pwc-archive/papers-with-abstracts huggingface dataset:
Attentional Heterogeneous Graph Neural Network: Application to Program Reidentification
Authors: Shen Wang, Zhengzhang Chen, Ding Li, Lu-An Tang, Jingchao Ni, Zhichun Li, Junghwan Rhee, Haifeng Chen, Philip S. Yu
Date: 2018-12-10 00:00:00
Tasks: Graph Classification, Graph Embedding, Graph Neural Network, Intrusion Detection, Representation Learning

Program or process is an integral part of almost every IT/OT system. Can we trust the identity/ID (e.g., executable name) of the program? To avoid detection, malware may disguise itself using the ID of a legitimate program, and a system tool (e.g., PowerShell) used by the attackers may have the fake ID of another common software, which is less sensitive. However, existing intrusion detection techniques often overlook this critical program reidentification problem (i.e., checking the program's identity). In this paper, we propose an attentional heterogeneous graph neural network model (DeepHGNN) to verify the program's identity based on its system behaviors. The key idea is to leverage the representation learning of the heterogeneous program behavior graph to guide the reidentification process. We formulate the program reidentification as a graph classification problem and develop an effective attentional heterogeneous graph embedding algorithm to solve it. Extensive experiments --- using real-world enterprise monitoring data and real attacks --- demonstrate the effectiveness of DeepHGNN across multiple popular metrics and the robustness to the normal dynamic changes like program version upgrades.
An Automatic System for Unconstrained Video-Based Face Recognition
Authors: Jingxiao Zheng, Rajeev Ranjan, Ching-Hui Chen, Jun-Cheng Chen, Carlos D. Castillo, Rama Chellappa
Date: 2018-12-10 00:00:00
Tasks: Face Recognition

Although deep learning approaches have achieved performance surpassing humans for still image-based face recognition, unconstrained video-based face recognition is still a challenging task due to large volume of data to be processed and intra/inter-video variations on pose, illumination, occlusion, scene, blur, video quality, etc. In this work, we consider challenging scenarios for unconstrained video-based face recognition from multiple-shot videos and surveillance videos with low-quality frames. To handle these problems, we propose a robust and efficient system for unconstrained video-based face recognition, which is composed of modules for face/fiducial detection, face association, and face recognition. First, we use multi-scale single-shot face detectors to efficiently localize faces in videos. The detected faces are then grouped respectively through carefully designed face association methods, especially for multi-shot videos. Finally, the faces are recognized by the proposed face matcher based on an unsupervised subspace learning approach and a subspace-to-subspace similarity metric. Extensive experiments on challenging video datasets, such as Multiple Biometric Grand Challenge (MBGC), Face and Ocular Challenge Series (FOCS), IARPA Janus Surveillance Video Benchmark (IJB-S) for low-quality surveillance videos and IARPA JANUS Benchmark B (IJB-B) for multiple-shot videos, demonstrate that the proposed system can accurately detect and associate faces from unconstrained videos and effectively learn robust and discriminative features for recognition.
Accelerating Convolutional Neural Networks via Activation Map Compression
Authors: Georgios Georgiadis
Date: 2018-12-10 00:00:00
Proceeding: CVPR 2019 6

The deep learning revolution brought us an extensive array of neural network
architectures that achieve state-of-the-art performance in a wide variety of
Computer Vision tasks including among others, classification, detection and
segmentation. In parallel, we have also been observing an unprecedented demand
in computational and memory requirements, rendering the efficient use of neural
networks in low-powered devices virtually unattainable. Towards this end, we
propose a three-stage compression and acceleration pipeline that sparsifies,
quantizes and entropy encodes activation maps of Convolutional Neural Networks.
Sparsification increases the representational power of activation maps leading
to both acceleration of inference and higher model accuracy. Inception-V3 and
MobileNet-V1 can be accelerated by as much as $1.6\times$ with an increase in
accuracy of $0.38\%$ and $0.54\%$ on the ImageNet and CIFAR-10 datasets
respectively. Quantizing and entropy coding the sparser activation maps lead to
higher compression over the baseline, reducing the memory cost of the network
execution. Inception-V3 and MobileNet-V1 activation maps, quantized to $16$
bits, are compressed by as much as $6\times$ with an increase in accuracy of
$0.36\%$ and $0.55\%$ respectively.
Supervised Deep Kriging for Single-Image Super-Resolution
Authors: Gianni Franchi, Angela Yao, Andreas Kolb
Date: 2018-12-10 00:00:00
Tasks: Image Super-Resolution, Spatial Interpolation, Super-Resolution

