in the sciences, in the social sciences and engineering systems etc. The rates of various types of processes in life are subject to random fluctuations leading to variability in the systems. The variabilities give rise to white noise which lead to unpredictability about the processes in the systems. This chapter exhibits compartmental random dynamical models involving stochastic systems of differential equations, Markov processes, and random walk processes etc. to investigate random dynamical processes of infectious systems such as infectious diseases of humans or animals, the spread of rumours in social networks, and the spread of malicious signals on wireless sensory networks etc. A steptostep approach to identify, and represent the various constituents of random dynamic processes in infectious systems is presented. In particular, a method to derive two independent environmental white noise processes, general nonlinear incidence rates, and multiple random delays in infectious systems is presented. A unique aspect of this chapter is that the ideas, mathematical modeling techniques and analysis, and the examples are delivered through original research on the modeling of vectorborne diseases of human beings or other species. A unique method to investigate the impacts of the strengths of the noises on the overall outcome of the infectious system is presented. Numerical simulation results are presented to validate the results of the chapter.
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By
Priimenko, Viatcheslav; Vishnevskii, Mikhail; Pires, Adolfo
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The motion of an elastic conductive body in the electromagnetic field is described by the Lamé and Maxwell equations, coupled through socalled nonlinear magnetoelastic effect. In our research we follow the Dunkin–Eringen model due to its simplicity and wide application. First, we consider a mixed initialboundary value problem. In the 3Dcase the main result is the proof of the existence and uniqueness theorem. Uniqueness is proved under additional assumptions on the smoothness of the solution. In the 2Dcase we succeeded in proving the uniqueness result without additional a priori assumptions about the smoothness of the solutions obtained. The situation in a sense is similar to the Navier–Stokes equations. However, unlike the twodimensional problem for the Navier–Stokes equations, when it was sufficient to use embedding theorems to prove the uniqueness result, we make essential use of the BrézisWainger inequality, which allowed to estimate the solution in the
$$L^{\infty }$$
norm and obtain the necessary a priori estimates. In addition, we prove the solvability of an inverse problem, which consists in identifying the unknown scalar function
$$\alpha (t)$$
in the elastic force
$$\alpha (t)\mathbf {\beta }(\mathbf {x},t)$$
acting on an elastic conductive body when some additional measurement is available.
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By
Serrano, Hélia
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The theory of homogenization focuses on finding the effective macroscopic behaviour of composite materials with a heterogeneous periodic microstructure. This chapter summarizes some homogenization results on the Maxwell equations in the stationary and nonstationary regime, coupled with linear and power law constitutive relations, which are obtained by a variational technique based on the
$$\varGamma $$
convergence of associated sequences of energies. The Maxwell equations usually appear in many fields of Engineering, Mechanics and Physics.
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By
Corcino, Roberto B.
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1 Citations
In this chapter, a certain variation of Bernoulli and Euler numbers and polynomials is introduced by means of polylogarithm, particularly, the polyBernoulli and Euler numbers and polynomials. Furthermore, a certain generalization of polyBernoulli and polyEuler numbers and polynomials is defined by means of multiple polylogarithm. Common properties shared by the family of Bernoulli and Euler numbers and polynomials are discussed including recurrence relations, explicit formulas and several identities expressing these generalizations in terms of the other special numbers and functions (e.g. Stirling numbers and their generalizations).
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By
Uysal, Gümrah; Dutta, Hemen
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This chapter consists of five sections. First section is devoted to introduction part in which the description of the problem is presented and theoretical background is given. In the second section, the preliminary concepts which are utilized in the sequel are introduced. Then, the conditions under which double singular integral operators involving summation are welldefined in the space of Lebesgue measurable functions defined on different sets are presented. In the third section, Fatou type convergences of handled operators are discussed. In the fourth section, the rate of convergences with respect to obtained approximations in the preceding section are established. In the last section, we present some concluding remarks.
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By
Obodan, Natalia I.; Adlucky, Victor J.; Gromov, Vasilii A.
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The chapter presents novel approaches to predict and control buckling of thinwalled structures; mathematically, these approaches are formalized as the first and second inverse bifurcation problems for von Karman equations. Both approaches are based upon the method employed to solve the direct bifurcation problem for the equations in question. The approach considered was applied to several difficult problems of actual practice, viz., for the first inverse problem, to the problems of optimal thickness distribution and optimal external pressure distribution for a cylindrical shell, optimal curvature for a cylindrical panel as well; for the second inverse problem, to the problem to predict buckling of a cylindrical shell under an external pressure.
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By
Roider, Florian; Rümelin, Sonja; Pfleging, Bastian; Gross, Tom
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In the last decade, the number and variety of secondary tasks in modern vehicles has grown exponentially. To address this variety, drivers can choose between alternative input modalities to complete each task in the most adequate way. However, the process of switching between different modalities might cause increased cognitive effort and finally result in a loss of efficiency. Therefore, the effects of switching between input modalities have to be examined in detail. We present a user study with 18 participants that investigates these effects when switching between touch and speech input on task efficiency and driver distraction in a dualtask setup. Our results show that the sequential combination of adequate modalities for subtasks did not affect task completion times and thus reduced the duration of the entire interaction. We argue to promote modality switches and discuss the implications on application areas beyond the automotive context.
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By
Sterkenburg, Jason; Landry, Steven; Jeon, Myounghoon
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Using touchscreens while driving introduces competition for visual attention that increases crash risk. To resolve this issue, we have developed an auditorysupported air gesture system. We conducted two experiments using the driving simulator to investigate the influence of this system on driving performance, eye glance behavior, secondary task performance, and driver workload. In Experiment 1 we investigated the impact of menu layout and auditory displays with 23 participants. In Experiment 2 we compared the best systems from Experiment 1 with equivalent touchscreen systems with 24 participants. Results from Experiment 1 showed that menus arranged in 2 × 2 grids outperformed systems with 4 × 4 grids across all measures and also demonstrated that auditory displays can be used to reduce visual demands of invehicle controls. In Experiment 2 auditorysupported air gestures allowed drivers to look at the road more, showed equivalent driver workload and driving performance, and slightly decreased secondary task performance compared to touchscreens. Implications are discussed with multiple resources theory and Fitts’s law.
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By
Fank, Jana; Richardson, Natalie T.; Diermeyer, Frank
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The general role of personal assistants in form of anthropomorphised conversational, virtual or robotic agents in cars is subject to research since a few years and the first results indicate numerous positive effects of these anthropomorphised interfaces. However, no comprehensive review of the conducted studies has been comprised yet. Furthermore, existing studies on the effect of anthropomorphism mainly focus on passenger cars. This article provides a comprehensive review and summary of the conducted studies and investigates the applicability to commercial transportation, in particular to anthropomorphised interaction between truck driver and truck. In the first part of the article, a literature review describes the details, aspects and various forms of anthropomorphism as well as its observed positives effects. The review focusses on studies referring to anthropomorphism in passenger cars, complemented by relevant research results from nonautomotive disciplines. The second part of this article aims to derive innovative and applicable concepts for the anthropomorphised drivertruck interfaces using the DesignThinking approach: building on a comprehensive literature review to identify user needs and problems, an interdisciplinary expert workshop developed the two first anthropomorphised drivertruck interaction concepts. The paper finishes with carving out the differences between anthropomorphised cardriver and truckdriver interaction. The next step of research will then be the implementation of the developed interaction concepts in a first prototype followed by the respective user evaluation.
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By
Braun, Michael; Broy, Nora; Pfleging, Bastian; Alt, Florian
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In this paper we investigate how natural language interfaces can be integrated with cars in a way such that their influence on driving performance is being minimized. In particular, we focus on how speechbased interaction can be supported through a visualization of the conversation. Our work is motivated by the fact that speech interfaces (like Alexa, Siri, Cortana, etc.) are increasingly finding their way into our everyday life. We expect such interfaces to become commonplace in vehicles in the future. Cars are a challenging environment, since speech interaction here is a secondary task that should not negatively affect the primary task, that is driving. At the outset of our work, we identify the design space for such interfaces. We then compare different visualization concepts in a driving simulator study with 64 participants. Our results yield that (1) text summaries support drivers in recalling information and enhances user experience but can also increase distraction, (2) the use of keywords minimizes cognitive load and influence on driving performance, and (3) the use of icons increases the attractiveness of the interface.
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By
Löcken, Andreas; Yan, Fei; Heuten, Wilko; Boll, Susanne
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Currently, lane change decision aid systems primarily address foveal vision and thus compete for drivers’ attention with interfaces of other assistant systems. Also, alternative modalities such as acoustic perception (Mahapatra et al., in: 2008 International conference on advanced computer theory and engineering, pp 992–995.
https://doi.org/10.1109/ICACTE.2008.165
, 2008), tactile perception (Löcken et al., in: Adjunct proceedings of the 7th international conference on automotive user interfaces and interactive vehicular applications, AutomotiveUI ’15, pp 32–37. ACM, New York, NY, USA.
https://doi.org/10.1145/2809730.2809758
, 2015), or peripheral vision (Löcken et al., in: Proceedings of the 7th international conference on automotive user interfaces and interactive vehicular applications, AutomotiveUI ’15, pp 204–211. ACM, New York, NY, USA.
https://doi.org/10.1145/2799250.2799259
, 2015), have been introduced for lane change support. We are especially interested in ambient light displays (ALD) addressing peripheral vision since they can adapt to the driver’s attention using changing saliency levels (Matthews et al., in: Proceedings of the 17th Annual ACM symposium on user interface software and technology, UIST ’04, pp 247–256, ACM.
https://doi.org/10.1145/1029632.1029676
, 2004). The primary objective of this research is to compare the effect of ambient light and focal icons on driving performance and gaze behavior. We conducted two driving simulator experiments. The first experiment evaluated an ambient light cue in a free driving scenario. The second one focused on the difference in gaze behavior between ALD and focal icons, called “abstract faces with emotional expressions” (FEE). The results show that drivers decide more often for safe gaps in rightward maneuvers with ambient light cues. Similarly, drivers decide to overtake more often when the gaps are big enough with both displays in the second experiment. Regarding gaze behavior, drivers looked longer towards the forward area, and less often and shorter into the side mirrors when using ALD. This effect supports the assumption that drivers perceive the ALD with peripheral vision. In contrast, FEE did not significantly affect the gaze behavior when compared to driving without assistance. These results help us to understand the effect of different modalities on performance and gaze behavior, and to explore appropriate modalities for lane change support.
