pwc-archive/papers-with-abstracts huggingface dataset:
Foothill: A Quasiconvex Regularization for Edge Computing of Deep Neural Networks
Authors: Mouloud Belbahri, Eyyüb Sari, Sajad Darabi, Vahid Partovi Nia
Date: 2019-01-18 00:00:00
Tasks: Edge-computing, General Classification, image-classification, Image Classification, object-detection, Object Detection, Quantization

Deep neural networks (DNNs) have demonstrated success for many supervised learning tasks, ranging from voice recognition, object detection, to image classification. However, their increasing complexity might yield poor generalization error that make them hard to be deployed on edge devices. Quantization is an effective approach to compress DNNs in order to meet these constraints. Using a quasiconvex base function in order to construct a binary quantizer helps training binary neural networks (BNNs) and adding noise to the input data or using a concrete regularization function helps to improve generalization error. Here we introduce foothill function, an infinitely differentiable quasiconvex function. This regularizer is flexible enough to deform towards $L_1$ and $L_2$ penalties. Foothill can be used as a binary quantizer, as a regularizer, or as a loss. In particular, we show this regularizer reduces the accuracy gap between BNNs and their full-precision counterpart for image classification on ImageNet.
Differentially Private High Dimensional Sparse Covariance Matrix Estimation
Authors: Di Wang, Jinhui Xu
Date: 2019-01-18 00:00:00
Tasks: Vocal Bursts Intensity Prediction

In this paper, we study the problem of estimating the covariance matrix under
differential privacy, where the underlying covariance matrix is assumed to be
sparse and of high dimensions. We propose a new method, called DP-Thresholding,
to achieve a non-trivial $\ell_2$-norm based error bound, which is
significantly better than the existing ones from adding noise directly to the
empirical covariance matrix. We also extend the $\ell_2$-norm based error bound
to a general $\ell_w$-norm based one for any $1\leq w\leq \infty$, and show
that they share the same upper bound asymptotically. Our approach can be easily
extended to local differential privacy. Experiments on the synthetic datasets
show consistent results with our theoretical claims.
Learning a Deep Convolution Network with Turing Test Adversaries for Microscopy Image Super Resolution
Authors: Francis Tom, Himanshu Sharma, Dheeraj Mundhra, Tathagato Rai Dastidar, Debdoot Sheet
Date: 2019-01-18 00:00:00
Tasks: Image Super-Resolution, SSIM, Super-Resolution

Adversarially trained deep neural networks have significantly improved
performance of single image super resolution, by hallucinating photorealistic
local textures, thereby greatly reducing the perception difference between a
real high resolution image and its super resolved (SR) counterpart. However,
application to medical imaging requires preservation of diagnostically relevant
features while refraining from introducing any diagnostically confusing
artifacts. We propose using a deep convolutional super resolution network
(SRNet) trained for (i) minimising reconstruction loss between the real and SR
images, and (ii) maximally confusing learned relativistic visual Turing test
(rVTT) networks to discriminate between (a) pair of real and SR images (T1) and
(b) pair of patches in real and SR selected from region of interest (T2). The
adversarial loss of T1 and T2 while backpropagated through SRNet helps it learn
to reconstruct pathorealism in the regions of interest such as white blood
cells (WBC) in peripheral blood smears or epithelial cells in histopathology of
cancerous biopsy tissues, which are experimentally demonstrated here.
Experiments performed for measuring signal distortion loss using peak signal to
noise ratio (pSNR) and structural similarity (SSIM) with variation of SR scale
factors, impact of rVTT adversarial losses, and impact on reporting using SR on
a commercially available artificial intelligence (AI) digital pathology system
substantiate our claims.
Multisource Region Attention Network for Fine-Grained Object Recognition in Remote Sensing Imagery
Authors: Gencer Sumbul, Ramazan Gokberk Cinbis, Selim Aksoy
Date: 2019-01-18 00:00:00
Tasks: General Classification, Object Recognition