We propose a novel single-image super-resolution approach based on the
geostatistical method of kriging. Kriging is a zero-bias minimum-variance
estimator that performs spatial interpolation based on a weighted average of
known observations. Rather than solving for the kriging weights via the
traditional method of inverting covariance matrices, we propose a supervised
form in which we learn a deep network to generate said weights. We combine the
kriging weight generation and kriging process into a joint network that can be
learned end-to-end. Our network achieves competitive super-resolution results
as other state-of-the-art methods. In addition, since the super-resolution
process follows a known statistical framework, we are able to estimate bias and
variance, something which is rarely possible for other deep networks.
Lévy Flights of the Collective Imagination
Authors: William H. W. Thompson, Zachary Wojtowicz, Simon DeDeo
Date: 2018-12-10 00:00:00
Tasks: Translation

We present a structured random-walk model that captures key aspects of how
people communicate in groups. Our model takes the form of a correlated L\'{e}vy
flight that quantifies the balance between focused discussion of an idea and
long-distance leaps in semantic space. We apply our model to three cases of
increasing structural complexity: philosophical texts by Aristotle, Hume, and
Kant; four days of parliamentary debate during the French Revolution; and
branching comment trees on the discussion website Reddit. In the philosophical
and parliamentary cases, the model parameters that describe this balance
converge under coarse-graining to limit regions that demonstrate the emergence
of large-scale structure, a result which is robust to translation between
languages. Meanwhile, we find that the political forum we consider on Reddit
exhibits a debate-like pattern, while communities dedicated to the discussion
of science and news show much less temporal order, and may make use of the
emergent, tree-like topology of comment replies to structure their epistemic
explorations. Our model allows us to quantify the ways in which social
technologies such as parliamentary procedures and online commenting systems
shape the joint exploration of ideas.
Facial Expression Recognition using Facial Landmark Detection and Feature Extraction via Neural Networks
Authors: Fuzail Khan
Date: 2018-12-10 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER), Facial Landmark Detection

The proposed framework in this paper has the primary objective of classifying the facial expression shown by a person. These classifiable expressions can be any one of the six universal emotions along with the neutral emotion. After the initial facial localization is performed, facial landmark detection and feature extraction are applied where in the landmarks are determined to be the fiducial features: the eyebrows, eyes, nose and lips. This is primarily done using state-of-the-art facial landmark detection algorithms as well as traditional edge and corner point detection methods using Sobel filters and Shi Tomasi corner point detection methods respectively. This leads to generation of input feature vectors being formulated using Euclidean distances and trained into a Multi-Layer Perceptron (MLP) neural network in order to classify the expression being displayed. The results achieved have further dealt with higher uniformity in certain emotions and the inherently subjective nature of expression.
Pretraining by Backtranslation for End-to-end ASR in Low-Resource Settings
Authors: Matthew Wiesner, Adithya Renduchintala, Shinji Watanabe, Chunxi Liu, Najim Dehak, Sanjeev Khudanpur
Date: 2018-12-10 00:00:00
Tasks: Data Augmentation, Decoder

We explore training attention-based encoder-decoder ASR in low-resource settings. These models perform poorly when trained on small amounts of transcribed speech, in part because they depend on having sufficient target-side text to train the attention and decoder networks. In this paper we address this shortcoming by pretraining our network parameters using only text-based data and transcribed speech from other languages. We analyze the relative contributions of both sources of data. Across 3 test languages, our text-based approach resulted in a 20% average relative improvement over a text-based augmentation technique without pretraining. Using transcribed speech from nearby languages gives a further 20-30% relative reduction in character error rate.
Deep Learning with Mixed Supervision for Brain Tumor Segmentation
Authors: Pawel Mlynarski, Hervé Delingette, Antonio Criminisi, Nicholas Ayache
Date: 2018-12-10 00:00:00
Tasks: Brain Tumor Segmentation, Deep Learning, General Classification, Segmentation, Tumor Segmentation

