Direct tensor expression by Eulerian approach for constitutive relations based on strain invariants in transversely isotropic green elasticity - finite extension and torsion

It has been proven by J.C.Criscione that constitutive relations(mixed approach) based on a set of five strain invariants (Beta-1, Beta-2, Beta-3, Beta-4, Beta-5) are useful and stable for experimentally determining response terms for transversely isotropic material. On the other hand, Rivlin’s classical model is an unsuitable choice for determining response terms due to the co-alignment of the five invariants (I1, I2, I3, I4, I5). Despite this, however, a mixed (Lagrangian and Eulerian) approach causes unnecessary computational time and requires intricate calculation in the constitutive relation. Through changing the way to approach the derivation of a constitutive relation, we have verified that using an Eulerian approach causes shorter computational time and simpler calculation than using a mixed approach does. We applied this approach to a boundary value problem under specific deformation, i.e. finite extension and torsion to a fiber reinforced circular cylinder. The results under this deformation show that the computational time by Eulerian is less than half of the time by mixed. The main reason for the difference is that we have to determine two unit vectors on the cross fiber direction from the right Cauchy Green deformation tensor at every radius of the cylinder when we use a mixed approach. On the contrary, we directly use the left Cauchy Green deformation tensor in the constitutive relation by the Eulerian approach without defining the two cross fiber vectors. Moreover, the computational time by the Eulerian approach is not influenced by the degree of deformation even in the case of computational time by the Eulerian approach, possibly becoming the same as the computational time by the mixed approach. This is from the theoretical thought that the mixed approach is almost the same as the Eulerian approach under small deformation. This new constitutive relation by Eulerian approach will have more advantages with regard to saving computational time as the deformation gets more complicated. Therefore, since the Eulerain approach effectively shortens computational time, this may enhance the computational tools required to approach the problems with greater degrees of anisotropy and viscoelasticity

Andro-Simnet: Android Malware Family Classification Using Social Network Analysis

While the rapid adaptation of mobile devices changes our daily life more conveniently, the threat derived from malware is also increased. There are lots of research to detect malware to protect mobile devices, but most of them adopt only signature-based malware detection method that can be easily bypassed by polymorphic and metamorphic malware. To detect malware and its variants, it is essential to adopt behavior-based detection for efficient malware classification. This paper presents a system that classifies malware by using common behavioral characteristics along with malware families. We measure the similarity between malware families with carefully chosen features commonly appeared in the same family. With the proposed similarity measure, we can classify malware by malware's attack behavior pattern and tactical characteristics. Also, we apply a community detection algorithm to increase the modularity within each malware family network aggregation. To maintain high classification accuracy, we propose a process to derive the optimal weights of the selected features in the proposed similarity measure. During this process, we find out which features are significant for representing the similarity between malware samples. Finally, we provide an intuitive graph visualization of malware samples which is helpful to understand the distribution and likeness of the malware networks. In the experiment, the proposed system achieved 97% accuracy for malware classification and 95% accuracy for prediction by K-fold cross-validation using the real malware dataset.Comment: 13 pages, 11 figures, dataset link: http://ocslab.hksecurity.net/Datasets/andro-simnet , demo video: https://youtu.be/JmfS-ZtCbg4 , In Proceedings of the 16th Annual Conference on Privacy, Security and Trust (PST), 201

Self-Supervised Motion Retargeting with Safety Guarantee

In this paper, we present self-supervised shared latent embedding (S3LE), a data-driven motion retargeting method that enables the generation of natural motions in humanoid robots from motion capture data or RGB videos. While it requires paired data consisting of human poses and their corresponding robot configurations, it significantly alleviates the necessity of time-consuming data-collection via novel paired data generating processes. Our self-supervised learning procedure consists of two steps: automatically generating paired data to bootstrap the motion retargeting, and learning a projection-invariant mapping to handle the different expressivity of humans and humanoid robots. Furthermore, our method guarantees that the generated robot pose is collision-free and satisfies position limits by utilizing nonparametric regression in the shared latent space. We demonstrate that our method can generate expressive robotic motions from both the CMU motion capture database and YouTube videos