Fine-grained object recognition concerns the identification of the type of an
object among a large number of closely related sub-categories. Multisource data
analysis, that aims to leverage the complementary spectral, spatial, and
structural information embedded in different sources, is a promising direction
towards solving the fine-grained recognition problem that involves low
between-class variance, small training set sizes for rare classes, and class
imbalance. However, the common assumption of co-registered sources may not hold
at the pixel level for small objects of interest. We present a novel
methodology that aims to simultaneously learn the alignment of multisource data
and the classification model in a unified framework. The proposed method
involves a multisource region attention network that computes per-source
feature representations, assigns attention scores to candidate regions sampled
around the expected object locations by using these representations, and
classifies the objects by using an attention-driven multisource representation
that combines the feature representations and the attention scores from all
sources. All components of the model are realized using deep neural networks
and are learned in an end-to-end fashion. Experiments using RGB, multispectral,
and LiDAR elevation data for classification of street trees showed that our
approach achieved 64.2% and 47.3% accuracies for the 18-class and 40-class
settings, respectively, which correspond to 13% and 14.3% improvement relative
to the commonly used feature concatenation approach from multiple sources.
Slim LSTM networks: LSTM_6 and LSTM_C6
Authors: Atra Akandeh, Fathi M. Salem
Date: 2019-01-18 00:00:00

We have shown previously that our parameter-reduced variants of Long
Short-Term Memory (LSTM) Recurrent Neural Networks (RNN) are comparable in
performance to the standard LSTM RNN on the MNIST dataset. In this study, we
show that this is also the case for two diverse benchmark datasets, namely, the
review sentiment IMDB and the 20 Newsgroup datasets. Specifically, we focus on
two of the simplest variants, namely LSTM_6 (i.e., standard LSTM with three
constant fixed gates) and LSTM_C6 (i.e., LSTM_6 with further reduced cell body
input block). We demonstrate that these two aggressively reduced-parameter
variants are competitive with the standard LSTM when hyper-parameters, e.g.,
learning parameter, number of hidden units and gate constants are set properly.
These architectures enable speeding up training computations and hence, these
networks would be more suitable for online training and inference onto portable
devices with relatively limited computational resources.
Deep learning for seismic phase detection and picking in the aftershock zone of 2008 Mw7.9 Wenchuan earthquake
Authors: Lijun Zhu, Zhigang Peng, James McClellan, Chenyu Li, Dongdong Yao, Zefeng Li, Lihua Fang
Date: 2019-01-18 00:00:00

The increasing volume of seismic data from long-term continuous monitoring
motivates the development of algorithms based on convolutional neural network
(CNN) for faster and more reliable phase detection and picking. However, many
less studied regions lack a significant amount of labeled events needed for
traditional CNN approaches. In this paper, we present a CNN-based Phase-
Identification Classifier (CPIC) designed for phase detection and picking on
small to medium sized training datasets. When trained on 30,146 labeled phases
and applied to one-month of continuous recordings during the aftershock
sequences of the 2008 MW 7.9 Wenchuan Earthquake in Sichuan, China, CPIC
detects 97.5% of the manually picked phases in the standard catalog and
predicts their arrival times with a five-times improvement over the ObsPy AR
picker. In addition, unlike other CNN-based approaches that require millions of
training samples, when the off-line training set size of CPIC is reduced to
only a few thousand training samples the accuracy stays above 95%. The online
implementation of CPIC takes less than 12 hours to pick arrivals in 31-day
recordings on 14 stations. In addition to the catalog phases manually picked by
analysts, CPIC finds more phases for existing events and new events missed in
the catalog. Among those additional detections, some are confirmed by a matched
filter method while others require further investigation. Finally, when tested
on a small dataset from a different region (Oklahoma, US), CPIC achieves 97%
accuracy after fine tuning only the fully connected layer of the model. This
result suggests that the CPIC developed in this study can be used to identify
and pick P/S arrivals in other regions with no or minimum labeled phases.
A Recent Survey on the Applications of Genetic Programming in Image Processing
Authors: Asifullah Khan, Aqsa Saeed Qureshi, Noorul Wahab, Mutawara Hussain, Muhammad Yousaf Hamza
Date: 2019-01-18 00:00:00
Tasks: feature selection, Image Compression

