pwc-archive/papers-with-abstracts huggingface dataset:
Modeling and Soft-fault Diagnosis of Underwater Thrusters with Recurrent Neural Networks
Authors: Samy Nascimento, Matias Valdenegro-Toro
Date: 2018-07-11 00:00:00
Tasks: Fault Detection, Fault Diagnosis, General Classification

Noncritical soft-faults and model deviations are a challenge for Fault
Detection and Diagnosis (FDD) of resident Autonomous Underwater Vehicles
(AUVs). Such systems may have a faster performance degradation due to the
permanent exposure to the marine environment, and constant monitoring of
component conditions is required to ensure their reliability. This works
presents an evaluation of Recurrent Neural Networks (RNNs) for a data-driven
fault detection and diagnosis scheme for underwater thrusters with empirical
data. The nominal behavior of the thruster was modeled using the measured
control input, voltage, rotational speed and current signals. We evaluated the
performance of fault classification using all the measured signals compared to
using the computed residuals from the nominal model as features.
VFunc: a Deep Generative Model for Functions
Authors: Philip Bachman, Riashat Islam, Alessandro Sordoni, Zafarali Ahmed
Date: 2018-07-11 00:00:00
Tasks: model

We introduce a deep generative model for functions. Our model provides a
joint distribution p(f, z) over functions f and latent variables z which lets
us efficiently sample from the marginal p(f) and maximize a variational lower
bound on the entropy H(f). We can thus maximize objectives of the form
E_{f~p(f)}[R(f)] + c*H(f), where R(f) denotes, e.g., a data log-likelihood term
or an expected reward. Such objectives encompass Bayesian deep learning in
function space, rather than parameter space, and Bayesian deep RL with
representations of uncertainty that offer benefits over bootstrapping and
parameter noise. In this short paper we describe our model, situate it in the
context of prior work, and present proof-of-concept experiments for regression
and RL.
Variational Capsules for Image Analysis and Synthesis
Authors: Huaibo Huang, Lingxiao Song, Ran He, Zhenan Sun, Tieniu Tan
Date: 2018-07-11 00:00:00
Tasks: Attribute, Diversity, General Classification, image-classification, Image Classification, Image Generation

A capsule is a group of neurons whose activity vector models different
properties of the same entity. This paper extends the capsule to a generative
version, named variational capsules (VCs). Each VC produces a latent variable
for a specific entity, making it possible to integrate image analysis and image
synthesis into a unified framework. Variational capsules model an image as a
composition of entities in a probabilistic model. Different capsules'
divergence with a specific prior distribution represents the presence of
different entities, which can be applied in image analysis tasks such as
classification. In addition, variational capsules encode multiple entities in a
semantically-disentangling way. Diverse instantiations of capsules are related
to various properties of the same entity, making it easy to generate diverse
samples with fine-grained semantic attributes. Extensive experiments
demonstrate that deep networks designed with variational capsules can not only
achieve promising performance on image analysis tasks (including image
classification and attribute prediction) but can also improve the diversity and
controllability of image synthesis.
A Recurrent Neural Network Survival Model: Predicting Web User Return Time
Authors: Georg L. Grob, Ângelo Cardoso, C. H. Bryan Liu, Duncan A. Little, Benjamin Paul Chamberlain
Date: 2018-07-11 00:00:00
Tasks: Survival Analysis, Time Series, Time Series Analysis

The size of a website's active user base directly affects its value. Thus, it
is important to monitor and influence a user's likelihood to return to a site.
Essential to this is predicting when a user will return. Current state of the
art approaches to solve this problem come in two flavors: (1) Recurrent Neural
Network (RNN) based solutions and (2) survival analysis methods. We observe
that both techniques are severely limited when applied to this problem.
Survival models can only incorporate aggregate representations of users instead
of automatically learning a representation directly from a raw time series of
user actions. RNNs can automatically learn features, but can not be directly
trained with examples of non-returning users who have no target value for their
return time. We develop a novel RNN survival model that removes the limitations
of the state of the art methods. We demonstrate that this model can
successfully be applied to return time prediction on a large e-commerce dataset
with a superior ability to discriminate between returning and non-returning
users than either method applied in isolation.
FINN-L: Library Extensions and Design Trade-off Analysis for Variable Precision LSTM Networks on FPGAs
Authors: Vladimir Rybalkin, Alessandro Pappalardo, Muhammad Mohsin Ghaffar, Giulio Gambardella, Norbert Wehn, Michaela Blott
Date: 2018-07-11 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR), Quantization