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By
FakhrHosseini, S. Maryam; Jeon, Myounghoon
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Emotions influence the way drivers process and react to internal or environmental factors, but relatively little research has focused on drivers’ emotions. Of many emotional states, anger is considered the most serious threat on the road. Therefore, having an affective intelligent system in the car that can estimate drivers’ anger and respond to it appropriately can help drivers adapt to momenttomoment changes in driving situations. To this end, we integrated behavioral, physiological, and subjective data to monitor drivers’ affective states in various driving contexts to address the question: “can selfselected music mitigate the effects of anger on driving performance?” In our experiment, three groups of participants (in total 52) drove using a driving simulator: anger without music, anger with music, and neutral without music. Results showed that angry drivers who did not listen to music had riskier driving behavior than emotionneutral drivers. Results from heart rate, oxygenation level in prefrontal cortex, and selfreport questionnaires showed that music could help angry drivers react at the similar level to emotionneutral drivers. Regarding personality characteristics, drivers who had angerexpression out style had riskier driving behavior. Divers’ workload data showed lower performance and higher effort for angry drivers without music. In conclusion, this study shows that multimodal sensing can be effectively used to holistically assess drivers’ emotional states and that music can be used as a possible multimodal strategy to mitigate the anger effects on driving performance as well as drivers’ subjective experiences.
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By
Krawczyk, Bartosz; Triguero, Isaac; García, Salvador; Woźniak, Michał; Herrera, Francisco
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1 Citations
Instance reduction techniques are data preprocessing methods originally developed to enhance the nearest neighbor rule for standard classification. They reduce the training data by selecting or generating representative examples of a given problem. These algorithms have been designed and widely analyzed in multiclass problems providing very competitive results. However, this issue was rarely addressed in the context of oneclass classification. In this specific domain a reduction of the training set may not only decrease the classification time and classifier’s complexity, but also allows us to handle internal noisy data and simplify the data description boundary. We propose two methods for achieving this goal. The first one is a flexible framework that adjusts any instance reduction method to oneclass scenario by introduction of meaningful artificial outliers. The second one is a novel modification of evolutionary instance reduction technique that is based on differential evolution and uses consistency measure for model evaluation in filter or wrapper modes. It is a powerful native oneclass solution that does not require an access to counterexamples. Both of the proposed algorithms can be applied to any type of oneclass classifier. On the basis of extensive computational experiments, we show that the proposed methods are highly efficient techniques to reduce the complexity and improve the classification performance in oneclass scenarios.
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By
Han, Xixian; Wang, Bailing; Lai, Guojun
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In many applications, dynamic skyline query is an important operation to find the interesting tuples in a potentially huge data space. Given the query point, dynamic skyline query returns tuples which are not dynamically dominated by other tuples. It is found that the existing algorithms cannot process dynamic skyline query on massive data efficiently. This paper proposes a novel dynamicsortedlistbased DDS algorithm to efficiently compute dynamic skyline results on massive data. Given the query point, the dynamic sorted list of each attribute is not materialized but generated dynamically by the sorted list of the attribute. DDS retrieves the tuples in the involved dynamic sorted lists in the roundrobin fashion until the early termination condition is satisfied, and computes the dynamic skyline results by retrieving the candidates. The pruning operation is devised to reduce the number of the retrieved candidates. The extensive experimental results, conducted on synthetic and reallife data sets, show that DDS outperforms the existing algorithms significantly.
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By
Chen, BoHeng; Li, ChengTe; Chuang, KunTa; Pang, Jun; Zhang, Yang
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With the popularity of mobile devices and various sensors, the local geographical activities of human beings can be easily accessed than ever. Yet due to the privacy concern, it is difficult to acquire the social connections among people possessed by services providers, which can benefit applications such as identifying terrorists and recommender systems. In this paper, we propose the locationaware acquaintance inference (LAI) problem, which aims at finding the acquaintances for any given query individual based on solely people’s local geographical activities, such as geotagged posts in Instagram and meeting events in Meetup, within a targeted geospatial area. We propose to leverage the concept of active learning to tackle the LAI problem. We develop a novel semisupervised model, active learningenhanced random walk (ARW), which imposes the idea of active learning into the technique of random walk with restart (RWR) in an activity graph. Specifically, we devise a series of candidate selection strategies to select unlabeled individuals for labeling and perform the different graph refinement mechanisms that reflect the labeling feedback to guide the RWR random surfer. Experiments conducted on Instagram and Meetup datasets exhibit the promising performance, compared with a set of stateoftheart methods. With a series of empirical settings, ARW is demonstrated to derive satisfying results of acquaintance inference in different real scenarios.
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By
Araujo, Miguel; Ribeiro, Pedro; Song, Hyun Ah; Faloutsos, Christos
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Given an heterogeneous social network, can we forecast its future? Can we predict who will start using a given hashtag on twitter? Can we leverage side information, such as who retweets or follows whom, to improve our membership forecasts? We present TensorCast, a novel method that forecasts timeevolving networks more accurately than current stateoftheart methods by incorporating multiple data sources in coupled tensors. TensorCast is (a) scalable, being linearithmic on the number of connections; (b) effective, achieving over 20% improved precision on top1000 forecasts of community members; (c) general, being applicable to data sources with different structure. We run our method on multiple realworld networks, including DBLP, epidemiology data, power grid data, and a Twitter temporal network with over 310 million nonzeros, where we predict the evolution of the activity of the use of political hashtags.
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By
Mir, Suhail Qadir; Quadri, S. M. K.
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The aim of the paper is to empirically evaluate the quantitative Availability metric derived from the dependencies among the individual measurable components of an information system. The Availability metric is twofold, based on the operating program and the network delay metric of the information system (for the local bound component composition the availability metric is purely based on the software/operating program, for the remote bound component composition the metric incorporates the delay metric of the network). The metric is used for measuring Availability of an information system from the security perspective, the measurements may be done at the systemdesign level or for a developed system the metric is applied to the individual working components (software/program code. The system to be evaluated is a network based video monitoring system EES and all the measurements are done using the source code of the system. The steps mentioned in the availability evaluation algorithm are followed in the evaluation process of the system and the final output of the algorithm is the availability score IAV(SyS) for the EES system. The score gives an indication of security of the system with the current design.
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Desai, Aneri Mukeshbhai; Jhaveri, Rutvij H.
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In recent years, wireless technologies have gained enormous popularity and used vastly in a variety of applications. Mobile Ad hoc Networks (MANETs) are temporary networks which are built for specific purposes; they do not require any preestablished infrastructure. The dynamic nature of these networks makes them more utilizable in ubiquitous computing. These autonomous systems of wireless mobile nodes can be set up anywhere and anytime. However, due to high mobility, absence of centralized authority and open media nature, MANETs are more vulnerable to various security threats. As a result, they are prone to more security issues as compared to the traditional networks. Ad hoc networks are highly susceptible to various types of attacks. Sequence number attacks are such hazardous attacks which greatly diminish the performance of the network in different scenarios. Sequence number attacks suck some or all data packets and discard them. In past few years, various researchers proposed different solutions for detecting the sequence number attacks. In this paper, first we review notable works done by various researchers to detect sequence number attacks. The review thoroughly presents distinct aspects of the proposed approach. In addition, we propose a proactive predictive approach to mitigate sequence number attacks which discovers misbehaving nodes during route discovery phase. The proposed approach suggests modifications in Ad hoc ondemand distance vector (AODV) routing protocol.
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Dixit, Mahendra M.; Vijaya, C.
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2 Citations
In the extant context of data compression, numerous data reduction techniques have evolved and produced many innovative solutions. These elucidations may have resulted in further complexities during physical realizations. This research endeavor put forth experimentation outcomes in the field of Discrete Cosine Transform (DCT) based image compression using modified vector quantization and prototyping the algorithm on Digital Signal Processor (DSP) TMS320C6713 platform. In addition, such an algorithm is synthesized on Virtex5 XC5VSX50T Field Programmable Gate Array (FPGA). The performance metrics used and calculated here at algorithm level are Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR_, Compression Ratio (CR), Bits per Pixel (bpp), percentage Space Saving in accordance with modified variable vector quantization levels from 10 to 90.
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Gupta, Monika; Gupta, Parul
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With the fast growth of social media, interest is increasing in detecting popular events from tweets. Event extraction is a work which identifies events from tweets or database of tweets. Each and every day, hundreds of megabytes of current stories are being added into the news archives of the major news agencies, containing much important and interesting news. Aim of this event extraction strategy is to extract and retrieve major life events from twitter data. Example of events extraction include seminar presentation, Job opening, Admission in Top universities, new technology etc. The role of this extraction is to collect major life events in the form of retrievable entries that include structured data about major life event name, location and time. Most of previous research on event extraction was mainly on textual level extraction such as News, medical systems, text summarization, whereas less work has been done on event extraction from noisy text such as tweets. For instance, tweets are short and selfcontained which make them lack useful information. The target of this research is to develop algorithm and methodology that extract and efficiently conclude major life events extracted from social media.
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Sami, Memoona; Bhatti, Sania; Baloch, Junaid; Shah, Shehram
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The success of an institution depends on its ability to acquire accurate and timely data about its operations, to manage and use this data effectively. Computer laboratory record management plays a vital role in the organization and maintenance of computers in an institution. In existing system of Mehran University of Engineering and Technology, Jamshoro lab assistants are responsible to view the current status and configuration of individual computers in LAN based computer labs. The other and very important problem which lab assistants face is that they don’t know automatically which hardware and software changes are being done on any system. In this paper a Computer Laboratory Record Management System (CLRMS) is proposed to avoid such kind of problems. CLRMS is a network based Software which automatically keeps all the information of system including hardware and software. This software secures the records of system configuration for future information reporting, user management and security management. It also updates system database if any hardware is removed or any software changes occur via automatically retrieval method. CLRMS interfaces are easily understandable hence user friendly. It generates reports for both software and hardware. It shows complete structured configuration of a computer. It will keep records that which type of user was log on the system and what tasks he has performed like installation information of software, updates of software, version and vendor information.
