pwc-archive/papers-with-abstracts huggingface dataset:
A real-time analysis of rock fragmentation using UAV technology
Authors: Thomas Bamford, Kamran Esmaeili, Angela P. Schoellig
Date: 2016-07-14 00:00:00

Accurate measurement of blast-induced rock fragmentation is of great
importance for many mining operations. The post-blast rock size distribution
can significantly influence the efficiency of all the downstream mining and
comminution processes. Image analysis methods are one of the most common
methods used to measure rock fragment size distribution in mines regardless of
criticism for lack of accuracy to measure fine particles and other perceived
deficiencies. The current practice of collecting rock fragmentation data for
image analysis is highly manual and provides data with low temporal and spatial
resolution. Using UAVs for collecting images of rock fragments can not only
improve the quality of the image data but also automate the data collection
process. Ultimately, real-time acquisition of high temporal- and
spatial-resolution data based on UAV technology will provide a broad range of
opportunities for both improving blast design without interrupting the
production process and reducing the cost of the human operator. This paper
presents the results of a series of laboratory-scale rock fragment measurements
using a quadrotor UAV equipped with a camera. The goal of this work is to
highlight the benefits of aerial fragmentation analysis in terms of both
prediction accuracy and time effort. A pile of rock fragments with different
fragment sizes was placed in a lab that is equipped with a motion capture
camera system for precise UAV localization and control. Such an environment
presents optimal conditions for UAV flight and thus, is well-suited for
conducting proof-of-concept experiments before testing them in large-scale
field experiments. The pile was photographed by a camera attached to the UAV,
and the particle size distribution curves were generated in almost real-time.
The pile was also manually photographed and the results of the manual method
were compared to the UAV method.
Convolutional Residual Memory Networks
Authors: Joel Moniz, Christopher Pal
Date: 2016-06-16 00:00:00

Very deep convolutional neural networks (CNNs) yield state of the art results
on a wide variety of visual recognition problems. A number of state of the the
art methods for image recognition are based on networks with well over 100
layers and the performance vs. depth trend is moving towards networks in excess
of 1000 layers. In such extremely deep architectures the vanishing or exploding
gradient problem becomes a key issue. Recent evidence also indicates that
convolutional networks could benefit from an interface to explicitly
constructed memory mechanisms interacting with a CNN feature processing
hierarchy. Correspondingly, we propose and evaluate a memory mechanism enhanced
convolutional neural network architecture based on augmenting convolutional
residual networks with a long short term memory mechanism. We refer to this as
a convolutional residual memory network. To the best of our knowledge this
approach can yield state of the art performance on the CIFAR-100 benchmark and
compares well with other state of the art techniques on the CIFAR-10 and SVHN
benchmarks. This is achieved using networks with more breadth, much less depth
and much less overall computation relative to comparable deep ResNets without
the memory mechanism. Our experiments and analysis explore the importance of
the memory mechanism, network depth, breadth, and predictive performance.
The Chow Form of the Essential Variety in Computer Vision
Authors: Gunnar Fløystad, Joe Kileel, Giorgio Ottaviani
Date: 2016-04-15 00:00:00
Tasks: Form

The Chow form of the essential variety in computer vision is calculated. Our
derivation uses secant varieties, Ulrich sheaves and representation theory.
Numerical experiments show that our formula can detect noisy point
correspondences between two images.
Joint Deep Learning for Car Detection
Authors: Seyedshams Feyzabadi
Date: 2014-12-25 00:00:00
Tasks: Deep Learning, object-detection, Object Detection, Object Recognition, Occlusion Handling