It is well known that many types of artificial neural networks, including
recurrent networks, can achieve a high classification accuracy even with
low-precision weights and activations. The reduction in precision generally
yields much more efficient hardware implementations in regards to hardware
cost, memory requirements, energy, and achievable throughput. In this paper, we
present the first systematic exploration of this design space as a function of
precision for Bidirectional Long Short-Term Memory (BiLSTM) neural network.
Specifically, we include an in-depth investigation of precision vs. accuracy
using a fully hardware-aware training flow, where during training quantization
of all aspects of the network including weights, input, output and in-memory
cell activations are taken into consideration. In addition, hardware resource
cost, power consumption and throughput scalability are explored as a function
of precision for FPGA-based implementations of BiLSTM, and multiple approaches
of parallelizing the hardware. We provide the first open source HLS library
extension of FINN for parameterizable hardware architectures of LSTM layers on
FPGAs which offers full precision flexibility and allows for parameterizable
performance scaling offering different levels of parallelism within the
architecture. Based on this library, we present an FPGA-based accelerator for
BiLSTM neural network designed for optical character recognition, along with
numerous other experimental proof points for a Zynq UltraScale+ XCZU7EV MPSoC
within the given design space.
Proactive Intervention to Downtrend Employee Attrition using Artificial Intelligence Techniques
Authors: Aasheesh Barvey, Jitin Kapila, Kumarjit Pathak
Date: 2018-07-11 00:00:00
Tasks: General Classification, Management, regression

To predict the employee attrition beforehand and to enable management to take
individualized preventive action. Using Ensemble classification modeling
techniques and Linear Regression. Model could predict over 91% accurate
employee prediction, lead-time in separation and individual reasons causing
attrition. Prior intimation of employee attrition enables manager to take
preventive actions to retain employee or to manage the business consequences of
attrition. Once deployed this will model can help in downtrend Employee
Attrition, will help manager to manage team more effectively. Model does not
cover the natural calamities, and unforeseen events occurring at an individual
level like accident, death etc.
A punishment voting algorithm based on super categories construction for acoustic scene classification
Authors: Weiping Zheng, Zhenyao Mo, Jiantao Yi
Date: 2018-07-11 00:00:00
Tasks: Acoustic Scene Classification, Clustering, General Classification, Scene Classification

In acoustic scene classification researches, audio segment is usually split into multiple samples. Majority voting is then utilized to ensemble the results of the samples. In this paper, we propose a punishment voting algorithm based on the super categories construction method for acoustic scene classification. Specifically, we propose a DenseNet-like model as the base classifier. The base classifier is trained by the CQT spectrograms generated from the raw audio segments. Taking advantage of the results of the base classifier, we propose a super categories construction method using the spectral clustering. Super classifiers corresponding to the constructed super categories are further trained. Finally, the super classifiers are utilized to enhance the majority voting of the base classifier by punishment voting. Experiments show that the punishment voting obviously improves the performances on both the DCASE2017 Development dataset and the LITIS Rouen dataset.
MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network
Authors: Muhammed Kocabas, Salih Karagoz, Emre Akbas
Date: 2018-07-11 00:00:00
Proceeding: ECCV 2018 9
Tasks: Human Detection, Keypoint Detection, Multi-Person Pose Estimation, Pose Estimation

