811 research outputs found
Multiple Spectral-Spatial Classification Approach for Hyperspectral Data
A .new multiple classifier approach for spectral-spatial classification of hyperspectral images is proposed. Several classifiers are used independently to classify an image. For every pixel, if all the classifiers have assigned this pixel to the same class, the pixel is kept as a marker, i.e., a seed of the spatial region, with the corresponding class label. We propose to use spectral-spatial classifiers at the preliminary step of the marker selection procedure, each of them combining the results of a pixel-wise classification and a segmentation map. Different segmentation methods based on dissimilar principles lead to different classification results. Furthermore, a minimum spanning forest is built, where each tree is rooted on a classification -driven marker and forms a region in the spectral -spatial classification: map. Experimental results are presented for two hyperspectral airborne images. The proposed method significantly improves classification accuracies, when compared to previously proposed classification techniques
Parallel Platform For New Secure Stream Ciphers Based On Np-hard Problems
Tujuan kajian ini adalah untuk mengenal pasti unsur-unsur utama reka bentuk sifer aliran yang selamat dan pantas.
The purpose of this study was to identify the key elements for secure and fast stream cipher’s design. In cryptography
Practically Solving LPN in High Noise Regimes Faster Using Neural Networks
We conduct a systematic study of solving the learning parity with noise
problem (LPN) using neural networks. Our main contribution is designing
families of two-layer neural networks that practically outperform classical
algorithms in high-noise, low-dimension regimes. We consider three settings
where the numbers of LPN samples are abundant, very limited, and in between. In
each setting we provide neural network models that solve LPN as fast as
possible. For some settings we are also able to provide theories that explain
the rationale of the design of our models. Comparing with the previous
experiments of Esser, Kubler, and May (CRYPTO 2017), for dimension ,
noise rate , the ''Guess-then-Gaussian-elimination'' algorithm
takes 3.12 days on 64 CPU cores, whereas our neural network algorithm takes 66
minutes on 8 GPUs. Our algorithm can also be plugged into the hybrid algorithms
for solving middle or large dimension LPN instances.Comment: 37 page
Google Research Football: A Novel Reinforcement Learning Environment
Recent progress in the field of reinforcement learning has been accelerated
by virtual learning environments such as video games, where novel algorithms
and ideas can be quickly tested in a safe and reproducible manner. We introduce
the Google Research Football Environment, a new reinforcement learning
environment where agents are trained to play football in an advanced,
physics-based 3D simulator. The resulting environment is challenging, easy to
use and customize, and it is available under a permissive open-source license.
In addition, it provides support for multiplayer and multi-agent experiments.
We propose three full-game scenarios of varying difficulty with the Football
Benchmarks and report baseline results for three commonly used reinforcement
algorithms (IMPALA, PPO, and Ape-X DQN). We also provide a diverse set of
simpler scenarios with the Football Academy and showcase several promising
research directions
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