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Ghanbari, Elham; Shakery, Azadeh
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1 Citations
Learning to rank (LTR), as a machine learning technique for ranking tasks, has become one of the most popular research topics in the area of information retrieval (IR). Crosslingual information retrieval (CLIR), in which the language of the query is different from the language of the documents, is one of the important IR tasks that can potentially benefit from LTR. Our focus in this paper is the use of LTR for CLIR. To rank the documents in the target language in response to the query in the source language, we propose a local querydependent approach based on LTR for CLIR, which is called LQDLTR for CLIR. The core idea of LQDLTR for CLIR is the use of the local characteristics of similar queries to construct the LTR model, instead of using a single global ranking model for all queries. Since the query and the documents are in different languages, the traditional features that are used in LTR cannot be used directly for CLIR. Thus, defining appropriate features is a major step in the use of LTR for CLIR. In this paper, three categories of crosslingual features are defined: query–document features, document features, and query features. To define the crosslingual features, translation resources are used to fill the gap between the documents and the queries. Then, in LQDLTR for CLIR, a neighborhood of similar queries based on crosslingual query features is used to create a local ranking function by the LTR algorithm for a given query. The LTR algorithm uses two crosslingual feature sets, namely document features and query–document features, to learn the model. The query features that are used to identify the neighbors are not involved in the learning phase. Experimental results indicate that the CLIR performance improves with the use of crosslingual features that use several translations and their probabilities to compute the features, compared to the use of monolingual features in traditional LTR, which translate a query according to the best translation and ignore the probabilities. Moreover, experimental results show that LQDLTR for CLIR outperforms the baseline information retrieval methods and other LTR ranking models in terms of the MAP and NDCG measures.
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Nagpal, Renu; Singh, Parminder; Garg, B. P.
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The integration of concurrent bacterial foraging with emotional PSO known as concurrent bacterial foraging with emotional intelligence (CBFEI) is proposed in this paper. This technique is used to optimize the functions with multiple local optima with high dimensions and real time applications with less computational cost and better accuracy. In original BFO, the bacteria positions are updated sequentially and its performance is degraded due to fixed step size. But in CBFEI, positions of bacteria are updated concurrently, which is called as concurrent bacterial foraging and mutation is used for dynamic step size to attain accurate optima with fast convergence. The psychology factors of emotion such as joyful and sad are introduced in CBF, which is treated as mutation based on emotional intelligence. The joyful bacterium enjoys in reproducing more accurate global best while bacterium will shrink from its current position, if it is sad. The premature convergence is avoided by mutation. The seven benchmark functions are used to validate the performance of CBFEI. The different evaluation parameters and ANOVA are used to compare the results of CBFEI with other optimization algorithms. The proposed technique achieves more accurate results in terms of optimum solution and better convergence as compared to other techniques.
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Visvam Devadoss, Ambeth Kumar; Thirulokachander, Vijay Rajasekar; Visvam Devadoss, Ashok Kumar
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In today’s digital world, online journalism plays a vital role across various facets of humanity, starting from daytoday lives, to the extent of deciding presidential elections. Moreover, with the growth in popularity of automation in every possible field known to humans, automated journalism is an important domain to research into. In this, there are many factors that come into play such as relative importance of news and human emotions, apart from just statistical data. To determine the impact that artificial intelligence can have on online journalism, an automatically generating information platform using AI has been developed. The objective is to generate a fully functional information platform that creates content and news articles automatically, which is achieved by analyzing internet trends, mining data related to the trends from other news sources, classifying the data, and categorizing and generating information to resemble those written by human journalists, both grammatically and linguistically. This is achieved with the help of machine learning and NLTK (Natural Language Toolkit) modules for Python which is used to process trends across social media platforms, especially Twitter.
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Paul, Arati; Mal, Pranati; Gulgulia, Priya K.; Srivastava, Y. K.; Chowdary, V. M.
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Estate property management system mainly deals with geographical area with associated information for monitoring and managing estate properties. Geographic Information System (GIS) has potential to handle spatial and aspatial data in a single platform which makes GIS a favourable choice for estate property management system. The availability of free and open source GIS helped this technology affordable and popular in large number of applications. Hence there is a demand to develop a new or upgrade an existing nonGIS based system to a GIS based property management system. In this paper a novel case study is presented where an Oracle based system is integrated with GIS platform using freeware ArcGIS Explorer (AGX) customization. The integration of dynamic data from Oracle server is challenging due to nonsupportive database type in AGX. The issue is overcome by introducing an intermediate file handling mechanism in the GIS interface using Oracle client in .net frame work. The proposed system can run in multiple nodes in a local area network and capable of fetching data from Oracle server. It serves user queries on both spatial and aspatial data that enables spatial visualisation of aspatial data.
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Ahmed, Ammar A. Q.; Maheswari, D.
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Telecom, being a dynamic and competitive industry which contains an inherently high potential for customer churn, necessitating of accurate churn prediction models. Regular classification approaches fail to effectively predict churn due to low correlation levels between conventional performance metrics and business goals. This work presents an ensemble stacking incorporated with upliftingbased strategies for telecom churn prediction model. Evaluations have been performed based on conventional performance and a cost heuristic, with a major focus upon the cost heuristic. This mode of operation exhibits a high correlation levels between performance indicators and business goals, thus enabling the algorithm suitable for most costsensitive applications. A heterogeneous ensemble is created by using multiple algorithms to provide first level predictions. Those predictions with discrepancies are processed at the secondary level using a heuristic based combiner to provide the final predictions. Combination heuristics are finetuned based on the cost to predict more accurately concentrating on business goals. Subsequently, Customer uplifting is performed on final predictions, thus making the proposed model 50% more cost efficient than the stateoftheart ensemble models.
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Satyanarayana, S.; Tayar, Yerremsetty; Prasad, R. Siva Ram
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In recent years, multiclass imbalance data classification is a major problem in big data. In such situations, we focused on developing a new Deep Artificial Neural Network Learning Optimization (DANNLO) Classifier for large collection of imbalanced data. In our proposed work, first the dataset reduction using principal component analysis for dimensionality reduction and initial centroid is computed. Then, parallel hierarchical pillar kmeans clustering algorithm based on MapReduce is used to partitioning of an imbalanced data set into similar subset, which can improve the computational cost. The resultant clusters are given as input to the deep ANN for learning. In the next stage, deep neural network has been trained using the back propagation algorithm. In order to optimize the ndimensional weight space, firefly optimization algorithm is used. Attractiveness and distance of each firefly is computed. Hadoop is used to handle these large volumes of variable size data. Imbalanced datasets is taken from ECDC (European Centre for Disease Prevention and Control) repository. The experimental results illustrated that the proposed method can significantly improve the effectiveness in classifying imbalanced data based on TP rate, Fmeasure, Gmean measures, confusion matrix, precision, recall, and ROC. The experimental results suggests that DANNLO classifier exceed other ordinary classifiers such as SVM and Random forest classifier on tested imbalanced data sets.
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Jain, Gauri; Sharma, Manisha; Agarwal, Basant
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Classifying spam is a topic of ongoing research in the area of natural language processing, especially with the increase in the usage of the Internet for social networking. This has given rise to the increase in spam activity by the spammers who try to take commercial or noncommercial advantage by sending the spam messages. In this paper, we have implemented an evolving area of technique known as deep learning technique. A special architecture known as Long Short Term Memory (LSTM), a variant of the Recursive Neural Network (RNN) is used for spam classification. It has an ability to learn abstract features unlike traditional classifiers, where the features are handcrafted. Before using the LSTM for classification task, the text is converted into semantic word vectors with the help of word2vec, WordNet and ConceptNet. The classification results are compared with the benchmark classifiers like SVM, Naïve Bayes, ANN, kNN and Random Forest. Two corpuses are used for comparison of results: SMS Spam Collection dataset and Twitter dataset. The results are evaluated using metrics like Accuracy and F measure. The evaluation of the results shows that LSTM is able to outperform traditional machine learning methods for detection of spam with a considerable margin.
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Razzaghi, Parvin
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In this paper, a new approach to selftaught learning is proposed. Classification in target task with limited labeled target data gets improved thanks to enormous unlabeled source data. The target and source data can be drawn from different distributions. In the previous approaches, covariate shift assumption is considered in which the marginal distributions p(x) change over domains and the conditional distributions p(yx) remain the same. In our approach, we propose a new objective function which simultaneously learns a common space ℑ(.) where the conditional distributions over domains p(ℑ(x)y) remain the same and learns robust SVM classifiers for target task using both source and target data in the new representation. Hence, in the proposed objective function, the hidden label of the source data is also incorporated. We applied the proposed approach on Caltech256 and MSRC + LMO datasets and compared the performance of our algorithm to the available competing methods. Our method has a superior performance to the successful existing algorithms.
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Jiang, JyunYu; Li, ChengTe
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While users can interact with others online, more and more social networking services can help people to organize various offline social events, such as dinner parties and study groups, on the Internet. The hosts can invite friends or strangers to participate in their events in either manual or collaborative manner. However, such invitation manners may cost substantial time. Besides, the invitees may be uninterested or even unexpectedly contain spammers. In this paper, we aim at developing a predictive model to accurate recommend event participants. Specifically, given the host who initializes a social event, along with its event contexts, including the underlying social network, categories, and geolocations, our model will recommend a ranked list of candidate participants with the highest participation potential. We propose a featurebased matrix factorization model that optimizes pairwise errors of user rankings for training events, using six categories of features that represent the tendency of a user to attend the event. Experiments conducted on two eventbased social networks Meetup and Plancast and Twitter retweet data exhibit the promising performance of our approach, together with an extensive study to analyze the factors affecting users’ event participation.
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Hamid, Yasir; Shah, Firdous A; Sugumaran, M.
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Network Intrusion Detection is the process of analyzing the network traffic so as to unearth any unsafe and possibly disastrous exchanges happening over the network. In the nature of guaranteeing the confidentiality, availability, and integrity of any networking system, the accurate and speedy classification of the transactions becomes indispensable. The potential problem of all the Intrusion Detection System models at the moment, are lower detection rate for less frequent attack groups, and a higher false alarm rate. In case of networks and simulation works signal processing has been a latest and popular technique. In this study, a hybrid method based on coupling Discrete Wavelet Transforms and Artificial Neural Network (ANN) for Intrusion Detection is proposed. The imbalance of the instances across the dataset was eliminated by SMOTE based oversampling of less frequent class and random undersampling of the dominant class. A threelayer ANN was used for classification. The experimental results on KDD99 dataset advocate about the fact that the proposed model has higher accuracy, detection rate and at the same time has reduced false alarms making it suitable for realtime networks.
