pwc-archive/papers-with-abstracts huggingface dataset:
Instance-based Inductive Deep Transfer Learning by Cross-Dataset Querying with Locality Sensitive Hashing
Authors: Somnath Basu Roy Chowdhury, K. M. Annervaz, Ambedkar Dukkipati
Date: 2018-02-16 00:00:00
Proceeding: WS 2019 11
Tasks: News Classification, Retrieval, Transfer Learning

Supervised learning models are typically trained on a single dataset and the
performance of these models rely heavily on the size of the dataset, i.e.,
amount of data available with the ground truth. Learning algorithms try to
generalize solely based on the data that is presented with during the training.
In this work, we propose an inductive transfer learning method that can augment
learning models by infusing similar instances from different learning tasks in
the Natural Language Processing (NLP) domain. We propose to use instance
representations from a source dataset, \textit{without inheriting anything}
from the source learning model. Representations of the instances of
\textit{source} \& \textit{target} datasets are learned, retrieval of relevant
source instances is performed using soft-attention mechanism and
\textit{locality sensitive hashing}, and then, augmented into the model during
training on the target dataset. Our approach simultaneously exploits the local
\textit{instance level information} as well as the macro statistical viewpoint
of the dataset. Using this approach we have shown significant improvements for
three major news classification datasets over the baseline. Experimental
evaluations also show that the proposed approach reduces dependency on labeled
data by a significant margin for comparable performance. With our proposed
cross dataset learning procedure we show that one can achieve
competitive/better performance than learning from a single dataset.
Unsupervised word sense disambiguation in dynamic semantic spaces
Authors: Jean-François Delpech
Date: 2018-02-07 00:00:00
Tasks: Clustering, Word Sense Disambiguation

In this paper, we are mainly concerned with the ability to quickly and
automatically distinguish word senses in dynamic semantic spaces in which new
terms and new senses appear frequently. Such spaces are built '"on the fly"
from constantly evolving data sets such as Wikipedia, repositories of patent
grants and applications, or large sets of legal documents for Technology
Assisted Review and e-discovery. This immediacy rules out supervision as well
as the use of a priori training sets. We show that the various senses of a term
can be automatically made apparent with a simple clustering algorithm, each
sense being a vector in the semantic space. While we only consider here
semantic spaces built by using random vectors, this algorithm should work with
any kind of embedding, provided meaningful similarities between terms can be
computed and do fulfill at least the two basic conditions that terms which
close meanings have high similarities and terms with unrelated meanings have
near-zero similarities.
Fooling End-to-end Speaker Verification by Adversarial Examples
Authors: Felix Kreuk, Yossi Adi, Moustapha Cisse, Joseph Keshet
Date: 2018-01-10 00:00:00
Tasks: Speaker Verification

Automatic speaker verification systems are increasingly used as the primary
means to authenticate costumers. Recently, it has been proposed to train
speaker verification systems using end-to-end deep neural models. In this
paper, we show that such systems are vulnerable to adversarial example attack.
Adversarial examples are generated by adding a peculiar noise to original
speaker examples, in such a way that they are almost indistinguishable from the
original examples by a human listener. Yet, the generated waveforms, which
sound as speaker A can be used to fool such a system by claiming as if the
waveforms were uttered by speaker B. We present white-box attacks on an
end-to-end deep network that was either trained on YOHO or NTIMIT. We also
present two black-box attacks: where the adversarial examples were generated
with a system that was trained on YOHO, but the attack is on a system that was
trained on NTIMIT; and when the adversarial examples were generated with a
system that was trained on Mel-spectrum feature set, but the attack is on a
system that was trained on MFCC. Results suggest that the accuracy of the
attacked system was decreased and the false-positive rate was dramatically
increased.
Measuring Human-perceived Similarity in Heterogeneous Collections
Authors: Jesse Anderton, Pavel Metrikov, Virgil Pavlu, Javed Aslam
Date: 2018-02-16 00:00:00

We present a technique for estimating the similarity between objects such as
movies or foods whose proper representation depends on human perception. Our
technique combines a modest number of human similarity assessments to infer a
pairwise similarity function between the objects. This similarity function
captures some human notion of similarity which may be difficult or impossible
to automatically extract, such as which movie from a collection would be a
better substitute when the desired one is unavailable. In contrast to prior
techniques, our method does not assume that all similarity questions on the
collection can be answered or that all users perceive similarity in the same
way. When combined with a user model, we find how each assessor's tastes vary,
affecting their perception of similarity.
3D Regression Neural Network for the Quantification of Enlarged Perivascular Spaces in Brain MRI
Authors: Florian Dubost, Hieab Adams, Gerda Bortsova, M. Arfan Ikram, Wiro Niessen, Meike Vernooij, Marleen de Bruijne
Date: 2018-02-16 00:00:00
Tasks: object-detection, Object Detection, regression, Small Object Detection

