64,269 research outputs found
Game Theory Meets Network Security: A Tutorial at ACM CCS
The increasingly pervasive connectivity of today's information systems brings
up new challenges to security. Traditional security has accomplished a long way
toward protecting well-defined goals such as confidentiality, integrity,
availability, and authenticity. However, with the growing sophistication of the
attacks and the complexity of the system, the protection using traditional
methods could be cost-prohibitive. A new perspective and a new theoretical
foundation are needed to understand security from a strategic and
decision-making perspective. Game theory provides a natural framework to
capture the adversarial and defensive interactions between an attacker and a
defender. It provides a quantitative assessment of security, prediction of
security outcomes, and a mechanism design tool that can enable
security-by-design and reverse the attacker's advantage. This tutorial provides
an overview of diverse methodologies from game theory that includes games of
incomplete information, dynamic games, mechanism design theory to offer a
modern theoretic underpinning of a science of cybersecurity. The tutorial will
also discuss open problems and research challenges that the CCS community can
address and contribute with an objective to build a multidisciplinary bridge
between cybersecurity, economics, game and decision theory
Generated Data with Sparse Regularized Multi-Pseudo Label for Person Re-Identification
© 1994-2012 IEEE. Recently, Generative Adversarial Network (GAN) has been adopted to improve person re-identification (person re-ID) performance through data augmentation. However, directly leveraging generated data to train a re-ID model may easily lead to over-fitting issue on these extra data and decrease the generalisability of model to learn true ID-related features from real data. Inspired by the previous approach which assigns multi-pseudo labels on the generated data to reduce the risk of over-fitting, we propose to take sparse regularization into consideration. We attempt to further improve the performance of current re-ID models by using the unlabeled generated data. The proposed Sparse Regularized Multi-Pseudo Label (SRMpL) can effectively prevent the over-fitting issue when some larger weights are assigned to the generated data. Our experiments are carried out on two publicly available person re-ID datasets (e.g., Market-1501 and DukeMTMC-reID). Compared with existing unlabeled generated data re-ID solutions, our approach achieves competitive performance. Two classical re-ID models are used to verify our sparse regularization label on generated data, i.e., an ID-embedding network and a two-stream network
Multipole polarizability of a graded spherical particle
We have studied the multipole polarizability of a graded spherical particle
in a nonuniform electric field, in which the conductivity can vary radially
inside the particle. The main objective of this work is to access the effects
of multipole interactions at small interparticle separations, which can be
important in non-dilute suspensions of functionally graded materials. The
nonuniform electric field arises either from that applied on the particle or
from the local field of all other particles. We developed a differential
effective multipole moment approximation (DEMMA) to compute the multipole
moment of a graded spherical particle in a nonuniform external field. Moreover,
we compare the DEMMA results with the exact results of the power-law graded
profile and the agreement is excellent. The extension to anisotropic DEMMA will
be studied in an Appendix.Comment: LaTeX format, 2 eps figures, submitted for publication
- …