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Bajpai, Rochak; Sengar, Sujata; Iyer, Sridhar; Singh, Shree Prakash
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This article investigates the performance degradation of a mixed line rate (MLR) optical wavelength division multiplexing network in the presence of the combined FWM, XPM and SRS effects. For performance evaluation a novel mathematical model is developed which evaluates the results in terms of qualityfactor. Further, the simulations are performed (i) based on the optical frequency grid defined by the ITUT Recommendation G.692, and (ii) considering the ITUT compliant optical fibers viz., G.652, G. 652D, G. 653, G. 654, G.655, and LEAF. The obtained results show that among the various considered fibers and modulation format configuration cases, irrespective of spacing between the channels (i) SMF (G.652) and DSF (G.653) fibers provide the best and the worst performances, respectively, and (ii) a combination of the central channel transmitting at 40 Gbps using the duobinary modulation format with adjacent channels transmitting at 10 Gbps using OOK modulation format, provides the best performance. However, with increase in spacing between the channels, the performance is enhanced owing to mitigation of the deleterious nonlinear effects. Overall, the results clearly show that choice of the data rate of both, the central channel and its adjacent channel has a major effect on the MLR network performance.
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Yamin, Mohammad
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During the first 15 years of this century, seven thousand people have been crushed to death in stampedes. Many would argue that these fatalities could have been prevented by better control and management. Crowd management today needs to minimise the chances of occurrence of stampedes, fires and other disasters and also to deal with the ongoing threat of terrorism and outbreak of communicable diseases like EBOLA, HIV Aids, Swine Influenza H1N1, H1N2, various strands of flu, Severe Acute Respiratory Syndrome (SARS) and Middle Eastern Respiratory Syndrome (MERS). These challenges have created a need for using all available resources, especially modern tools and technology, when dealing with crowds. Radio Frequency Identification (RFID), which is already benefiting many industrial and government organisations around the world, may be useful for scanning crowded locations and hence in helping to prevent overcrowding. Other wireless technologies should also be considered for possible use in crowded events. Ideally, some of the regular crowded event locations should be transformed into smart cities. In this article we shall discuss different kinds of crowds and technologies for their management. In particular, we shall analyse cases where wireless and mobile technologies can be utilised effectively. The Hajj, which has witnessed several stampedes, is chosen as the case study but most of our findings would be applicable in other events like the Kumbh Mela.
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Gupta, Rupal; Kumar, Adesh; Sahay, Gaurangi
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This paper concentrates on the Automatic Identification System (AIS receiver) and various interfaces on FPGA which is used in the satellite. The AIS is used to detect a collision of ships. Vessels equipped with AIS provide vessel navigation information like their speed, location etc. Satellites that are fitted with SAIS receivers, receives the information and transmits it to the onboard computers. Transmission of information requires various interfaces. The paper presents the design and implementation of I2C and MILSTD1553 bus protocol, which interfaces FPGA board and on board computers in satellite and synthesized on Virtex5 FPGA in Xilinx ISE 14.2 platform. The functional simulation of the bus is also carried under different test cases. Small satellites make use of an I2C bus. For the purpose of interfacing lowspeed peripheral device on FPGA, I2C bus which is a multimaster, the twowire bidirectional serial bus is used. The MILSTD1553 bus is a standard data bus, mostly use in spacecraft onboard data handling subsystem in the military. In this, communication is in between one master terminal called bus controller and other slave terminals called remote terminal (RT). MIL 1553 bus can hold up to 30 RT.
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Betchoo, Hemchandra; Halkhoree, Roshan; Santally, Mohammad Issack; Sungkur, Roopesh Kevin
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eServices help in fostering the development of a knowledge society as more citizens and businesses can access, adopt and use government services through online channels. Existing evidence in Mauritius, however, shows low usage of online government services. The goal of this research consisted of developing a framework to guide government agencies in Mauritius in increasing the likelihood of success and uptake of eServices. This study uses a novel approach, i.e. the RealTime Delphi method based on a consensus of expert opinions as the main research method to identify critical success factors influencing the user uptake of eServices. Moreover, it not only proposes a framework as a solution but also operationalises the framework into a tool which can be used in practice to predict the likelihood of takeup of an eService implemented by a Government agency. Additionally, the tool was made flexible so that it can be used not only in the Mauritian context but can also be adapted to reflect the context of other countries. Government policymakers can leverage on the developed framework to formulate better policies and guidelines for the successful takeup of online public services in Mauritius. Furthermore, eService owners can use the tool to evaluate their eService strategy and identify priority areas where resources and effort have to be dedicated so that their eServices can be successfully adopted and used by citizens. The validity and effectiveness of the tool was evaluated using existing eServices in Mauritius having varying user uptake and the results were found to reflect to a great extent the prevailing uptake situation of existing eServices.
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Sonkar, S. K.; Kharat, M. U.
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Virtual machine (VM) prediction and an effective resource management are the attractive areas in the cloud environment. VM prediction is an important task to execute the jobs for delay minimization and unnecessary states avoidance. Cloud computing attracted towards the increase in a number of applications that run on remote servers in parallel manner. Increase in parallelism reduces the CPU utilization adversely. Hence, the proper VM prediction and management are necessary stages in provisioning scheme. Also time required for allocating jobs is more in existing algorithms due to the number of computations involved. Therefore a novel algorithm is required to improve the performance of the job allocation with makespan reduction. In this paper the new algorithm is proposed that includes the VM capacity and execution time for load prediction and performance improvement purpose. Our proposed research work utilizes the VM clustering and optimization algorithms to improve job sequencing performance. The cost computation prior to clustering includes the VM capacity as a major factor. Clustering of VM with highcost and isolation of lowcost and highcost clusters reduces the searching time of VM and solve the imbalance state problem in traditional methods. The optimization algorithm with suitable initialization function reduces the time and steps for selection of VM for suitable job. The proposed model outperformance is established by the selected parameters.
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Yadav, Ramjeet Singh
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As we know that in this new era, the availability of modern data sets is massive. It is very difficult to find the variation and appropriate class for a data set. So, this paper introduces a comparison between fuzzy and vague sets for handling Structured Query Language (SQL) processing problems. This paper proposed a new method to convert crisp set into vague set with help of Positive Ordered Transformation formula (POTF). Further, vague sets are converted into fuzzy sets with help of Transforming Vague Set into Fuzzy Set method proposed by Liu et al. (Trans Comput Sci II LNCS 5152:133–144, 2008). Further the similarity measures have been used to obtain similar tuple for classical fuzzy, vague and converted fuzzy sets based on SQL query processing. This proposed system diverse a resultant as a set based on supply limit/αcut for fuzzy/vagueness/unclear information. After testing through many cases, this paper discussed a very good finding about proposed method for SQL query processing problems.
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Malik, Sunesh; Reddlapalli, Rama Kishore
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In today’s era, Fortifying robustness and imperceptibility of digital watermarking has become non trivial. In this paper, a digital image watermarking algorithm based on entropy of blocks and histogram is proposed to improve imperceptibility. In this, first host image is divided into blocks and then blocks are culled on the basis of entropy value for watermark embedding. After that, watermark is embedded into selected blocks by using histogram shape method. Histogram shape method divides selected blocks into groups and further locations of pixels within groups are identified for watermark embedding. Histogram shape method makes algorithm more robust against attacks. The integration of block based histogram shape approach and entropy makes algorithm more imperceptible. Experiment results demonstrate that proposed method has an excellent imperceptibility. Proposed method also resists against noise and scaling attacks.
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Rath, Mamata
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Resource provision and security requirement for largescale cloud applications is a challenging issue while design of any web based client oriented business application. Extensive research on various issues in real environment has reported that ondemand provision of resources in cloud where connectivity issue persists for heterogeneous communication channel, requires developers to consider network infrastructure and the environment, which is beyond certain control. In wireless mobile network, the network condition is always changeable and cannot be predicted neither controlled. In this paper resource provisioning and checking of continuous availability of resource to the clients has been carried out using a web based application software that uses cloud servers and data centers. Secondly, an improved security feature has been added to the mobile stations in a registered group to eliminate the unnecessary utilization of resource by unauthorized station which maliciously consumes bandwidth and other facility provided by the cloud provider. Simulation results show that the proposed system performs better than other similar approaches when compared with specific network parameters.
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Malar, B.; Nadarajan, R.; Gowri Thangam, J.
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Isotonic separation is a classification technique which constructs a model by transforming the training set into a linear programming problem (LPP). It is computationally expensive to solve largescale LPPs using traditional methods when data set grows. This paper proposes a hybrid binary classification algorithm, metaheuristic isotonic separation with particle swarm optimization and convergence criterion (MeHeIS–CPSO), in which a particle swarm optimizationbased metaheuristic is embedded in the training phase to find a solution for LPP. The proposed framework formulates the LPP as a directed acyclic graph (DAG) and arranges decision variables using topological sort. It obtains a new threshold value from training set and sets up a convergence criterion using this threshold. It also deploys a new correlation coefficientbased supervised feature selection technique to select isotonic features and improves predictive accuracy of the classifier. Experiments are conducted on publicly available data sets and synthetic data set. Theoretical, empirical, and statistical analyses show that MeHeIS–CPSO is superior to its predecessors in terms of training time and predictive ability on large data sets. It also outperforms stateoftheart machine learning and isotonic classification techniques in terms of predictive performance on small and largescale data sets.
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Aggarwal, Manish
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We learn the parameters of the popular multinomial logit model to gain insights about a DM’s decision process. We accomplish this objective through the recent algorithmic advances in the emerging field of preference learning. The empirical evaluation of the proposed approach is performed on a set of 12 publicly available benchmark datasets. First experimental results suggest that our approach is not only intuitively appealing, but also competitive to stateoftheart preference learning methods in terms of the prediction accuracy.
