24 research outputs found
A Latent Encoder Coupled Generative Adversarial Network (LE-GAN) for Efficient Hyperspectral Image Super-resolution
Realistic hyperspectral image (HSI) super-resolution (SR) techniques aim to
generate a high-resolution (HR) HSI with higher spectral and spatial fidelity
from its low-resolution (LR) counterpart. The generative adversarial network
(GAN) has proven to be an effective deep learning framework for image
super-resolution. However, the optimisation process of existing GAN-based
models frequently suffers from the problem of mode collapse, leading to the
limited capacity of spectral-spatial invariant reconstruction. This may cause
the spectral-spatial distortion on the generated HSI, especially with a large
upscaling factor. To alleviate the problem of mode collapse, this work has
proposed a novel GAN model coupled with a latent encoder (LE-GAN), which can
map the generated spectral-spatial features from the image space to the latent
space and produce a coupling component to regularise the generated samples.
Essentially, we treat an HSI as a high-dimensional manifold embedded in a
latent space. Thus, the optimisation of GAN models is converted to the problem
of learning the distributions of high-resolution HSI samples in the latent
space, making the distributions of the generated super-resolution HSIs closer
to those of their original high-resolution counterparts. We have conducted
experimental evaluations on the model performance of super-resolution and its
capability in alleviating mode collapse. The proposed approach has been tested
and validated based on two real HSI datasets with different sensors (i.e.
AVIRIS and UHD-185) for various upscaling factors and added noise levels, and
compared with the state-of-the-art super-resolution models (i.e. HyCoNet, LTTR,
BAGAN, SR- GAN, WGAN).Comment: 18 pages, 10 figure
Logistic model tree extraction from artificial neural networks
Artificial neural networks (ANNs) are a powerful and
widely used pattern recognition technique. However, they remain
âblack boxesâ giving no explanation for the decisions they make.
This paper presents a new algorithm for extracting a logistic model
tree (LMT) from a neural network, which gives a symbolic representation
of the knowledge hidden within the ANN. Landwehrâs
LMTs are based on standard decision trees, but the terminal nodes
are replaced with logistic regression functions. This paper reports
the results of an empirical evaluation that compares the new decision
tree extraction algorithm with Quinlanâs C4.5 and ExTree.
The evaluation used 12 standard benchmark datasets from the
University of California, Irvine machine-learning repository. The
results of this evaluation demonstrate that the new algorithm
produces decision trees that have higher accuracy and higher
fidelity than decision trees created by both C4.5 and ExTree
Decision tree extraction from trained neural networks
Artificial Neural Networks (ANNs) have proved both a popular
and powerful technique for pattern recognition tasks in
a number of problem domains. However, the adoption of
ANNs in many areas has been impeded, due to their inability
to explain how they came to their conclusion, or show in
a readily comprehendible form the knowledge they have obtained.
This paper presents an algorithm that addresses these problems.
The algorithm achieves this by extracting a Decision
Tree, a graphical and easily understood symbolic representation
of a decision process, from a trained ANN. The algorithm
does not make assumptions about the ANNâs architecture or
training algorithm; therefore, it can be applied to any type of
ANN. The algorithm is empirically compared with Quinlanâs
C4.5 (a common Decision Tree induction algorithm) using
standard benchmark datasets. For most of the datasets used
in the evaluation, the new algorithm is shown to extract Decision
Trees that have a higher predictive accuracy than those
induced using C4.5 directly
Layer-Wise Partitioning and Merging for Efficient and Scalable Deep Learning
Deep Neural Network (DNN) models are usually trained sequentially from one
layer to another, which causes forward, backward and update locking's problems,
leading to poor performance in terms of training time. The existing parallel
strategies to mitigate these problems provide suboptimal runtime performance.