Most of the current state-of-the-art methods for tumor segmentation are based
on machine learning models trained on manually segmented images. This type of
training data is particularly costly, as manual delineation of tumors is not
only time-consuming but also requires medical expertise. On the other hand,
images with a provided global label (indicating presence or absence of a tumor)
are less informative but can be obtained at a substantially lower cost. In this
paper, we propose to use both types of training data (fully-annotated and
weakly-annotated) to train a deep learning model for segmentation. The idea of
our approach is to extend segmentation networks with an additional branch
performing image-level classification. The model is jointly trained for
segmentation and classification tasks in order to exploit information contained
in weakly-annotated images while preventing the network to learn features which
are irrelevant for the segmentation task. We evaluate our method on the
challenging task of brain tumor segmentation in Magnetic Resonance images from
BRATS 2018 challenge. We show that the proposed approach provides a significant
improvement of segmentation performance compared to the standard supervised
learning. The observed improvement is proportional to the ratio between
weakly-annotated and fully-annotated images available for training.
Trade Selection with Supervised Learning and OCA
Authors: David Saltiel, Eric Benhamou
Date: 2018-12-09 00:00:00
Tasks: feature selection

In recent years, state-of-the-art methods for supervised learning have
exploited increasingly gradient boosting techniques, with mainstream efficient
implementations such as xgboost or lightgbm. One of the key points in
generating proficient methods is Feature Selection (FS). It consists in
selecting the right valuable effective features. When facing hundreds of these
features, it becomes critical to select best features. While filter and
wrappers methods have come to some maturity, embedded methods are truly
necessary to find the best features set as they are hybrid methods combining
features filtering and wrapping. In this work, we tackle the problem of finding
through machine learning best a priori trades from an algorithmic strategy. We
derive this new method using coordinate ascent optimization and using block
variables. We compare our method to Recursive Feature Elimination (RFE) and
Binary Coordinate Ascent (BCA). We show on a real life example the capacity of
this method to select good trades a priori. Not only this method outperforms
the initial trading strategy as it avoids taking loosing trades, it also
surpasses other method, having the smallest feature set and the highest score
at the same time. The interest of this method goes beyond this simple trade
classification problem as it is a very general method to determine the optimal
feature set using some information about features relationship as well as using
coordinate ascent optimization.
A Hybrid Distribution Feeder Long-Term Load Forecasting Method Based on Sequence Prediction
Authors: Ming Dong, L. S. Grumbach
Date: 2018-12-09 00:00:00
Tasks: Feature Engineering, feature selection, Load Forecasting, Time Series Analysis, Time Series Forecasting

Distribution feeder long-term load forecast (LTLF) is a critical task many electric utility companies perform on an annual basis. The goal of this task is to forecast the annual load of distribution feeders. The previous top-down and bottom-up LTLF methods are unable to incorporate different levels of information. This paper proposes a hybrid modeling method using sequence prediction for this classic and important task. The proposed method can seamlessly integrate top-down, bottom-up and sequential information hidden in multi-year data. Two advanced sequence prediction models Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks are investigated in this paper. They successfully solve the vanishing and exploding gradient problems a standard recurrent neural network has. This paper firstly explains the theories of LSTM and GRU networks and then discusses the steps of feature selection, feature engineering and model implementation in detail. In the end, a real-world application example for a large urban grid in West Canada is provided. LSTM and GRU networks under different sequential configurations and traditional models including bottom-up, ARIMA and feed-forward neural network are all implemented and compared in detail. The proposed method demonstrates superior performance and great practicality.
seq2graph: Discovering Dynamic Dependencies from Multivariate Time Series with Multi-level Attention
Authors: Xuan-Hong Dang, Syed Yousaf Shah, Petros Zerfos
Date: 2018-12-07 00:00:00
Tasks: Clustering, Management, Time Series, Time Series Analysis