Genetic Programming (GP) has been primarily used to tackle optimization, classification, and feature selection related tasks. The widespread use of GP is due to its flexible and comprehensible tree-type structure. Similarly, research is also gaining momentum in the field of Image Processing, because of its promising results over vast areas of applications ranging from medical Image Processing to multispectral imaging. Image Processing is mainly involved in applications such as computer vision, pattern recognition, image compression, storage, and medical diagnostics. This universal nature of images and their associated algorithm, i.e., complexities, gave an impetus to the exploration of GP. GP has thus been used in different ways for Image Processing since its inception. Many interesting GP techniques have been developed and employed in the field of Image Processing, and consequently, we aim to provide the research community an extensive view of these techniques. This survey thus presents the diverse applications of GP in Image Processing and provides useful resources for further research. Also, the comparison of different parameters used in different applications of Image Processing is summarized in tabular form. Moreover, analysis of the different parameters used in Image Processing related tasks is carried-out to save the time needed in the future for evaluating the parameters of GP. As more advancement is made in GP methodologies, its success in solving complex tasks, not only in Image Processing but also in other fields, may increase. Additionally, guidelines are provided for applying GP in Image Processing related tasks, the pros and cons of GP techniques are discussed, and some future directions are also set.
SAML-QC: a Stochastic Assessment and Machine Learning based QC technique for Industrial Printing
Authors: Azhar Hussain
Date: 2019-01-18 00:00:00
Tasks: BIG-bench Machine Learning, Optical Character Recognition, Optical Character Recognition (OCR), Text Detection

Recently, the advancement in industrial automation and high-speed printing
has raised numerous challenges related to the printing quality inspection of
final products. This paper proposes a machine vision based technique to assess
the printing quality of text on industrial objects. The assessment is based on
three quality defects such as text misalignment, varying printing shades, and
misprinted text. The proposed scheme performs the quality inspection through
stochastic assessment technique based on the second-order statistics of
printing. First: the text-containing area on printed product is identified
through image processing techniques. Second: the alignment testing of the
identified text-containing area is performed. Third: optical character
recognition is performed to divide the text into different small boxes and only
the intensity value of each text-containing box is taken as a random variable
and second-order statistics are estimated to determine the varying printing
defects in the text under one, two and three sigma thresholds. Fourth: the
K-Nearest Neighbors based supervised machine learning is performed to provide
the stochastic process for misprinted text detection. Finally, the technique is
deployed on an industrial image for the printing quality assessment with
varying values of n and m. The results have shown that the proposed SAML-QC
technique can perform real-time automated inspection for industrial printing.
Multi-agent Reinforcement Learning Embedded Game for the Optimization of Building Energy Control and Power System Planning
Authors: Jun Hao
Date: 2019-01-17 00:00:00
Tasks: Management, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

Most of the current game-theoretic demand-side management methods focus
primarily on the scheduling of home appliances, and the related numerical
experiments are analyzed under various scenarios to achieve the corresponding
Nash-equilibrium (NE) and optimal results. However, not much work is conducted
for academic or commercial buildings. The methods for optimizing
academic-buildings are distinct from the optimal methods for home appliances.
In my study, we address a novel methodology to control the operation of
heating, ventilation, and air conditioning system (HVAC).
  With the development of Artificial Intelligence and computer technologies,
reinforcement learning (RL) can be implemented in multiple realistic scenarios
and help people to solve thousands of real-world problems. Reinforcement
Learning, which is considered as the art of future AI, builds the bridge
between agents and environments through Markov Decision Chain or Neural Network
and has seldom been used in power system. The art of RL is that once the
simulator for a specific environment is built, the algorithm can keep learning
from the environment. Therefore, RL is capable of dealing with constantly
changing simulator inputs such as power demand, the condition of power system
and outdoor temperature, etc. Compared with the existing distribution power
system planning mechanisms and the related game theoretical methodologies, our
proposed algorithm can plan and optimize the hourly energy usage, and have the
ability to corporate with even shorter time window if needed.
Extension of Convolutional Neural Network with General Image Processing Kernels
Authors: Jay Hoon Jung, Yousun Shin, YoungMin Kwon
Date: 2019-01-16 00:00:00