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Wu, Xiaolan; Zhang, Chengzhi
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Specialized academic social networking sites are gaining popularity in scientific communication. A huge volume of interdisciplinary information is generated when researchers from multiple disciplines participate in scientific communication, which makes it possible to discover interdisciplinary users from a range of disciplines. In this study we analyze ScienceNet, one of the most wellknown academic social networking sites in China, to find highimpact interdisciplinary users. We focus on the discipline distribution of friends and adopt phylogenetic species evenness on discipline phylogenetic trees to find 128 highimpact interdisciplinary users. A questionnaire was then sent to these academics to test the accuracy of this method. The questionnaire results show that our approach can determine authority users who span specific disciplines. Thus our approach will be useful for finding interdisciplinary collaborators and academic social networking siterelated international peer reviewers.
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Nicolaisen, Jeppe; Frandsen, Tove Faber
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This paper presents a largescale study of the phenomenon ‘uncitedness’. A literature review indicates that uncitedness is related to at least three factors: Field, document type, and time. To explore these factors and their mutual influence further, and at much larger scale than previous studies, the paper focuses on seven subject areas (arts and humanities; social sciences; computer science; mathematics; engineering; medicine; physics and astronomy), seven document types (articles; reviews; notes; letters; conference papers; books; book chapters), and a 20year publication window (1996–2015). Documents are searched in Scopus, and retrieved yearbyyear, disciplinebydiscipline, and for each individual document type (total: 29,472,184 documents; 7,508,741 uncited documents). The results show great variance in uncitedness ratios between subject areas and document types. This is probably caused by a somewhat tacitly agreed upon genre hierarchy existing in all subject areas, yet with important local traits and differences. The importance of the timedimension is documented. Time to first citation varies a great deal between subject areas, and the uncitedness ratio is consequently shown to be quite sensitive to the length of citation windows.
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Huang, Muhsuan; Shaw, WangChing; Lin, ChiShiou
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Journal Citation Reports (JCR) and its journal ranking in terms of impact factor are highly influential in research evaluation. Comparisons of impact factor are valuable only when journals are of the same subject. However, a particular JCR subject category, Information Science and Library Science (IS–LS), combines two different study fields, namely Management Information Systems (MIS) and Library and Information Science (LIS). The combination of these subjects in a single category has caused the undesirable suppression of LIS journals in annual rankings. This study used papers and citation data from 88 IS–LS journals published between 2005 and 2014 to study subfield differences between MIS and LIS and their impact factor performances over 10 years. The study further examined the subfield differences within LIS, examining the differences and performances of library science, information science, and scientometric research. The results indicate that MIS and LIS are considerably different in terms of publishing and citation characteristics, cited subjects, and author affiliations. Moreover, significant differences were observed among LIS subfields. Furthermore, the results suggested that MIS and LIS pertain to two different research communities. Stakeholders must consider this difference and allow reasonable subfield differentiation and rank adjustment when using JCR for constructive research evaluations.
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Gulzar, Yonis; Alwan, Ali A.; Abdullah, Radhwan Mohamed; Xin, Qin; Swidan, Marwa B.
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Skyline queries have been extensively incorporated in various contemporary database applications. The list includes but is not limited to multicriteria decisionmaking systems, decision support systems, and recommendation systems. Due to its great benefits and wide application range, many skyline algorithms have already been proposed in numerous data settings. Nonetheless, most researchers presume the completion of data meaning that all data item values are available. Since this assumption cannot be sustained in a large number of realworld database applications, the existing algorithms are rather inadequate to be directly applied on a database with incomplete data. In such cases, processing skyline queries on incomplete data incur exhaustive pairwise comparisons between data items, which may lead to loss of the transitivity property of the skyline technique. Losing the transitivity property may in turn give rise to the problem of cyclic dominance. In order to address these issues, we propose a new skyline algorithm called Sortingbased Cluster Skyline Algorithm (SCSA) that combines the sorting and partitioning techniques and simplifies the skyline computation on an incomplete dataset. These two techniques help boost the skyline process and avoid many unnecessary pairwise comparisons between data items to prune the dominated data items. The comprehensive experiments carried out on both synthetic and reallife datasets demonstrate the effectiveness and versatility of our approach as compared to the currently used approaches.
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Tubishat, Mohammad; Abushariah, Mohammad A. M.; Idris, Norisma; Aljarah, Ibrahim
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To help individuals or companies make a systematic and more accurate decisions, sentiment analysis (SA) is used to evaluate the polarity of reviews. In SA, feature selection phase is an important phase for machine learning classifiers specifically when the datasets used in training is huge. Whale Optimization Algorithm (WOA) is one of the recent metaheuristic optimization algorithm that mimics the whale hunting mechanism. However, WOA suffers from the same problem faced by many other optimization algorithms and tend to fall in local optima. To overcome these problems, two improvements for WOA algorithm are proposed in this paper. The first improvement includes using Elite OppositionBased Learning (EOBL) at initialization phase of WOA. The second improvement involves the incorporation of evolutionary operators from Differential Evolution algorithm at the end of each WOA iteration including mutation, crossover, and selection operators. In addition, we also used Information Gain (IG) as a filter features selection technique with WOA using Support Vector Machine (SVM) classifier to reduce the search space explored by WOA. To verify our proposed approach, four Arabic benchmark datasets for sentiment analysis are used since there are only a few studies in sentiment analysis conducted for Arabic language as compared to English. The proposed algorithm is compared with six wellknown optimization algorithms and two deep learning algorithms. The comprehensive experiments results show that the proposed algorithm outperforms all other algorithms in terms of sentiment analysis classification accuracy through finding the best solutions, while its also minimizes the number of selected features.
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Saini, Naveen; Saha, Sriparna; Harsh, Aditya; Bhattacharyya, Pushpak
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Multiobjective clustering refers to the partitioning of a given collection of objects into various Kgroups based on some similarity/dissimilarity criterion while optimizing different partition quality measures simultaneously. The current paper proposes an automated decomposition based multiobjective clustering technique, SOMDEA_clust, which is a fusion of selforganizing map (SOM) and multiobjective differential evolution. A novel reproduction operator is designed where the ensemble of multiple neighborhoods extracted using selforganizing map is used for constructing the variable mating pool size. The probabilities of selecting different sizes of the neighborhood are updated based on their performances in generating new improved solutions in the last few generations. Decomposition based selection scheme is also utilized in our paper which divides the multiobjective optimization (MOO) problem into a number of single objective subproblems. The objective functions corresponding to these subproblems are optimized in a collaborative manner by the use of MOO. The potentiality of the proposed framework is shown for clustering four reallife data sets and five artificial data sets in comparison to some existing multiobjective based clustering techniques, namely MOCK, SMEA_clust, MEA_clust, a single objective based genetic clustering technique, SOGA and a traditional clustering technique, Kmeans. To show the utility of SOM based reproduction operators, another decomposition based multiobjective clustering technique (MDEA_clust) without the use of SOM based operators is also developed in this paper. In order to show the efficacy of the proposed clustering technique in handling large data sets, two large scale datasets having more than 5000 data points are also utilized. As a reallife application, the proposed clustering technique is applied for scientific/web document clustering where a set of scientific/web documents are partitioned based on their contentsimilarities. Semantic representation is utilized to covert the text document into a real vector. Experimental results clearly illustrate the effectiveness of fusion of SOM and DE in developing an effective clustering technique.
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Dölz, Jürgen; Gerig, Thomas; Lüthi, Marcel; Harbrecht, Helmut; Vetter, Thomas
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Gaussian Process Morphable Models (GPMMs) unify a variety of nonrigid deformation models for surface and image registration. Deformation models, such as Bsplines, radial basis functions, and PCA models are defined as a probability distribution using a Gaussian process. The method depends heavily on the lowrank approximation of the Gaussian process, which is mandatory to obtain a parametric representation of the model. In this article, we propose the use of the pivoted Cholesky decomposition for this task, which has the following advantages: (1) Compared to the current state of the art used in GPMMs, it provides a fully controllable approximation error. The algorithm greedily computes new basis functions until the userdefined approximation accuracy is reached. (2) Unlike the currently used approach, this method can be used in a blackboxlike scenario, whereas the method automatically chooses the amount of basis functions for a given model and accuracy. (3) We propose the Newton basis as an alternative basis for GPMMs. The proposed basis does not need an SVD computation and can be iteratively refined. We show that the proposed basis functions achieve competitive registration results while providing the mentioned advantages for its computation.
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C C, Nisha; Mohan, Anuraj
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A recommender system is an active tool for information filtering that can be deployed in a complex and dynamic online environment to provide the most relevant and accurate content to the users based on their unique preferences and tastes. The recent direction towards enhancing the recommender system leverages deep learning techniques and trust information. However, building a unified model for a recommender system that integrates deep architecture with trust information is an open challenge. Here, we propose a hybrid method by modeling a joint optimization function which extends deep Autoencoder with topk semantic social information. We use network representation learning methods to capture the implicit semantic social information. We conducted experiments with various realworld data sets and evaluated the performance of the proposed method using different evaluation measures. Experimental results show the performance improvement of the proposed system compared to stateoftheart methods.
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Kim, Jinseok; Diesner, Jana
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Link prediction in collaboration networks is often solved by identifying structural properties of existing nodes that are disconnected at one point in time, and that share a link later on. The maximally possible recall rate or upper bound of this approach’s success is capped by the proportion of links that are formed among existing nodes embedded in these properties. Consequentially, sustained links as well as links that involve one or two new network participants are typically not predicted. The purpose of this study is to highlight formational constraints that need to be considered to increase the practical value of link prediction methods targeted for collaboration networks. In this study, we identify the distribution of basic link formation types based on four largescale, overtime collaboration networks, showing that roughly speaking, 25% of links represent continued collaborations, 25% of links are new collaborations between existing authors, and 50% are formed between an existing author and a new network member. This implies that for collaboration networks, increasing the accuracy of computational link prediction solutions may not be a reasonable goal when the ratio of collaboration links that are eligible to the classic link prediction process is low.