In this work, we have proposed a novel layer-wise partitioning and merging,
forward and backward pass parallel framework to provide better training
performance. The novelty of the proposed work consists of 1) a layer-wise
partition and merging model which can minimise communication overhead between
devices without the memory cost of existing strategies during the training
process; 2) a forward pass and backward pass parallelisation and optimisation
to address the update locking problem and minimise the total training cost. The
experimental evaluation on real use cases shows that the proposed method
outperforms the state-of-the-art approaches in terms of training speed; and
achieves almost linear speedup without compromising the accuracy performance of
the non-parallel approach
A Biologically Interpretable Two-Stage Deep Neural Network (BIT-DNN) for Vegetation Recognition From Hyperspectral Imagery
Spectral-spatial-based deep learning models have recently proven to be effective in hyper-spectral image (HSI) classification for various earth monitoring applications such as land cover classification and agricultural monitoring. However, due to the nature of ``black-box'' model representation, how to explain and interpret the learning process and the model decision, especially for vegetation classification, remains an open challenge. This study proposes a novel interpretable deep learning model--a biologically interpretable two-stage deep neural network (BIT-DNN), by incorporating the prior-knowledge (i.e., biophysical and biochemical attributes and their hierarchical structures of target entities)-based spectral-spatial feature transformation into the proposed framework, capable of achieving both high accuracy and interpretability on HSI-based classification tasks. The proposed model introduces a two-stage feature learning process: in the first stage, an enhanced interpretable feature block extracts the low-level spectral features associated with the biophysical and biochemical attributes of target entities; and in the second stage, an interpretable capsule block extracts and encapsulates the high-level joint spectral-spatial features representing the hierarchical structure of biophysical and biochemical attributes of these target entities, which provides the model an improved performance on classification and intrinsic interpretability with reduced computational complexity. We have tested and evaluated the model using four real HSI data sets for four separate tasks (i.e., plant species classification, land cover classification, urban scene recognition, and crop disease recognition tasks). The proposed model has been compared with five state-of-the-art deep learning models. The results demonstrate that the proposed model has competitive advantages in terms of both classification accuracy and model interpretability, especially for vegetation classification
SCNN-Attack: A Side-Channel Attack to Identify YouTube Videos in a VPN and Non-VPN Network Traffic
Encryption Protocols e.g., HTTPS is utilized to secure the traffic between servers and clients for YouTube and other video streaming services, and to further secure the communication, VPNs are used. However, these protocols are not sufficient to hide the identity of the videos from someone who can sniff the network traffic. The present work explores the methodologies and features to identify the videos in a VPN and non-VPN network traffic. To identify such videos, a side-channel attack using a Sequential Convolution Neural Network is proposed. The results demonstrate that a sequence of bytes per second from even one-minute sniffing of network traffic is sufficient to predict the video with high accuracy. The accuracy is increased to 90% accuracy in the non-VPN, 66% accuracy in the VPN, and 77% in the mixed VPN and non-VPN traffic, for models with two-minute sniffing
CPU and RAM Energy-based SLA-aware Workload Consolidation Techniques for Clouds
Cloud computing offers hardware and software resources delivered as services. It provides solutions for dynamic as well as ââpay as you goââ provision of resources. Energy consumption of these resources is high which leads to higher operational costs and carbon emissions in data centers. A number of research studies have been conducted on energy efficiency of data centers, but most of them concentrate on single factor energy consumption, i.e., energy consumed by CPU only, and energy consumption by Random Access Memory (RAM) is neglected. However, recently the focus has been turned towards impact of energy consumption by RAM on data centers. Studies have shown that RAM consumes about 25% of joint energy consumed by a serverâs CPU and RAM. In this paper, two energy-aware virtual machine (VM) consolidation schemes are proposed that take into account a serverâs capacity in terms of CPU and RAM to reduce the overall energy consumption. The proposed schemes are compared with existing schemes using CloudSim simulator. The results show that the proposed schemes reduce the energy cost with improved Service Level Agreement (SLA)
A Modified Bayesian Framework for Multi-Sensor Target Tracking with Out-of-Sequence-Measurements
Target detection and tracking is important in military as well as in civilian applications. In order to detect and track high-speed incoming threats, modern surveillance systems are equipped with multiple sensors to overcome the limitations of single-sensor based tracking systems. This research proposes the use of information from RADAR and Infrared sensors (IR) for tracking and estimating target state dynamics. A new technique is developed for information fusion of the two sensors in a way that enhances performance of the data association algorithm. The measurement acquisition and processing time of these sensors is not the same; consequently the fusion center measurements arrive out of sequence. To ensure the practicality of system, proposed algorithm compensates the Out of Sequence Measurements (OOSMs) in cluttered environment. This is achieved by a novel algorithm which incorporates a retrodiction based approach to compensate the effects of OOSMs in a modified Bayesian technique. The proposed modification includes a new gating strategy to fuse and select measurements from two sensors which originate from the same target. The state estimation performance is evaluated in terms of Root Mean Squared Error (RMSE) for both position and velocity, whereas, track retention statistics are evaluated to gauge the performance of the proposed tracking algorithm. The results clearly show that the proposed technique improves track retention and and false track discrimination (FTD)
Investigation of 9000 hours multi-stress aging effects on High-Temperature Vulcanized Silicone Rubber with silica (nano/micro) filler hybrid composite insulator
Degradation in the polymeric insulators is caused due to the environmental stresses. The main aim of this paper is to explore the improved aging characteristics of hybrid samples by adding nano/micro silica in High Temperature Vulcanized Silicone Rubber (HTV-SiR) under long term accelerated aging conditions for 9000 hours. As HTV-SiR is unable to sustain environmental stresses for a long time, thus a long term accelerated aging behavior is an important phenomenon to be considered for field application. The aging characteristics of nano/micro filled HTV-SiR are analyzed by using techniques such as Scanning Electron Microscopy (SEM), Leakage Current (LC), Fourier Transform Infrared Microscopy (FTIR), Hydrophobicity Classification (HC), and breakdown strength for the aging time of 9000 hours. FTIR and leakage currents are measured after every cycle. All the co-filled samples revealed escalated aging characteristics as compared to the neat sample except the SN8 sample (8% nano-silica+20% micro-silica) after 9000 hours of aging. The highest loading of 6% and 8% nano-silica with 20% micro-silica do not contribute to the improved performance when compared with the neat and hybrid samples. However, from the critical experimental analysis, it is deduced that SN2 sample (2% nano-silica+20% micro-silica) is highly resistant to the long term accelerated aging conditions. SN2 has no cracks, lower loss percentages in the important FTIR absorption peaks, higher breakdown strength and superior HC after aging as compared to the unfilled and hybrid samples