We applied pre-defined kernels also known as filters or masks developed for
image processing to convolution neural network. Instead of letting neural
networks find its own kernels, we used 41 different general-purpose kernels of
blurring, edge detecting, sharpening, discrete cosine transformation, etc. for
the first layer of the convolution neural networks. This architecture, thus
named as general filter convolutional neural network (GFNN), can reduce
training time by 30% with a better accuracy compared to the regular
convolutional neural network (CNN). GFNN also can be trained to achieve 90%
accuracy with only 500 samples. Furthermore, even though these kernels are not
specialized for the MNIST dataset, we achieved 99.56% accuracy without ensemble
nor any other special algorithms.
Measuring Effectiveness of Video Advertisements
Authors: James Hahn, Adriana Kovashka
Date: 2019-01-15 00:00:00

Advertisements are unavoidable in modern society. Times Square is notorious
for its incessant display of advertisements. Its popularity is worldwide and
smaller cities possess miniature versions of the display, such as Pittsburgh
and its digital works in Oakland on Forbes Avenue. Tokyo's Ginza district
recently rose to popularity due to its upscale shops and constant onslaught of
advertisements to pedestrians. Advertisements arise in other mediums as well.
For example, they help popular streaming services, such as Spotify, Hulu, and
Youtube TV gather significant streams of revenue to reduce the cost of monthly
subscriptions for consumers. Ads provide an additional source of money for
companies and entire industries to allocate resources toward alternative
business motives. They are attractive to companies and nearly unavoidable for
consumers. One challenge for advertisers is examining a advertisement's
effectiveness or usefulness in conveying a message to their targeted
demographics. Rather than constructing a single, static image of content, a
video advertisement possesses hundreds of frames of data with varying scenes,
actors, objects, and complexity. Therefore, measuring effectiveness of video
advertisements is important to impacting a billion-dollar industry. This paper
explores the combination of human-annotated features and common video
processing techniques to predict effectiveness ratings of advertisements
collected from Youtube. This task is seen as a binary (effective vs.
non-effective), four-way, and five-way machine learning classification task.
The first findings in terms of accuracy and inference on this dataset, as well
as some of the first ad research, on a small dataset are presented. Accuracies
of 84\%, 65\%, and 55\% are reached on the binary, four-way, and five-way tasks
respectively.
Android Malware Detection Using Autoencoder
Authors: Abdelmonim Naway, Yuancheng Li
Date: 2019-01-14 00:00:00
Tasks: Android Malware Detection, Malware Detection

Smartphones have become an intrinsic part of human's life. The smartphone
unifies diverse advanced characteristics. It enables users to store various
data such as photos, health data, credential bank data, and personal
information. The Android operating system is the prevalent mobile operating
system and, in the meantime, the most targeted operating system by malware
developers. Recently the unparalleled development of Android malware put
pressure on researchers to propose effective methods to suppress the spread of
the malware. In this paper, we propose a deep learning approach for Android
malware detection. The proposed approach investigates five different feature
sets and applies Autoencoder to identify malware. The experimental results show
that the proposed approach can identify malware with high accuracy.
DCNN-GAN: Reconstructing Realistic Image from fMRI
Authors: Yunfeng Lin, Jiangbei Li, Hanjing Wang
Date: 2019-01-13 00:00:00
Tasks: Generative Adversarial Network

Visualizing the perceptual content by analyzing human functional magnetic
resonance imaging (fMRI) has been an active research area. However, due to its
high dimensionality, complex dimensional structure, and small number of samples
available, reconstructing realistic images from fMRI remains challenging.
Recently with the development of convolutional neural network (CNN) and
generative adversarial network (GAN), mapping multi-voxel fMRI data to complex,
realistic images has been made possible. In this paper, we propose a model,
DCNN-GAN, by combining a reconstruction network and GAN. We utilize the CNN for
hierarchical feature extraction and the DCNN-GAN to reconstruct more realistic
images. Extensive experiments have been conducted, showing that our method
outperforms previous works, regarding reconstruction quality and computational
cost.
Optical Flow augmented Semantic Segmentation networks for Automated Driving
Authors: Hazem Rashed, Senthil Yogamani, Ahmad El-Sallab, Pavel Krizek, Mohamed El-Helw
Date: 2019-01-11 00:00:00
Tasks: Optical Flow Estimation, Semantic Segmentation