Enlarged perivascular spaces (EPVS) in the brain are an emerging imaging
marker for cerebral small vessel disease, and have been shown to be related to
increased risk of various neurological diseases, including stroke and dementia.
Automatic quantification of EPVS would greatly help to advance research into
its etiology and its potential as a risk indicator of disease. We propose a
convolutional network regression method to quantify the extent of EPVS in the
basal ganglia from 3D brain MRI. We first segment the basal ganglia and
subsequently apply a 3D convolutional regression network designed for small
object detection within this region of interest. The network takes an image as
input, and outputs a quantification score of EPVS. The network has
significantly more convolution operations than pooling ones and no final
activation, allowing it to span the space of real numbers. We validated our
approach using a dataset of 2000 brain MRI scans scored visually. Experiments
with varying sizes of training and test sets showed that a good performance can
be achieved with a training set of only 200 scans. With a training set of 1000
scans, the intraclass correlation coefficient (ICC) between our scoring method
and the expert's visual score was 0.74. Our method outperforms by a large
margin - more than 0.10 - four more conventional automated approaches based on
intensities, scale-invariant feature transform, and random forest. We show that
the network learns the structures of interest and investigate the influence of
hyper-parameters on the performance. We also evaluate the reproducibility of
our network using a set of 60 subjects scanned twice (scan-rescan
reproducibility). On this set our network achieves an ICC of 0.93, while the
intrarater agreement reaches 0.80. Furthermore, the automatic EPVS scoring
correlates similarly to age as visual scoring.
An Image Processing based Object Counting Approach for Machine Vision Application
Authors: Mehmet Baygin, Mehmet Karakose, Alisan Sarimaden, Erhan Akin
Date: 2018-02-16 00:00:00
Tasks: Object Counting

Machine vision applications are low cost and high precision measurement
systems which are frequently used in production lines. With these systems that
provide contactless control and measurement, production facilities are able to
reach high production numbers without errors. Machine vision operations such as
product counting, error control, dimension measurement can be performed through
a camera. In this paper, a machine vision application is proposed, which can
perform object-independent product counting. The proposed approach is based on
Otsu thresholding and Hough transformation and performs automatic counting
independently of product type and color. Basically one camera is used in the
system. Through this camera, an image of the products passing through a
conveyor is taken and various image processing algorithms are applied to these
images. In this approach using images obtained from a real experimental setup,
a real-time machine vision application was installed. As a result of the
experimental studies performed, it has been determined that the proposed
approach gives fast, accurate and reliable results.
Heuristic Optimization for Automated Distribution System Planning in Network Integration Studies
Authors: Alexander Scheidler, Leon Thurner, Martin Braun
Date: 2017-11-09 00:00:00

Network integration studies try to assess the impact of future developments,
such as the increase of Renewable Energy Sources or the introduction of Smart
Grid Technologies, on large-scale network areas. Goals can be to support
strategic alignment in the regulatory framework or to adapt the network
planning principles of Distribution System Operators. This study outlines an
approach for the automated distribution system planning that can calculate
network reconfiguration, reinforcement and extension plans in a fully automated
fashion. This allows the estimation of the expected cost in massive
probabilistic simulations of large numbers of real networks and constitutes a
core component of a framework for large-scale network integration studies.
Exemplary case study results are presented that were performed in cooperation
with different major distribution system operators. The case studies cover the
estimation of expected network reinforcement costs, technical and economical
assessment of smart grid technologies and structural network optimisation.
Gaussian process modeling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria
Authors: Marko Järvenpää, Michael Gutmann, Aki Vehtari, Pekka Marttinen
Date: 2016-10-20 00:00:00
Tasks: Model Selection

Approximate Bayesian computation (ABC) can be used for model fitting when the
likelihood function is intractable but simulating from the model is feasible.
However, even a single evaluation of a complex model may take several hours,
limiting the number of model evaluations available. Modelling the discrepancy
between the simulated and observed data using a Gaussian process (GP) can be
used to reduce the number of model evaluations required by ABC, but the
sensitivity of this approach to a specific GP formulation has not yet been
thoroughly investigated. We begin with a comprehensive empirical evaluation of
using GPs in ABC, including various transformations of the discrepancies and
two novel GP formulations. Our results indicate the choice of GP may
significantly affect the accuracy of the estimated posterior distribution.
Selection of an appropriate GP model is thus important. We formulate expected
utility to measure the accuracy of classifying discrepancies below or above the
ABC threshold, and show that it can be used to automate the GP model selection
step. Finally, based on the understanding gained with toy examples, we fit a
population genetic model for bacteria, providing insight into horizontal gene
transfer events within the population and from external origins.
PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach
Authors: Luca Pappalardo, Paolo Cintia, Paolo Ferragina, Emanuele Massucco, Dino Pedreschi, Fosca Giannotti
Date: 2018-02-14 00:00:00
Tasks: BIG-bench Machine Learning