In this paper, we present MultiPoseNet, a novel bottom-up multi-person pose
estimation architecture that combines a multi-task model with a novel
assignment method. MultiPoseNet can jointly handle person detection, keypoint
detection, person segmentation and pose estimation problems. The novel
assignment method is implemented by the Pose Residual Network (PRN) which
receives keypoint and person detections, and produces accurate poses by
assigning keypoints to person instances. On the COCO keypoints dataset, our
pose estimation method outperforms all previous bottom-up methods both in
accuracy (+4-point mAP over previous best result) and speed; it also performs
on par with the best top-down methods while being at least 4x faster. Our
method is the fastest real time system with 23 frames/sec. Source code is
available at: https://github.com/mkocabas/pose-residual-network
Recurrent Neural Networks with Flexible Gates using Kernel Activation Functions
Authors: Simone Scardapane, Steven Van Vaerenbergh, Danilo Comminiello, Simone Totaro, Aurelio Uncini
Date: 2018-07-11 00:00:00

Gated recurrent neural networks have achieved remarkable results in the
analysis of sequential data. Inside these networks, gates are used to control
the flow of information, allowing to model even very long-term dependencies in
the data. In this paper, we investigate whether the original gate equation (a
linear projection followed by an element-wise sigmoid) can be improved. In
particular, we design a more flexible architecture, with a small number of
adaptable parameters, which is able to model a wider range of gating functions
than the classical one. To this end, we replace the sigmoid function in the
standard gate with a non-parametric formulation extending the recently proposed
kernel activation function (KAF), with the addition of a residual
skip-connection. A set of experiments on sequential variants of the MNIST
dataset shows that the adoption of this novel gate allows to improve accuracy
with a negligible cost in terms of computational power and with a large
speed-up in the number of training iterations.
Cross-spectral Iris Recognition for Mobile Applications using High-quality Color Images
Authors: Mateusz Trokielewicz, Ewelina Bartuzi
Date: 2018-07-11 00:00:00
Tasks: Iris Recognition

With the recent shift towards mobile computing, new challenges for biometric
authentication appear on the horizon. This paper provides a comprehensive study
of cross-spectral iris recognition in a scenario, in which high quality color
images obtained with a mobile phone are used against enrollment images
collected in typical, near-infrared setups. Grayscale conversion of the color
images that employs selective RGB channel choice depending on the iris
coloration is shown to improve the recognition accuracy for some combinations
of eye colors and matching software, when compared to using the red channel
only, with equal error rates driven down to as low as 2%. The authors are not
aware of any other paper focusing on cross-spectral iris recognition is a
scenario with near-infrared enrollment using a professional iris recognition
setup and then a mobile-based verification employing color images.
Presentation Attack Detection for Cadaver Iris
Authors: Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz
Date: 2018-07-11 00:00:00
Tasks: General Classification

This paper presents a deep-learning-based method for iris presentation attack
detection (PAD) when iris images are obtained from deceased people. Our
approach is based on the VGG-16 architecture fine-tuned with a database of 574
post-mortem, near-infrared iris images from the
Warsaw-BioBase-PostMortem-Iris-v1 database, complemented by a dataset of 256
images of live irises, collected within the scope of this study. Experiments
described in this paper show that our approach is able to correctly classify
iris images as either representing a live or a dead eye in almost 99% of the
trials, averaged over 20 subject-disjoint, train/test splits. We also show that
the post-mortem iris detection accuracy increases as time since death elapses,
and that we are able to construct a classification system with
APCER=0%@BPCER=1% (Attack Presentation and Bona Fide Presentation
Classification Error Rates, respectively) when only post-mortem samples
collected at least 16 hours post-mortem are considered. Since acquisitions of
ante- and post-mortem samples differ significantly, we applied countermeasures
to minimize bias in our classification methodology caused by image properties
that are not related to the PAD. This included using the same iris sensor in
collection of ante- and post-mortem samples, and analysis of class activation
maps to ensure that discriminant iris regions utilized by our classifier are
related to properties of the eye, and not to those of the acquisition protocol.
This paper offers the first known to us PAD method in a post-mortem setting,
together with an explanation of the decisions made by the convolutional neural
network. Along with the paper we offer source codes, weights of the trained
network, and a dataset of live iris images to facilitate reproducibility and
further research.
Temporal Convolution Networks for Real-Time Abdominal Fetal Aorta Analysis with Ultrasound
Authors: Nicolo' Savioli, Silvia Visentin, Erich Cosmi, Enrico Grisan, Pablo Lamata, Giovanni Montana
Date: 2018-07-11 00:00:00