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Habib, Raja; Afzal, Muhammad Tanvir
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Digital libraries suffer from the problem of information overload due to immense proliferation of research papers in journals and conference papers. This makes it challenging for researchers to access the relevant research papers. Fortunately, research paper recommendation systems offer a solution to this dilemma by filtering all the available information and delivering what is most relevant to the user. Researchers have proposed numerous approaches for research paper recommendation which are based on metadata, content, citation analysis, collaborative filtering, etc. Approaches based on citation analysis, including cocitation and bibliographic coupling, have proven to be significant. Researchers have extended the cocitation approach to include content analysis and citation proximity analysis and this has led to improvement in the accuracy of recommendations. However, in cocitation analysis, similarity between papers is discovered based on the frequency of cocited papers in different research papers that can belong to different areas. Bibliographic coupling, on the other hand, determines the relevance between two papers based on their common references. Therefore, bibliographic coupling has inherited the benefits of recommending relevant papers; however, traditional bibliographic coupling does not consider the citing patterns of common references in different logical sections of the citing papers. Since the use of citation proximity analysis in cocitation has improved the accuracy of paper recommendation, this paper proposes a paper recommendation approach that extends the traditional bibliographic coupling by exploiting the distribution of citations in logical sections in bibliographically coupled papers. Comprehensive automated evaluation utilizing Jensen Shannon Divergence was conducted to evaluate the proposed approach. The results showed significant improvement over traditional bibliographic coupling and contentbased research paper recommendation.
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Chaouch, Imen; Driss, Olfa Belkahla; Ghedira, Khaled
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Distributed scheduling problems are among the most investigated research topics in the fields of Operational Research, and represents one of the greatest challenges faced by industrialists and researchers today. The Distributed Job shop Scheduling Problem (DJSP) deals with the assignment of jobs to factories and with determining the sequence of operations on each machine in distributed manufacturing environments. The objective is to minimize the global makespan over all the factories. Since the problem is NPhard to solve, one option to cope with this intractability is to use an approximation algorithm that guarantees nearoptimal solutions quickly. Ant based algorithm has proved to be very effective and efficient in numerous scheduling problems, such as permutation flow shop scheduling, flexible job shop scheduling problems and network scheduling, etc. This paper proposes a hybrid ant colony algorithm combined with local search to solve the Distributed Job shop Scheduling Problem. A novel dynamic assignment rule of jobs to factories is also proposed. Furthermore, the Taguchi method for robust design is adopted for finding the optimum combination of parameters of the antbased algorithm. To validate the performance of the proposed algorithm, intensive experiments are carried out on 480 large instances derived from wellknown classical jobshop scheduling benchmarks. Also, we show that our algorithm can process up to 10 factories. The results prove the efficiency of the proposed algorithm in comparison with others.
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Hu, Xiaocheng; Sheng, Cheng; Tao, Yufei
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We revisit the problem of constructing an external memory data structure on a planar subdivision formed by n segments to answer point location queries optimally in
$$O(\log _B n)$$
I/Os. The objective is to achieve the I/O cost of
$$ sort (n) = O(\frac{n}{B} \log _{M/B} \frac{n}{B})$$
, where B is the number of words in a disk block, and M being the number of words in memory. The previous algorithms are able to achieve this either in expectation or under the tall cache assumption of
$$M \ge B^2$$
. We present the first algorithm that solves the problem deterministically for all values of M and B satisfying
$$M \ge 2B$$
.
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Ben Basat, Ran; Einziger, Gil; Friedman, Roy; Kassner, Yaron
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This paper considers the problem of estimating the sum the last
$$W$$
elements of a stream of integers in
$$\left\{ 0,1,\ldots , R \right\} $$
. Specifically, we study the memory requirements for computing a
$$ R W\varepsilon $$
additive approximation for the window’s sum. We derive a lower bound of
$$W\log \left\lfloor {\frac{1}{2W\varepsilon } + 1}\right\rfloor $$
bits when
$$\varepsilon \le 1/2W$$
and show a matching succinct algorithm that uses
$$(1+o(1)) \left( {W\log \left\lfloor {\frac{1}{2W\varepsilon } + 1}\right\rfloor }\right) $$
bits. Next, we prove a
$$(1o(1)) \varepsilon ^{1} /2$$
bits lower bound when
$$\varepsilon =\omega \left( {W^{1}}\right) \wedge \varepsilon =o(\log ^{1}W)$$
and provide a succinct algorithm that requires
$$(1+o(1)) \varepsilon ^{1} /2$$
bits. We show that when
$$\varepsilon =\varOmega \left( {\log ^{1}W}\right) $$
any solution to the problem must consume at least
$$(1o(1))\cdot \left( {{ \varepsilon ^{1} /2}+\log W}\right) $$
bits, while our algorithm needs
$$(1+o(1))\cdot \left( {{ \varepsilon ^{1} /2}+2\log W}\right) $$
bits. Finally, we show that our lower bounds generalize to randomized algorithms as well, while our algorithms are deterministic and can process elements and answer queries in O(1) worstcase time.
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He, ZhiFen; Yang, Ming
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Multilabel learning deals with the problem where each instance may be associated with multiple labels simultaneously, and how to discover and exploit the label correlations is one of important research issues. In this paper, we propose a novel sparse and lowrank representationbased method for multilabel classification (SLMLC), which can automatically exploit the asymmetric correlations among labels while learning the model parameters in a unified learning framework. More specifically, we assume that the weight matrix is divided into a sparse matrix and a lowrank matrix, where the sparse and lowrank matrices are utilized to capture the specific features that are relevant to each label and the shared feature subspace among all labels, respectively. Then, we integrate multilabel classification and label correlations into a joint learning framework to learn the correlations among labels and the model parameters simultaneously. Lastly, the formulation is transformed into its convex surrogate due to its nonconvexity, and we solve it by developing an alternating iterative method. Experimental results on fifteen data sets in terms of six evaluation criteria show that SLMLC achieves superior performance compared to the stateoftheart multilabel classification algorithms.
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Sanches, Silvio R. R.; Oliveira, Claiton; Sementille, Antonio C.; Freire, Valdinei
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Background subtraction is the prerequisite for a wide range of applications including video surveillance, smart environments and content retrieval. Real environments present some challenging situations even for the most recent algorithms, such as shadows, illumination changes, dynamic background, among others. If a real environment is previously known and the challenging situations of this environment can be predicted, the choice of an appropriate algorithm to deal with such situations may be essential for obtaining better segmentation results. In our work, we identify the main situations that affect the performance of background subtraction algorithms and present a classification of these challenging situations. In addition, we present a solution that uses videos and groundtruths from existing datasets to evaluate the performance of segmentation algorithms when they need to deal with a specific challenging situation.
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Schlipf, Lena; Schmidt, Jens M.
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Canonical orderings and their relatives such as stnumberings have been used as a key tool in algorithmic graph theory for the last decades. Recently, a unifying link behind all these orders has been shown that links them to wellknown graph decompositions into parts that have a prescribed vertexconnectivity. Despite extensive interest in canonical orderings, no analogue of this unifying concept is known for edgeconnectivity. In this paper, we establish such a concept named edgeorders and show how to compute (1, 1)edgeorders of 2edgeconnected graphs as well as (2, 1)edgeorders of 3edgeconnected graphs in linear time, respectively. While the former can be seen as the edgevariants of stnumberings, the latter are the edgevariants of Mondshein sequences and nonseparating ear decompositions. The methods that we use for obtaining such edgeorders differ considerably in almost all details from the ones used for their vertexcounterparts, as different graphtheoretic constructions are used in the inductive proof and standard reductions from edge to vertexconnectivity are bound to fail. As a first application, we consider the famous EdgeIndependent Spanning Tree Conjecture, which asserts that every kedgeconnected graph contains k rooted spanning trees that are pairwise edgeindependent. We illustrate the impact of the above edgeorders by deducing algorithms that construct 2 and 3edge independent spanning trees of 2 and 3edgeconnected graphs, the latter of which improves the best known running time from
$$O(n^2)$$
to linear time.
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By
Huang, Zengfeng; Peng, Pan
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In this paper we study graph problems in the dynamic streaming model, where the input is defined by a sequence of edge insertions and deletions. As many natural problems require
$$\varOmega (n)$$
space, where n is the number of vertices, existing works mainly focused on designing
$${O}(n\cdot \mathrm {poly}\log n)$$
space algorithms. Although sublinear in the number of edges for dense graphs, it could still be too large for many applications (e.g., n is huge or the graph is sparse). In this work, we give singlepass algorithms beating this space barrier for two classes of problems. We present o(n) space algorithms for estimating the number of connected components with additive error
$$\varepsilon n$$
of a general graph and
$$(1+\varepsilon )$$
approximating the weight of the minimum spanning tree of a connected graph with bounded edge weights, for any small constant
$$\varepsilon >0$$
. The latter improves upon the previous
$$O(n\cdot \mathrm {poly}\log n)$$
space algorithm given by Ahn et al. (SODA 2012) for the same class of graphs. We initiate the study of approximate graph property testing in the dynamic streaming model, where we want to distinguish graphs satisfying the property from graphs that are
$$\varepsilon $$
far from having the property. We consider the problem of testing kedge connectivity, kvertex connectivity, cyclefreeness and bipartiteness (of planar graphs), for which, we provide algorithms using roughly
$${O}(n^{1\varepsilon }\cdot \mathrm {poly}\log n)$$
space, which is o(n) for any constant
$$\varepsilon $$
. To complement our algorithms, we present
$$\varOmega (n^{1O(\varepsilon )})$$
space lower bounds for these problems, which show that such a dependence on
$$\varepsilon $$
is necessary.
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Boyar, Joan; Eidenbenz, Stephan J.; Favrholdt, Lene M.; Kotrbčík, Michal; Larsen, Kim S.
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This paper is devoted to the online dominating set problem and its variants. We believe the paper represents the first systematic study of the effect of two limitations of online algorithms: making irrevocable decisions while not knowing the future, and being incremental, i.e., having to maintain solutions to all prefixes of the input. This is quantified through competitive analyses of online algorithms against two optimal algorithms, both knowing the entire input, but only one having to be incremental. We also consider the competitive ratio of the weaker of the two optimal algorithms against the other. We consider important graph classes, distinguishing between connected and not necessarily connected graphs. For the classic graph classes of trees, bipartite, planar, and general graphs, we obtain tight results in almost all cases. We also derive upper and lower bounds for the class of boundeddegree graphs. From these analyses, we get detailed information regarding the significance of the necessary requirement that online algorithms be incremental. In some cases, having to be incremental fully accounts for the online algorithm’s disadvantage.