The problem of evaluating the performance of soccer players is attracting the
interest of many companies and the scientific community, thanks to the
availability of massive data capturing all the events generated during a match
(e.g., tackles, passes, shots, etc.). Unfortunately, there is no consolidated
and widely accepted metric for measuring performance quality in all of its
facets. In this paper, we design and implement PlayeRank, a data-driven
framework that offers a principled multi-dimensional and role-aware evaluation
of the performance of soccer players. We build our framework by deploying a
massive dataset of soccer-logs and consisting of millions of match events
pertaining to four seasons of 18 prominent soccer competitions. By comparing
PlayeRank to known algorithms for performance evaluation in soccer, and by
exploiting a dataset of players' evaluations made by professional soccer
scouts, we show that PlayeRank significantly outperforms the competitors. We
also explore the ratings produced by {\sf PlayeRank} and discover interesting
patterns about the nature of excellent performances and what distinguishes the
top players from the others. At the end, we explore some applications of
PlayeRank -- i.e. searching players and player versatility --- showing its
flexibility and efficiency, which makes it worth to be used in the design of a
scalable platform for soccer analytics.
A complete hand-drawn sketch vectorization framework
Authors: Luca Donati, Simone Cesano, Andrea Prati
Date: 2018-02-16 00:00:00

Vectorizing hand-drawn sketches is a challenging task, which is of paramount
importance for creating CAD vectorized versions for the fashion and creative
workflows. This paper proposes a complete framework that automatically
transforms noisy and complex hand-drawn sketches with different stroke types in
a precise, reliable and highly-simplified vectorized model. The proposed
framework includes a novel line extraction algorithm based on a
multi-resolution application of Pearson's cross correlation and a new unbiased
thinning algorithm that can get rid of scribbles and variable-width strokes to
obtain clean 1-pixel lines. Other contributions include variants of pruning,
merging and edge linking procedures to post-process the obtained paths.
Finally, a modification of the original Schneider's vectorization algorithm is
designed to obtain fewer control points in the resulting Bezier splines. All
the proposed steps of the framework have been extensively tested and compared
with state-of-the-art algorithms, showing (both qualitatively and
quantitatively) its outperformance.
X-View: Graph-Based Semantic Multi-View Localization
Authors: Abel Gawel, Carlo Del Don, Roland Siegwart, Juan Nieto, Cesar Cadena
Date: 2017-09-28 00:00:00

Global registration of multi-view robot data is a challenging task.
Appearance-based global localization approaches often fail under drastic
view-point changes, as representations have limited view-point invariance. This
work is based on the idea that human-made environments contain rich semantics
which can be used to disambiguate global localization. Here, we present X-View,
a Multi-View Semantic Global Localization system. X-View leverages semantic
graph descriptor matching for global localization, enabling localization under
drastically different view-points. While the approach is general in terms of
the semantic input data, we present and evaluate an implementation on visual
data. We demonstrate the system in experiments on the publicly available
SYNTHIA dataset, on a realistic urban dataset recorded with a simulator, and on
real-world StreetView data. Our findings show that X-View is able to globally
localize aerial-to-ground, and ground-to-ground robot data of drastically
different view-points. Our approach achieves an accuracy of up to 85 % on
global localizations in the multi-view case, while the benchmarked baseline
appearance-based methods reach up to 75 %.
Neuroscientific User Models: The Source of Uncertain User Feedback and Potentials for Improving Web Personalisation
Authors: Kevin Jasberg, Sergej Sizov
Date: 2018-02-16 00:00:00
Tasks: Decision Making, Decoder

In this paper we consider the neuroscientific theory of the Bayesian brain in
the light of adaptive web systems and content personalisation. In particular,
we elaborate on neural mechanisms of human decision-making and the origin of
lacking reliability of user feedback, often denoted as noise or human
uncertainty. To this end, we first introduce an adaptive model of cognitive
agency in which populations of neurons provide an estimation for states of the
world. Subsequently, we present various so-called decoder functions with which
neuronal activity can be translated into quantitative decisions. The interplay
of the underlying cognition model and the chosen decoder function leads to
different model-based properties of decision processes. The goal of this paper
is to promote novel user models and exploit them to naturally associate users
to different clusters on the basis of their individual neural characteristics
and thinking patterns. These user models might be able to turn the variability
of user behaviour into additional information for improving web personalisation
and its experience.
Training Deep Face Recognition Systems with Synthetic Data
Authors: Adam Kortylewski, Andreas Schneider, Thomas Gerig, Bernhard Egger, Andreas Morel-Forster, Thomas Vetter
Date: 2018-02-16 00:00:00
Tasks: Face Model, Face Recognition