Discovering temporal lagged and inter-dependencies in multivariate time
series data is an important task. However, in many real-world applications,
such as commercial cloud management, manufacturing predictive maintenance, and
portfolios performance analysis, such dependencies can be non-linear and
time-variant, which makes it more challenging to extract such dependencies
through traditional methods such as Granger causality or clustering. In this
work, we present a novel deep learning model that uses multiple layers of
customized gated recurrent units (GRUs) for discovering both time lagged
behaviors as well as inter-timeseries dependencies in the form of directed
weighted graphs. We introduce a key component of Dual-purpose recurrent neural
network that decodes information in the temporal domain to discover lagged
dependencies within each time series, and encodes them into a set of vectors
which, collected from all component time series, form the informative inputs to
discover inter-dependencies. Though the discovery of two types of dependencies
are separated at different hierarchical levels, they are tightly connected and
jointly trained in an end-to-end manner. With this joint training, learning of
one type of dependency immediately impacts the learning of the other one,
leading to overall accurate dependencies discovery. We empirically test our
model on synthetic time series data in which the exact form of (non-linear)
dependencies is known. We also evaluate its performance on two real-world
applications, (i) performance monitoring data from a commercial cloud provider,
which exhibit highly dynamic, non-linear, and volatile behavior and, (ii)
sensor data from a manufacturing plant. We further show how our approach is
able to capture these dependency behaviors via intuitive and interpretable
dependency graphs and use them to generate highly accurate forecasts.
DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal
Authors: Stanislas Chambon, Valentin Thorey, Pierrick J. Arnal, Emmanuel Mignot, Alexandre Gramfort
Date: 2018-12-07 00:00:00
Tasks: EEG, Electroencephalogram (EEG), K-complex detection, Sleep apnea detection, Sleep Arousal Detection, Sleep Micro-event detection, Sleep Quality, Spindle Detection, Time Series Analysis

Background: Electroencephalography (EEG) monitors brain activity during sleep
and is used to identify sleep disorders. In sleep medicine, clinicians
interpret raw EEG signals in so-called sleep stages, which are assigned by
experts to every 30s window of signal. For diagnosis, they also rely on shorter
prototypical micro-architecture events which exhibit variable durations and
shapes, such as spindles, K-complexes or arousals. Annotating such events is
traditionally performed by a trained sleep expert, making the process time
consuming, tedious and subject to inter-scorer variability. To automate this
procedure, various methods have been developed, yet these are event-specific
and rely on the extraction of hand-crafted features.
  New method: We propose a novel deep learning architecure called Dreem One
Shot Event Detector (DOSED). DOSED jointly predicts locations, durations and
types of events in EEG time series. The proposed approach, applied here on
sleep related micro-architecture events, is inspired by object detectors
developed for computer vision such as YOLO and SSD. It relies on a
convolutional neural network that builds a feature representation from raw EEG
signals, as well as two modules performing localization and classification
respectively.
  Results and comparison with other methods: The proposed approach is tested on
4 datasets and 3 types of events (spindles, K-complexes, arousals) and compared
to the current state-of-the-art detection algorithms.
  Conclusions: Results demonstrate the versatility of this new approach and
improved performance compared to the current state-of-the-art detection
methods.
Data-driven cortical clustering to provide a family of plausible solutions to M/EEG inverse problem
Authors: Kostiantyn Maksymenko, Maureen Clerc, Théodore Papadopoulo
Date: 2018-12-07 00:00:00
Tasks: Clustering, EEG, Electroencephalogram (EEG)

The M/EEG inverse problem is ill-posed. Thus additional hypotheses are needed
to constrain the solution space. In this work, we consider that brain activity
which generates an M/EEG signal is a connected cortical region. We study the
case when only one region is active at once. We show that even in this simple
case several configurations can explain the data. As opposed to methods based
on convex optimization which are forced to select one possible solution, we
propose an approach which is able to find several "good" candidates - regions
which are different in term of their sizes and/or positions but fit the data
with similar accuracy.
Towards Machine Learning Induction
Authors: Yutaka Nagashima
Date: 2018-12-04 00:00:00
Tasks: Automated Theorem Proving, BIG-bench Machine Learning