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Zhao, Xuewu; Ji, Junzhong; Wang, Xing
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Dynamic brain functional parcellation is an important way to reveal the dynamics of brain function. However, current dynamic brain functional parcellation methods can not meet the need to clearly understand the dynamics. This paper presents a dynamic brain functional parcellation method based on sliding window and artificial bee colony (ABC) algorithm (called SWABC). In SWABC, a functional connectivity similarity minimum criterion (FCSMC) is firstly developed for determining the length of a sliding window and functional connectivity matrices are calculated with Pearson correlation and windowed time series of voxels. Then, an improved ABC is employed to identify functional states through clustering these matrices, where a hybrid search strategy and a dynamic radius constraint are respectively designed for employed bee search and scout bee search to enhance the search capability of ABC. Next, functional connectivity between voxels in each functional state is computed by concatenating time series belonging to the same state, and functional parcellation results for all functional states are achieved by performing the improved ABC. Finally, with comparison to other four algorithms, the experimental results on fMRI data of posterior cingulate cortex show that SWABC not only has better search capability, but also can yield reasonable functional states and corresponding functional parcellation results with stronger functional consistency and regional continuity. Moreover, the rationality of functional parcellation results from SWABC is also verified by functional connectivity fingerprints of subregions in each of them.
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By
Herrmann, Marc; Herzog, Roland; Schmidt, Stephan; VidalNúñez, José; Wachsmuth, Gerd
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The total variation (TV)seminorm is considered for piecewise polynomial, globally discontinuous (DG) and continuous (CG) finite element functions on simplicial meshes. A novel, discrete variant (DTV) based on a nodal quadrature formula is defined. DTV has favorable properties, compared to the original TVseminorm for finite element functions. These include a convenient dual representation in terms of the supremum over the space of Raviart–Thomas finite element functions, subject to a set of simple constraints. It can therefore be shown that a variety of algorithms for classical image reconstruction problems, including TV
$$L^2$$
denoising and inpainting, can be implemented in low and higherorder finite element spaces with the same efficiency as their counterparts originally developed for images on Cartesian grids.
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By
Golan, Shay; Kopelowitz, Tsvi; Porat, Ely
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In the pattern matching with d wildcards problem one is given a text T of length n and a pattern P of length m that contains d wildcard characters, each denoted by a special symbol ‘?’. A wildcard character matches any other character. The goal is to establish for each mlength substring of T whether it matches P. In the streaming model variant of the pattern matching with d wildcards problem the text T arrives one character at a time and the goal is to report, before the next character arrives, if the last m characters match P while using only o(m) words of space. In this paper we introduce two new algorithms for the d wildcard pattern matching problem in the streaming model. The first is a randomized Monte Carlo algorithm that is parameterized by a constant
$$0\le \delta \le 1$$
. This algorithm uses
$$\tilde{O}(d^{1\delta })$$
amortized time per character and
$$\tilde{O}(d^{1+\delta })$$
words of space. The second algorithm, which is used as a black box in the first algorithm, is a randomized Monte Carlo algorithm which uses
$$O(d+\log m)$$
worstcase time per character and
$$O(d\log m)$$
words of space.
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Iqbal, Waleed; Qadir, Junaid; Tyson, Gareth; Mian, Adnan Noor; Hassan, Saeedul; Crowcroft, Jon
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Computer networking is a major research discipline in computer science, electrical engineering, and computer engineering. The field has been actively growing, in terms of both research and development, for the past hundred years. This study uses the article content and metadata of four important computer networking periodicals—IEEE Communications Surveys and Tutorials (COMST), IEEE/ACM Transactions on Networking (TON), ACM Special Interest Group on Data Communications (SIGCOMM), and IEEE International Conference on Computer Communications (INFOCOM)—obtained using ACM, IEEE Xplore, Scopus and CrossRef, for an 18year period (2000–2017) to address important bibliometrics questions. All of the venues are prestigious, yet they publish quite different research. The first two of these periodicals (COMST and TON) are highly reputed journals of the fields while SIGCOMM and INFOCOM are considered top conferences of the field. SIGCOMM and INFOCOM publish new original research. TON has a similar genre and publishes new original research as well as the extended versions of different research published in the conferences such as SIGCOMM and INFOCOM, while COMST publishes surveys and reviews (which not only summarize previous works but highlight future research opportunities). In this study, we aim to track the coevolution of trends in the COMST and TON journals and compare them to the publication trends in INFOCOM and SIGCOMM. Our analyses of the computer networking literature include: (a) metadata analysis; (b) contentbased analysis; and (c) citation analysis. In addition, we identify the significant trends and the most influential authors, institutes and countries, based on the publication count as well as article citations. Through this study, we are proposing a methodology and framework for performing a comprehensive bibliometric analysis on computer networking research. To the best of our knowledge, no such study has been undertaken in computer networking until now.
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Deligkas, Argyrios; Mertzios, George B.; Spirakis, Paul G.
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In the classical binary search in a path the aim is to detect an unknown target by asking as few queries as possible, where each query reveals the direction to the target. This binary search algorithm has been recently extended by EmamjomehZadeh et al. (in: Proceedings of the 48th annual ACM SIGACT symposium on theory of computing, STOC 2016, Cambridge, pp. 519–532, 2016) to the problem of detecting a target in an arbitrary graph. Similarly to the classical case in the path, the algorithm of EmamjomehZadeh et al. maintains a candidates’ set for the target, while each query asks an appropriately chosen vertex—the “median”—which minimises a potential
$$\varPhi $$
among the vertices of the candidates’ set. In this paper we address three open questions posed by EmamjomehZadeh et al., namely (a) detecting a target when the query response is a direction to an approximately shortest path to the target, (b) detecting a target when querying a vertex that is an approximate median of the current candidates’ set (instead of an exact one), and (c) detecting multiple targets, for which to the best of our knowledge no progress has been made so far. We resolve questions (a) and (b) by providing appropriate upper and lower bounds, as well as a new potential
$$\varGamma $$
that guarantees efficient target detection even by querying an approximate median each time. With respect to (c), we initiate a systematic study for detecting two targets in graphs and we identify sufficient conditions on the queries that allow for strong (linear) lower bounds and strong (polylogarithmic) upper bounds for the number of queries. All of our positive results can be derived using our new potential
$$\varGamma $$
that allows querying approximate medians.
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Copiello, Sergio; Bonifaci, Pietro
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This contribution focuses on the scholarly social network ResearchGate (RG). We take the cue from a recent change in the information shown on each researcher’s profile page, which now discloses the number of fulltext reads, in addition to the already provided number of overall reads. Building on the findings of two previous studies (OrdunaMalea et al. in Scientometrics 112(1):443–460, 2017.
https://doi.org/10.1007/s1119201723969
; Copiello and Bonifaci in Scientometrics 114(1):301–306, 2018.
https://doi.org/10.1007/s1119201725829
), we delve into the relationship among fulltext research items uploaded in that platform, fulltext reads of the same items, and the socalled RG Score. The dataset examined here provides conflicting results. Firstly, the number of fulltext publications and reads is significantly different, along with the RG Score, for the analyzed samples. Secondly, the RG Score implicitly rewards the ratio between the fulltexts available to users and total research items. Moreover, the same score seems to be affected to a greater degree by the level of overall reads. However, apart from an indirect relationship, it does not reward how much attention the fulltexts get in comparison to the other research items featured in the scholars’ profile pages.
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Burjons, Elisabet; Komm, Dennis; Schöngens, Marcel
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We study the impact of additional information on the hardness of the kserver problem on different metric spaces. To this end, we consider the wellknown model of computing with advice. In particular, we design an algorithm for the ddimensional Euclidean space, which generalizes a known result for the Euclidean plane. As another relevant setting, we investigate a metric space with positive curvature; in particular, the sphere. Both algorithms have constant strict competitive ratios while reading a constant number of advice bits with every request, independent of the number k of servers, and solely depending on parameters of the underlying metric structure.
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By
Kao, MongJen; Tu, HaiLun; Lee, D. T.
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We consider capacitated vertex cover with hard capacity constraints (VCHC) on hypergraphs. In this problem we are given a hypergraph
$$G=(V,E)$$
with a maximum edge size f. Each edge is associated with a demand and each vertex is associated with a weight (cost), a capacity, and an available multiplicity. The objective is to find a minimumweight vertex multiset such that the demands of the edges can be covered by the capacities of the vertices and the multiplicity of each vertex does not exceed its available multiplicity. In this paper we present an O(f) bicriteria approximation for VCHC that gives a tradeoff on the number of augmented multiplicity and the cost of the resulting cover. In particular, we show that, by augmenting the available multiplicity by a factor of
$$k \ge 2$$
, a cover with a cost ratio of
$$\left( 1+\frac{1}{k1}\right) (f1)$$
to the optimal cover for the original instance can be obtained. This improves over the previously best known guarantee, which has a cost ratio of
$$f^2$$
via augmenting the available multiplicity by a factor of f.
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By
Xu, Haitao; Duan, Feng; Pu, Pan
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As a lowcost environmentallyfriendly travel mode, public bicycles have been widely applied in many large cities and have greatly facilitated people’s daily lives. However, it is hard to find bicycles to rent or places to return at some stations in peak hours due to the unbalanced distribution of public bicycles. And the traditional scheduling methods have hysteresis, in general, the demands might have changed when the dispatch vehicle arrives the station. To better solve such problems, we propose a dynamic scheduling (DBS) model based on shortterm demand prediction. In this paper, we first adopt Kmeans to cluster the stations and adopt random forest (RF) to predict the checkout number of bikes in each clustering. In addition, the multisimilarity inference model is applied to calculate the checkout probability of each station for checkout prediction, and a probabilistic model is proposed for checkin prediction in the cluster. Based on the prediction results, an enhanced genetic algorithm (EGA) is applied to optimize the bicycle scheduling route. Finally, we evaluated the performance of the models through a oneyear dataset from Chicago’s public bikesharing system (BSS) with more than 500 stations and over 3.8 million travel records. Compared with other prediction methods and scheduling approaches, the proposed approach has better performance.