The automatic analysis of ultrasound sequences can substantially improve the
efficiency of clinical diagnosis. In this work we present our attempt to
automate the challenging task of measuring the vascular diameter of the fetal
abdominal aorta from ultrasound images. We propose a neural network
architecture consisting of three blocks: a convolutional layer for the
extraction of imaging features, a Convolution Gated Recurrent Unit (C-GRU) for
enforcing the temporal coherence across video frames and exploiting the
temporal redundancy of a signal, and a regularized loss function, called
\textit{CyclicLoss}, to impose our prior knowledge about the periodicity of the
observed signal. We present experimental evidence suggesting that the proposed
architecture can reach an accuracy substantially superior to previously
proposed methods, providing an average reduction of the mean squared error from
$0.31 mm^2$ (state-of-art) to $0.09 mm^2$, and a relative error reduction from
$8.1\%$ to $5.3\%$. The mean execution speed of the proposed approach of 289
frames per second makes it suitable for real time clinical use.
UniParse: A universal graph-based parsing toolkit
Authors: Daniel Varab, Natalie Schluter
Date: 2018-07-11 00:00:00
Proceeding: WS (NoDaLiDa) 2019 9
Tasks: Dependency Parsing

This paper describes the design and use of the graph-based parsing framework
and toolkit UniParse, released as an open-source python software package.
UniParse as a framework novelly streamlines research prototyping, development
and evaluation of graph-based dependency parsing architectures. UniParse does
this by enabling highly efficient, sufficiently independent, easily readable,
and easily extensible implementations for all dependency parser components. We
distribute the toolkit with ready-made configurations as re-implementations of
all current state-of-the-art first-order graph-based parsers, including even
more efficient Cython implementations of both encoders and decoders, as well as
the required specialised loss functions.
DeSTNet: Densely Fused Spatial Transformer Networks
Authors: Roberto Annunziata, Christos Sagonas, Jacques Calì
Date: 2018-07-11 00:00:00

Modern Convolutional Neural Networks (CNN) are extremely powerful on a range
of computer vision tasks. However, their performance may degrade when the data
is characterised by large intra-class variability caused by spatial
transformations. The Spatial Transformer Network (STN) is currently the method
of choice for providing CNNs the ability to remove those transformations and
improve performance in an end-to-end learning framework. In this paper, we
propose Densely Fused Spatial Transformer Network (DeSTNet), which, to our best
knowledge, is the first dense fusion pattern for combining multiple STNs.
Specifically, we show how changing the connectivity pattern of multiple STNs
from sequential to dense leads to more powerful alignment modules. Extensive
experiments on three benchmarks namely, MNIST, GTSRB, and IDocDB show that the
proposed technique outperforms related state-of-the-art methods (i.e., STNs and
CSTNs) both in terms of accuracy and robustness.
Perception of Image Features in Post-Mortem Iris Recognition: Humans vs Machines
Authors: Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz
Date: 2018-07-11 00:00:00
Tasks: Deep Learning, General Classification, Iris Recognition

