54,125 research outputs found
Problematising Civil Society- on What Terrain Does Xenophobia Flourish
Is there a need to reconceptualise civil society organisations (CSOs) given the fragmented, uneven, varied and sometimes contradictory responses of CSOs to the May 2008 violence
Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications
We present an overview and evaluation of a new, systematic approach for
generation of highly realistic, annotated synthetic data for training of deep
neural networks in computer vision tasks. The main contribution is a procedural
world modeling approach enabling high variability coupled with physically
accurate image synthesis, and is a departure from the hand-modeled virtual
worlds and approximate image synthesis methods used in real-time applications.
The benefits of our approach include flexible, physically accurate and scalable
image synthesis, implicit wide coverage of classes and features, and complete
data introspection for annotations, which all contribute to quality and cost
efficiency. To evaluate our approach and the efficacy of the resulting data, we
use semantic segmentation for autonomous vehicles and robotic navigation as the
main application, and we train multiple deep learning architectures using
synthetic data with and without fine tuning on organic (i.e. real-world) data.
The evaluation shows that our approach improves the neural network's
performance and that even modest implementation efforts produce
state-of-the-art results.Comment: The project web page at
http://vcl.itn.liu.se/publications/2017/TKWU17/ contains a version of the
paper with high-resolution images as well as additional materia
Using Taint Analysis and Reinforcement Learning (TARL) to Repair Autonomous Robot Software
It is important to be able to establish formal performance bounds for
autonomous systems. However, formal verification techniques require a model of
the environment in which the system operates; a challenge for autonomous
systems, especially those expected to operate over longer timescales. This
paper describes work in progress to automate the monitor and repair of
ROS-based autonomous robot software written for an a-priori partially known and
possibly incorrect environment model. A taint analysis method is used to
automatically extract the data-flow sequence from input topic to publish topic,
and instrument that code. A unique reinforcement learning approximation of MDP
utility is calculated, an empirical and non-invasive characterization of the
inherent objectives of the software designers. By comparing off-line (a-priori)
utility with on-line (deployed system) utility, we show, using a small but real
ROS example, that it's possible to monitor a performance criterion and relate
violations of the criterion to parts of the software. The software is then
patched using automated software repair techniques and evaluated against the
original off-line utility.Comment: IEEE Workshop on Assured IEEE Workshop on Assured Autonomous Systems,
May, 202
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