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By
Lovakov, Andrey; Agadullina, Elena
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For several decades the Soviet academic psychology community was isolated from the West, yet after the collapse of the Soviet Union each of the 15 countries went their own way in economic, social, and scientific development. The paper analyses publications from postSoviet countries in psychological journals in 1992–2017, i.e. 26 years after the collapse of the Soviet Union. Over the period in question, 15 postSoviet countries had published 4986 papers in psychology, accounting for less than one percent of the world output in psychological journals. However, the growth of postSoviet countries’ output in psychological journals, especially that of Russia and Estonia, is observed during this period. Over time, postSoviet authors began to write more papers in international teams, constantly increasing the proportion of papers in which they are leaders and main contributors. Their papers are still underrepresented in the best journals as well as among the most cited papers in the field and are also cited lower than the world average. However, the impact of psychological papers from postSoviet countries increases with time. There is a huge diversity between 15 postSoviet countries in terms of contribution, autonomy, and impact. Regarding the number of papers in psychological journals, the leading nations are Russia, Estonia, Lithuania, Ukraine, and Georgia. Estonia is the leader in autonomy in publishing papers in psychological journals among postSoviet countries. Papers from Estonia and Georgia are cited higher than the world average, whereas papers from Russia and Ukraine are cited below the world average. Estonia and Georgia also boast a high number of Highly cited papers.
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By
Sebo, Paul; Fournier, Jean Pascal; Ragot, Claire; Gorioux, PierreHenri; Herrmann, François R.; Maisonneuve, Hubert
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We aimed to assess publication speed of manuscripts submitted to general medical journals and to explore the link with various author, paper and journal characteristics. In this retrospective study of bibliometric data we retrieved 45 randomly selected papers published in 2016 from each of the highest impact factor journals of general internal medicine (n = 9) and primary care (n = 9). Only journals reporting submission and publication dates were included. The following data were extracted: first author (gender, place of affiliation, number of publications), paper (submission and publication dates, online publication, open access, number of authors, number of participants, study design, study results) and journal characteristics (impact factor, number of papers published). We computed for each paper the submissiontoacceptance, acceptancetopublication and submissiontopublication times. We performed linear regression with random effects models to identify the associations with predictors, adjusting for intracluster correlations. A total of 781 papers were included. The overall median submissiontoacceptance time was 123 days (interquartile range 111, min 1, max 922), acceptancetopublication time 68 days (interquartile range 88, min 2, max 802) and submissiontopublication time 224 days (interquartile range 156, min 24, max 1034). In multivariate analysis, online publication was strongly associated with reduced submissiontopublication time (difference: − 93 days, p value < 0.001). This study provides insight into the submissiontoacceptance, acceptancetopublication and submissiontopublication times in general medical journals. Researchers interested in reducing publication delays should focus on journals with online publication.
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LopezMartinez, A.; Cuevas, F. J.
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Circle extraction is usually a previous task used in different applications related to biometrics, robotics, medical image analysis among others. Solutions based on metaheuristic approaches, such as evolutionary and swarmbased algorithms, have been adopted in order to overcome the main deficiencies of Hough Transform methods. In this paper, the task of circle detection is presented as an optimization problem, where each circle represents an optimum within the feasible search space. To this end, a circle detection method is proposed based on the Teaching Learning Based Optimization algorithm, which is a populationbased technique that is inspired by the teaching and learning processes. Additionally, improvements to the evolutionary approach for circle detection are obtained by exploiting gradient information for the construction of the search space and the definition of the objective function. To validate the efficacy of the proposed circle detector, several tests using noisy and complex images as input were carried out, and the results compared with different approaches for circle detection.
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By
Lenc, Karel; Vedaldi, Andrea
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Despite the importance of image representations such as histograms of oriented gradients and deep Convolutional Neural Networks (CNN), our theoretical understanding of them remains limited. Aimed at filling this gap, we investigate two key mathematical properties of representations: equivariance and equivalence. Equivariance studies how transformations of the input image are encoded by the representation, invariance being a special case where a transformation has no effect. Equivalence studies whether two representations, for example two different parameterizations of a CNN, two different layers, or two different CNN architectures, share the same visual information or not. A number of methods to establish these properties empirically are proposed, including introducing transformation and stitching layers in CNNs. These methods are then applied to popular representations to reveal insightful aspects of their structure, including clarifying at which layers in a CNN certain geometric invariances are achieved and how various CNN architectures differ. We identify several predictors of geometric and architectural compatibility, including the spatial resolution of the representation and the complexity and depth of the models. While the focus of the paper is theoretical, direct applications to structuredoutput regression are demonstrated too.
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Prathap, Gangan
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Most performance exercises help identify sizedependent and sizeindependent indicators which separately represent quantity and quality proxies. A good example is the evaluation of a country’s scientific performance relative to its economic standing. Scientific performance is usually evaluated in terms of publications and citations and economic performance is measured in terms of gross domestic product (GDP). In this paper we use exergy, which is a scalar secondorder product of publications and citations as a measure of output, with GDP as input. X and GDP are scaledependent terms. We shall take the ratio X/GDP as the scaledependent measure of performance relative to economic strength. A scatter plot of X/GDP to GDP serves as a quality–quantity map and from this it is possible to identify what are called the skyline and shoreline boundaries showing the scaledependent upper and lower bounds of performance. One lesson that emerges is that quality does not necessarily grow with size; there is a noticeable scaledependent stratification. It is also possible to identify a few economies that stand out noticeably from the rest.
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Fluschnik, Till; Komusiewicz, Christian; Mertzios, George B.; Nichterlein, André; Niedermeier, Rolf; Talmon, Nimrod
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Hyperbolicity is a distancebased measure of how close a given graph is to being a tree. Due to its relevance in modeling realworld networks, hyperbolicity has seen intensive research over the last years. Unfortunately, the best known algorithms used in practice for computing the hyperbolicity number of an nvertex graph have running time
$$O(n^4)$$
. Exploiting the framework of parameterized complexity analysis, we explore possibilities for “lineartime FPT” algorithms to compute hyperbolicity. For example, we show that hyperbolicity can be computed in
$$2^{O(k)} + O(n +m)$$
time (where m and k denote the number of edges and the size of a vertex cover in the input graph, respectively) while at the same time, unless the Strong Exponential Time Hypothesis (SETH) fails, there is no
$$2^{o(k)}\cdot n^{2\varepsilon }$$
time algorithm for every
$$\varepsilon >0$$
.
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Prathap, Gangan
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We explore a dual score system that simultaneously evaluates the relative importance of authors and their papers from a given authorpapercitation heterogeneous network. An observed, or actual citation score for each paper is known at the paper–paper citation matrix level. From an author score obtained from an author–author citation matrix, it is possible to derive separately, an expected score for each paper. The ratio of observed to expected scores is an author based relative paper score for each paper. If the aggregation is journal based, then based on journal scores, one can derive in the same manner, expected, observed and relative paper citation scores. It follows that field based aggregation will lead to a similar family of field based paper scores.
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Adhikari, Agniv; Das, Paramita; Mukherjee, Abhik
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The breadth and velocity of innovation has resulted in explosion of research documents day by day. Academic conferences are being arranged worldwide, most of them in regular intervals, thereby generating a huge volume of research documents. Extracting undiscovered knowledge from the conference papers and thereby finding the interrelationship of conference research topics is a challenging task. This paper attempts towards knowledge discovery for the conference with the help of keywords mentioned in the papers presented therein. The scheme proposed here tries to include the entire set of conference research papers using a small subset of all available keywords. The correctness and complexity of the scheme are analyzed. Proof of concept is established through some flagship conference held annually round the globe. The performance is favourable when compared with available text mining methods, as far as practicable. Results indicate that the scheme could be useful in characterizing topical themes of academic conferences, which may benefit both participants and organizers.
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Zhang, Nan; Ding, Shifei; Liao, Hongmei; Jia, Weikuan
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The Restricted Boltzmann machine (RBM) has been proven to be a powerful tool in many specific applications, such as representational learning, document modeling, and many other learning tasks. However, the extensions of the RBM are rarely used in the field of multiview learning. In this paper, we present a new RBM model based on canonical correlation analysis, named as the correlation RBM, for multiview learning. The correlation RBM computes multiple representations by regularizing the marginal likelihood function with the consistency among representations from different views. In addition, the multimodal deep model can obtain a unified representation that fuses multiple representations together. Therefore, we stack the correlation RBM to create the correlation deep belief network (DBN), and then propose the multimodal correlation DBN for learning multiview data representations. Contrasting with existing multiview classification methods, such as multiview Gaussian process with posterior consistency (MvGP) and consensus and complementarity based maximum entropy discrimination (MED2C), the correlation RBM and the multimodal correlation DBN have achieved satisfactory results on twoclass and multiclass classification datasets. Experimental results show that correlation RBM and the multimodal correlation DBN are effective learning algorithms.
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Jahid, Tarik; Karmouni, Hicham; Sayyouri, Mhamed; Hmimid, Abdeslam; Qjidaa, Hassan
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The rise of the digital imaging is remarkable, and the methods and techniques of image processing and analysis of the digital one must also accompany this technological evolution. In a line of research on the moments theory associated with digital imaging, values are extracted from digital images for the needs of classifications or even of reconstruction, as unique descriptors of an image, our work fits. In this paper, we propose a new method, fast and efficient, for calculating orthogonal moments on the discrete 3D image. We opted for the orthogonal polynomials of Meixner and for a new representation of the 3D image by cuboids having same gray levels called image cuboid representation. Based on this representation, we calculate the moments on each cuboid before summing all cuboids in order to obtain the global moments of a 3D image. Through a set of simulations, we prove that our method allows to reduce the time required for the calculation of moment on a 3D image of any size and any order, but not only, this method makes it possible to improve the quality of 3D image reconstruction from loworder moment.
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Zhang, Jun; Teng, YuFan; Chen, Wei
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The support vector regression (SVR) has been employed to deal with stock price forecasting problems. However, the selection of appropriate kernel parameters is crucial to obtaining satisfactory forecasting performance. This paper proposes a novel approach for forecasting stock prices by combining the SVR with the firefly algorithm (FA). The proposed forecasting model has two stages. In the first stage, to enhance the global convergence speed, a modified version of the FA, which is termed the MFA, is developed in which the dynamic adjustment strategy and the oppositionbased chaotic strategy are introduced. In the second stage, a hybrid SVR model is proposed and combined with the MFA for stock price forecasting, in which the MFA is used to optimize the SVR parameters. Finally, comparative experiments are conducted to show the applicability and superiority of the proposed methods. Experimental results show the following: (1) Compared with other algorithms, the proposed MFA algorithm possesses superior performance, and (2) The proposed MFASVR prediction procedure can be considered as a feasible and effective tool for forecasting stock prices.
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