Post-mortem iris recognition can offer an additional forensic method of personal identification. However, in contrary to already well-established human examination of fingerprints, making iris recognition human-interpretable is harder, and therefore it has never been applied in forensic proceedings. There is no strong consensus among biometric experts which iris features, especially those in iris images acquired post-mortem, are the most important for human experts solving an iris recognition task. This paper explores two ways of broadening this knowledge: (a) with an eye tracker, the salient features used by humans comparing iris images on a screen are extracted, and (b) class-activation maps produced by the convolutional neural network solving the iris recognition task are analyzed. Both humans and deep learning-based solutions were examined with the same set of iris image pairs. This made it possible to compare the attention maps and conclude that (a) deep learning-based method can offer human-interpretable decisions backed by visual explanations pointing a human examiner to salient regions, and (b) in many cases humans and a machine used different features, what means that a deep learning-based method can offer a complementary support to human experts. This paper offers the first known to us human-interpretable comparison of machine-based and human-based post-mortem iris recognition, and the trained models annotating salient iris image regions.
CG-DIQA: No-reference Document Image Quality Assessment Based on Character Gradient
Authors: Hongyu Li, Fan Zhu, Junhua Qiu
Date: 2018-07-11 00:00:00
Tasks: Document Image Quality Assessment, Image Quality Assessment, Optical Character Recognition (OCR)

Document image quality assessment (DIQA) is an important and challenging
problem in real applications. In order to predict the quality scores of
document images, this paper proposes a novel no-reference DIQA method based on
character gradient, where the OCR accuracy is used as a ground-truth quality
metric. Character gradient is computed on character patches detected with the
maximally stable extremal regions (MSER) based method. Character patches are
essentially significant to character recognition and therefore suitable for use
in estimating document image quality. Experiments on a benchmark dataset show
that the proposed method outperforms the state-of-the-art methods in estimating
the quality score of document images.
Learning Singularity Avoidance
Authors: Jeevan Manavalan, Matthew Howard
Date: 2018-07-11 00:00:00

With the increase in complexity of robotic systems and the rise in non-expert
users, it can be assumed that task constraints are not explicitly known. In
tasks where avoiding singularity is critical to its success, this paper
provides an approach, especially for non-expert users, for the system to learn
the constraints contained in a set of demonstrations, such that they can be
used to optimise an autonomous controller to avoid singularity, without having
to explicitly know the task constraints. The proposed approach avoids
singularity, and thereby unpredictable behaviour when carrying out a task, by
maximising the learnt manipulability throughout the motion of the constrained
system, and is not limited to kinematic systems. Its benefits are demonstrated
through comparisons with other control policies which show that the constrained
manipulability of a system learnt through demonstration can be used to avoid
singularities in cases where these other policies would fail. In the absence of
the systems manipulability subject to a tasks constraints, the proposed
approach can be used instead to infer these with results showing errors less
than 10^-5 in 3DOF simulated systems as well as 10^-2 using a 7DOF real world
robotic system.
Improved SVD-based Initialization for Nonnegative Matrix Factorization using Low-Rank Correction
Authors: Atif Muhammad Syed, Sameer Qazi, Nicolas Gillis
Date: 2018-07-11 00:00:00

Due to the iterative nature of most nonnegative matrix factorization
(\textsc{NMF}) algorithms, initialization is a key aspect as it significantly
influences both the convergence and the final solution obtained. Many
initialization schemes have been proposed for NMF, among which one of the most
popular class of methods are based on the singular value decomposition (SVD).
However, these SVD-based initializations do not satisfy a rather natural
condition, namely that the error should decrease as the rank of factorization
increases. In this paper, we propose a novel SVD-based \textsc{NMF}
initialization to specifically address this shortcoming by taking into account
the SVD factors that were discarded to obtain a nonnegative initialization.
This method, referred to as nonnegative SVD with low-rank correction
(NNSVD-LRC), allows us to significantly reduce the initial error at a
negligible additional computational cost using the low-rank structure of the
discarded SVD factors. NNSVD-LRC has two other advantages compared to previous
SVD-based initializations: (1) it provably generates sparse initial factors,
and (2) it is faster as it only requires to compute a truncated SVD of rank
$\lceil r/2 + 1 \rceil$ where $r$ is the factorization rank of the sought NMF
decomposition (as opposed to a rank-$r$ truncated SVD for other methods). We
show on several standard dense and sparse data sets that our new method
competes favorably with state-of-the-art SVD-based initializations for NMF.