Induction lies at the heart of mathematics and computer science. However,
automated theorem proving of inductive problems is still limited in its power.
In this abstract, we first summarize our progress in automating inductive
theorem proving for Isabelle/HOL. Then, we present MeLoId, our approach to
suggesting promising applications of induction without completing a proof
search.
Kernel Treelets
Authors: Hedi Xia, Hector D. Ceniceros
Date: 2018-12-12 00:00:00
Tasks: Clustering

A new method for hierarchical clustering is presented. It combines treelets,
a particular multiscale decomposition of data, with a projection on a
reproducing kernel Hilbert space. The proposed approach, called kernel treelets
(KT), effectively substitutes the correlation coefficient matrix used in
treelets with a symmetric, positive semi-definite matrix efficiently
constructed from a kernel function. Unlike most clustering methods, which
require data sets to be numeric, KT can be applied to more general data and
yield a multi-resolution sequence of basis on the data directly in feature
space. The effectiveness and potential of KT in clustering analysis is
illustrated with some examples.
Isolating phyllotactic patterns embedded in the secondary growth of sweet cherry (Prunus avium L.) using magnetic resonance imaging
Authors: Mitchell Eithun, Daniel H. Chitwood, James Larson, Gregory Lang, Elizabeth Munch
Date: 2018-12-08 00:00:00
Tasks: Edge Detection

Epicormic branches arise from dormant buds patterned during the growth of
previous years. Dormant epicormic buds remain on the surface of trees, pushed
outward from the pith during secondary growth, but maintaining vascular
connections. Epicormic buds can be reactivated, either through natural
processes or intentionally, to rejuvenate orchards and control tree
architecture. Because epicormic structures are embedded within secondary
growth, tomographic approaches are a useful method to study them and understand
their development.
  We apply techniques from image processing to determine the locations of
epicormic vascular traces embedded within secondary growth of sweet cherry
(Prunus avium L.), revealing the juvenile phyllotactic pattern in the trunk of
an adult tree. Techniques include breadth-first search to find the pith of the
tree, edge detection to approximate the radius, and a conversion to polar
coordinates to threshold and segment phyllotactic features. Intensity values
from Magnetic Resonance Imaging (MRI) of the trunk are projected onto the
surface of a perfect cylinder to find the locations of traces in the "boundary
image". Mathematical phyllotaxy provides a means to capture the patterns in the
boundary image by modeling phyllotactic parameters. Our cherry tree specimen
has the conspicuous parastichy pair $(2,3)$, phyllotactic fraction 2/5, and
divergence angle of approximately 143 degrees.
  The methods described not only provide a framework to study phyllotaxy, but
for image processing of volumetric image data in plants. Our results have
practical implications for orchard rejuvenation and directed approaches to
influence tree architecture. The study of epicormic structures, which are
hidden within secondary growth, using tomographic methods also opens the
possibility of studying the genetic and environmental basis of such structures.
Adversarial Learning of Semantic Relevance in Text to Image Synthesis
Authors: Miriam Cha, Youngjune L. Gwon, H. T. Kung
Date: 2018-12-12 00:00:00
Tasks: Diversity, Image Generation, MS-SSIM, SSIM

We describe a new approach that improves the training of generative
adversarial nets (GANs) for synthesizing diverse images from a text input. Our
approach is based on the conditional version of GANs and expands on previous
work leveraging an auxiliary task in the discriminator. Our generated images
are not limited to certain classes and do not suffer from mode collapse while
semantically matching the text input. A key to our training methods is how to
form positive and negative training examples with respect to the class label of
a given image. Instead of selecting random training examples, we perform
negative sampling based on the semantic distance from a positive example in the
class. We evaluate our approach using the Oxford-102 flower dataset, adopting
the inception score and multi-scale structural similarity index (MS-SSIM)
metrics to assess discriminability and diversity of the generated images. The
empirical results indicate greater diversity in the generated images,
especially when we gradually select more negative training examples closer to a
positive example in the semantic space.