5,758 research outputs found
Reasons for female neonaticide in India
Invited commentary on ‘Neonaticide in India and the stigma of female gender: report of two cases’, Mishra et al
Online Reputation Systems in Web 2.0 Era
Web 2.0 has transformed how reputation systems are designed and used by the Web. Based on a thorough review of the existing online reputation systems and their challenges in use, this paper studied a case of Amazon’s reputation system for the impacts of Web 2.0. Through our case study, several distinguished features of new generation reputation systems are noted including multimedia feedbacks, reviewer centered, folksonomy (use of tag), community contribution, comprehensive reputation, dynamic and interactive system etc.. These new developments promise a path that move towards a trustworthy and reliable online reputation system in the Web 2.0 era
Ensuring Trust in One Time Exchanges: Solving the QoS Problem
We describe a pricing structure for the provision of IT services that ensures
trust without requiring repeated interactions between service providers and
users. It does so by offering a pricing structure that elicits truthful
reporting of quality of service (QoS) by providers while making them
profitable. This mechanism also induces truth-telling on the part of users
reserving the service
Path integrals and wavepacket evolution for damped mechanical systems
Damped mechanical systems with various forms of damping are quantized using
the path integral formalism. In particular, we obtain the path integral kernel
for the linearly damped harmonic oscillator and a particle in a uniform
gravitational field with linearly or quadratically damped motion. In each case,
we study the evolution of Gaussian wavepackets and discuss the characteristic
features that help us distinguish between different types of damping. For
quadratic damping, we show that the action and equation of motion of such a
system has a connection with the zero dimensional version of a currently
popular scalar field theory. Furthermore we demonstrate that the equation of
motion (for quadratic damping) can be identified as a geodesic equation in a
fictitious two-dimensional space.Comment: 15 pages, 6 figure
Rational Trust Modeling
Trust models are widely used in various computer science disciplines. The
main purpose of a trust model is to continuously measure trustworthiness of a
set of entities based on their behaviors. In this article, the novel notion of
"rational trust modeling" is introduced by bridging trust management and game
theory. Note that trust models/reputation systems have been used in game theory
(e.g., repeated games) for a long time, however, game theory has not been
utilized in the process of trust model construction; this is where the novelty
of our approach comes from. In our proposed setting, the designer of a trust
model assumes that the players who intend to utilize the model are
rational/selfish, i.e., they decide to become trustworthy or untrustworthy
based on the utility that they can gain. In other words, the players are
incentivized (or penalized) by the model itself to act properly. The problem of
trust management can be then approached by game theoretical analyses and
solution concepts such as Nash equilibrium. Although rationality might be
built-in in some existing trust models, we intend to formalize the notion of
rational trust modeling from the designer's perspective. This approach will
result in two fascinating outcomes. First of all, the designer of a trust model
can incentivise trustworthiness in the first place by incorporating proper
parameters into the trust function, which can be later utilized among selfish
players in strategic trust-based interactions (e.g., e-commerce scenarios).
Furthermore, using a rational trust model, we can prevent many well-known
attacks on trust models. These two prominent properties also help us to predict
behavior of the players in subsequent steps by game theoretical analyses
Bayesian Networks for Max-linear Models
We study Bayesian networks based on max-linear structural equations as
introduced in Gissibl and Kl\"uppelberg [16] and provide a summary of their
independence properties. In particular we emphasize that distributions for such
networks are generally not faithful to the independence model determined by
their associated directed acyclic graph. In addition, we consider some of the
basic issues of estimation and discuss generalized maximum likelihood
estimation of the coefficients, using the concept of a generalized likelihood
ratio for non-dominated families as introduced by Kiefer and Wolfowitz [21].
Finally we argue that the structure of a minimal network asymptotically can be
identified completely from observational data.Comment: 18 page
First exit times of solutions of stochastic differential equations driven by multiplicative Levy noise with heavy tails
In this paper we study first exit times from a bounded domain of a gradient
dynamical system perturbed by a small multiplicative
L\'evy noise with heavy tails. A special attention is paid to the way the
multiplicative noise is introduced. In particular we determine the asymptotics
of the first exit time of solutions of It\^o, Stratonovich and Marcus canonical
SDEs.Comment: 19 pages, 2 figure
Detecting a conditional extrme value model
In classical extreme value theory probabilities of extreme events are
estimated assuming all the components of a random vector to be in a domain of
attraction of an extreme value distribution. In contrast, the conditional
extreme value model assumes a domain of attraction condition on a
sub-collection of the components of a multivariate random vector. This model
has been studied in
\cite{heffernan:tawn:2004,heffernan:resnick:2007,das:resnick:2008a}.
In this paper we propose three statistics which act as tools to detect this
model in a bivariate set-up. In addition, the proposed statistics also help to
distinguish between two forms of the limit measure that is obtained in the
model.Comment: 21 pages, 4 figure
Heat Conduction Process on Community Networks as a Recommendation Model
Using heat conduction mechanism on a social network we develop a systematic
method to predict missing values as recommendations. This method can treat very
large matrices that are typical of internet communities. In particular, with an
innovative, exact formulation that accommodates arbitrary boundary condition,
our method is easy to use in real applications. The performance is assessed by
comparing with traditional recommendation methods using real data.Comment: 4 pages, 2 figure
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