Motion is a dominant cue in automated driving systems. Optical flow is
typically computed to detect moving objects and to estimate depth using
triangulation. In this paper, our motivation is to leverage the existing dense
optical flow to improve the performance of semantic segmentation. To provide a
systematic study, we construct four different architectures which use RGB only,
flow only, RGBF concatenated and two-stream RGB + flow. We evaluate these
networks on two automotive datasets namely Virtual KITTI and Cityscapes using
the state-of-the-art flow estimator FlowNet v2. We also make use of the ground
truth optical flow in Virtual KITTI to serve as an ideal estimator and a
standard Farneback optical flow algorithm to study the effect of noise. Using
the flow ground truth in Virtual KITTI, two-stream architecture achieves the
best results with an improvement of 4% IoU. As expected, there is a large
improvement for moving objects like trucks, vans and cars with 38%, 28% and 6%
increase in IoU. FlowNet produces an improvement of 2.4% in average IoU with
larger improvement in the moving objects corresponding to 26%, 11% and 5% in
trucks, vans and cars. In Cityscapes, flow augmentation provided an improvement
for moving objects like motorcycle and train with an increase of 17% and 7% in
IoU.
Adversarial Pseudo Healthy Synthesis Needs Pathology Factorization
Authors: Tian Xia, Agisilaos Chartsias, Sotirios A. Tsaftaris
Date: 2019-01-10 00:00:00
Tasks: Anomaly Detection, Data Augmentation, Image Generation, Translation

Pseudo healthy synthesis, i.e. the creation of a subject-specific `healthy'
image from a pathological one, could be helpful in tasks such as anomaly
detection, understanding changes induced by pathology and disease or even as
data augmentation. We treat this task as a factor decomposition problem: we aim
to separate what appears to be healthy and where disease is (as a map). The two
factors are then recombined (by a network) to reconstruct the input disease
image. We train our models in an adversarial way using either paired or
unpaired settings, where we pair disease images and maps (as segmentation
masks) when available. We quantitatively evaluate the quality of pseudo healthy
images. We show in a series of experiments, performed in ISLES and BraTS
datasets, that our method is better than conditional GAN and CycleGAN,
highlighting challenges in using adversarial methods in the image translation
task of pseudo healthy image generation.
Learning Continuous Face Age Progression: A Pyramid of GANs
Authors: Hongyu Yang, Di Huang, Yunhong Wang, Anil K. Jain
Date: 2019-01-10 00:00:00
Tasks: Face Recognition, Generative Adversarial Network, MORPH

The two underlying requirements of face age progression, i.e. aging accuracy
and identity permanence, are not well studied in the literature. This paper
presents a novel generative adversarial network based approach to address the
issues in a coupled manner. It separately models the constraints for the
intrinsic subject-specific characteristics and the age-specific facial changes
with respect to the elapsed time, ensuring that the generated faces present
desired aging effects while simultaneously keeping personalized properties
stable. To ensure photo-realistic facial details, high-level age-specific
features conveyed by the synthesized face are estimated by a pyramidal
adversarial discriminator at multiple scales, which simulates the aging effects
with finer details. Further, an adversarial learning scheme is introduced to
simultaneously train a single generator and multiple parallel discriminators,
resulting in smooth continuous face aging sequences. The proposed method is
applicable even in the presence of variations in pose, expression, makeup,
etc., achieving remarkably vivid aging effects. Quantitative evaluations by a
COTS face recognition system demonstrate that the target age distributions are
accurately recovered, and 99.88% and 99.98% age progressed faces can be
correctly verified at 0.001% FAR after age transformations of approximately 28
and 23 years elapsed time on the MORPH and CACD databases, respectively. Both
visual and quantitative assessments show that the approach advances the
state-of-the-art.