15,373 research outputs found
Security, Privacy and Safety Risk Assessment for Virtual Reality Learning Environment Applications
Social Virtual Reality based Learning Environments (VRLEs) such as vSocial
render instructional content in a three-dimensional immersive computer
experience for training youth with learning impediments. There are limited
prior works that explored attack vulnerability in VR technology, and hence
there is a need for systematic frameworks to quantify risks corresponding to
security, privacy, and safety (SPS) threats. The SPS threats can adversely
impact the educational user experience and hinder delivery of VRLE content. In
this paper, we propose a novel risk assessment framework that utilizes attack
trees to calculate a risk score for varied VRLE threats with rate and duration
of threats as inputs. We compare the impact of a well-constructed attack tree
with an adhoc attack tree to study the trade-offs between overheads in managing
attack trees, and the cost of risk mitigation when vulnerabilities are
identified. We use a vSocial VRLE testbed in a case study to showcase the
effectiveness of our framework and demonstrate how a suitable attack tree
formalism can result in a more safer, privacy-preserving and secure VRLE
system.Comment: Tp appear in the CCNC 2019 Conferenc
A Massive Data Parallel Computational Framework for Petascale/Exascale Hybrid Computer Systems
Heterogeneous systems are becoming more common on High Performance Computing
(HPC) systems. Even using tools like CUDA and OpenCL it is a non-trivial task
to obtain optimal performance on the GPU. Approaches to simplifying this task
include Merge (a library based framework for heterogeneous multi-core systems),
Zippy (a framework for parallel execution of codes on multiple GPUs), BSGP (a
new programming language for general purpose computation on the GPU) and
CUDA-lite (an enhancement to CUDA that transforms code based on annotations).
In addition, efforts are underway to improve compiler tools for automatic
parallelization and optimization of affine loop nests for GPUs and for
automatic translation of OpenMP parallelized codes to CUDA.
In this paper we present an alternative approach: a new computational
framework for the development of massively data parallel scientific codes
applications suitable for use on such petascale/exascale hybrid systems built
upon the highly scalable Cactus framework. As the first non-trivial
demonstration of its usefulness, we successfully developed a new 3D CFD code
that achieves improved performance.Comment: Parallel Computing 2011 (ParCo2011), 30 August -- 2 September 2011,
Ghent, Belgiu
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