Recent advances in deep learning have significantly increased the performance
of face recognition systems. The performance and reliability of these models
depend heavily on the amount and quality of the training data. However, the
collection of annotated large datasets does not scale well and the control over
the quality of the data decreases with the size of the dataset. In this work,
we explore how synthetically generated data can be used to decrease the number
of real-world images needed for training deep face recognition systems. In
particular, we make use of a 3D morphable face model for the generation of
images with arbitrary amounts of facial identities and with full control over
image variations, such as pose, illumination, and background. In our
experiments with an off-the-shelf face recognition software we observe the
following phenomena: 1) The amount of real training data needed to train
competitive deep face recognition systems can be reduced significantly. 2)
Combining large-scale real-world data with synthetic data leads to an increased
performance. 3) Models trained only on synthetic data with strong variations in
pose, illumination, and background perform very well across different datasets
even without dataset adaptation. 4) The real-to-virtual performance gap can be
closed when using synthetic data for pre-training, followed by fine-tuning with
real-world images. 5) There are no observable negative effects of pre-training
with synthetic data. Thus, any face recognition system in our experiments
benefits from using synthetic face images. The synthetic data generator, as
well as all experiments, are publicly available.
Combining Linear Non-Gaussian Acyclic Model with Logistic Regression Model for Estimating Causal Structure from Mixed Continuous and Discrete Data
Authors: Chao Li, Shohei Shimizu
Date: 2018-02-16 00:00:00
Tasks: Causal Discovery, Model Selection

Estimating causal models from observational data is a crucial task in data
analysis. For continuous-valued data, Shimizu et al. have proposed a linear
acyclic non-Gaussian model to understand the data generating process, and have
shown that their model is identifiable when the number of data is sufficiently
large. However, situations in which continuous and discrete variables coexist
in the same problem are common in practice. Most existing causal discovery
methods either ignore the discrete data and apply a continuous-valued algorithm
or discretize all the continuous data and then apply a discrete Bayesian
network approach. These methods possibly loss important information when we
ignore discrete data or introduce the approximation error due to
discretization. In this paper, we define a novel hybrid causal model which
consists of both continuous and discrete variables. The model assumes: (1) the
value of a continuous variable is a linear function of its parent variables
plus a non-Gaussian noise, and (2) each discrete variable is a logistic
variable whose distribution parameters depend on the values of its parent
variables. In addition, we derive the BIC scoring function for model selection.
The new discovery algorithm can learn causal structures from mixed continuous
and discrete data without discretization. We empirically demonstrate the power
of our method through thorough simulations.
Joint Estimation of Room Geometry and Modes with Compressed Sensing
Authors: Helena Peić Tukuljac, Thach Pham Vu, Hervé Lissek, Pierre Vandergheynst
Date: 2018-02-16 00:00:00
Tasks: compressed sensing, Room Impulse Response (RIR)

Acoustical behavior of a room for a given position of microphone and sound
source is usually described using the room impulse response. If we rely on the
standard uniform sampling, the estimation of room impulse response for
arbitrary positions in the room requires a large number of measurements. In
order to lower the required sampling rate, some solutions have emerged that
exploit the sparse representation of the room wavefield in the terms of plane
waves in the low-frequency domain. The plane wave representation has a simple
form in rectangular rooms. In our solution, we observe the basic axial modes of
the wave vector grid for extraction of the room geometry and then we propagate
the knowledge to higher order modes out of the low-pass version of the
measurements. Estimation of the approximate structure of the $k$-space should
lead to the reduction in the terms of number of required measurements and in
the increase of the speed of the reconstruction without great losses of
quality.
SpaRTA - Tracking across occlusions via global partitioning of 3D clouds of points
Authors: Andrea Cavagna, Stefania Melillo, Leonardo Parisi, Federico Ricci-Tersenghi
Date: 2018-02-16 00:00:00

Any 3D tracking algorithm has to deal with occlusions: multiple targets get
so close to each other that the loss of their identities becomes likely. In the
best case scenario, trajectories are interrupted, thus curbing the completeness
of the data-set; in the worse case scenario, identity switches arise,
potentially affecting in severe ways the very quality of the data. Here, we
present a novel tracking method that addresses the problem of occlusions within
large groups of featureless objects by means of three steps: i) it represents
each target as a cloud of points in 3D; ii) once a 3D cluster corresponding to
an occlusion occurs, it defines a partitioning problem by introducing a cost
function that uses both attractive and repulsive spatio-temporal proximity
links; iii) it minimizes the cost function through a semi-definite optimization
technique specifically designed to cope with link frustration. The algorithm is
independent of the specific experimental method used to collect the data. By
performing tests on public data-sets, we show that the new algorithm produces a
significant improvement over the state-of-the-art tracking methods, both by
reducing the number of identity switches and by increasing the accuracy of the
actual positions of the targets in real space.