Traditional object recognition approaches apply feature extraction, part
deformation handling, occlusion handling and classification sequentially while
they are independent from each other. Ouyang and Wang proposed a model for
jointly learning of all of the mentioned processes using one deep neural
network. We utilized, and manipulated their toolbox in order to apply it in car
detection scenarios where it had not been tested. Creating a single deep
architecture from these components, improves the interaction between them and
can enhance the performance of the whole system. We believe that the approach
can be used as a general purpose object detection toolbox. We tested the
algorithm on UIUC car dataset, and achieved an outstanding result. The accuracy
of our method was 97 % while the previously reported results showed an accuracy
of up to 91 %. We strongly believe that having an experiment on a larger
dataset can show the advantage of using deep models over shallow ones.
Fifty Shades of Ratings: How to Benefit from a Negative Feedback in Top-N Recommendations Tasks
Authors: Evgeny Frolov, Ivan Oseledets
Date: 2016-07-14 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Conventional collaborative filtering techniques treat a top-n recommendations
problem as a task of generating a list of the most relevant items. This
formulation, however, disregards an opposite - avoiding recommendations with
completely irrelevant items. Due to that bias, standard algorithms, as well as
commonly used evaluation metrics, become insensitive to negative feedback. In
order to resolve this problem we propose to treat user feedback as a
categorical variable and model it with users and items in a ternary way. We
employ a third-order tensor factorization technique and implement a higher
order folding-in method to support online recommendations. The method is
equally sensitive to entire spectrum of user ratings and is able to accurately
predict relevant items even from a negative only feedback. Our method may
partially eliminate the need for complicated rating elicitation process as it
provides means for personalized recommendations from the very beginning of an
interaction with a recommender system. We also propose a modification of
standard metrics which helps to reveal unwanted biases and account for
sensitivity to a negative feedback. Our model achieves state-of-the-art quality
in standard recommendation tasks while significantly outperforming other
methods in the cold-start "no-positive-feedback" scenarios.
Dynamic Question Ordering in Online Surveys
Authors: Kirstin Early, Jennifer Mankoff, Stephen E. Fienberg
Date: 2016-07-14 00:00:00
Tasks: Imputation, Survey

Online surveys have the potential to support adaptive questions, where later
questions depend on earlier responses. Past work has taken a rule-based
approach, uniformly across all respondents. We envision a richer interpretation
of adaptive questions, which we call dynamic question ordering (DQO), where
question order is personalized. Such an approach could increase engagement, and
therefore response rate, as well as imputation quality. We present a DQO
framework to improve survey completion and imputation. In the general
survey-taking setting, we want to maximize survey completion, and so we focus
on ordering questions to engage the respondent and collect hopefully all
information, or at least the information that most characterizes the
respondent, for accurate imputations. In another scenario, our goal is to
provide a personalized prediction. Since it is possible to give reasonable
predictions with only a subset of questions, we are not concerned with
motivating users to answer all questions. Instead, we want to order questions
to get information that reduces prediction uncertainty, while not being too
burdensome. We illustrate this framework with an example of providing energy
estimates to prospective tenants. We also discuss DQO for national surveys and
consider connections between our statistics-based question-ordering approach
and cognitive survey methodology.
Adaptable Precomputation for Random Walker Image Segmentation and Registration
Authors: Shawn Andrews, Ghassan Hamarneh
Date: 2016-07-14 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

The random walker (RW) algorithm is used for both image segmentation and
registration, and possesses several useful properties that make it popular in
medical imaging, such as being globally optimizable, allowing user interaction,
and providing uncertainty information. The RW algorithm defines a weighted
graph over an image and uses the graph's Laplacian matrix to regularize its
solutions. This regularization reduces to solving a large system of equations,
which may be excessively time consuming in some applications, such as when
interacting with a human user. Techniques have been developed that precompute
eigenvectors of a Laplacian offline, after image acquisition but before any
analysis, in order speed up the RW algorithm online, when segmentation or
registration is being performed. However, precomputation requires certain
algorithm parameters be fixed offline, limiting their flexibility. In this
paper, we develop techniques to update the precomputed data online when RW
parameters are altered. Specifically, we dynamically determine the number of
eigenvectors needed for a desired accuracy based on user input, and derive
update equations for the eigenvectors when the edge weights or topology of the
image graph are changed. We present results demonstrating that our techniques
make RW with precomputation much more robust to offline settings while only
sacrificing minimal accuracy.
Analysis of opinionated text for opinion mining
Authors: K Paramesha, K C Ravishankar
Date: 2016-07-09 00:00:00
Tasks: Opinion Mining, Sentence, Sentiment Analysis, Text Categorization

In sentiment analysis, the polarities of the opinions expressed on an
object/feature are determined to assess the sentiment of a sentence or document
whether it is positive/negative/neutral. Naturally, the object/feature is a
noun representation which refers to a product or a component of a product, let
us say, the "lens" in a camera and opinions emanating on it are captured in
adjectives, verbs, adverbs and noun words themselves. Apart from such words,
other meta-information and diverse effective features are also going to play an
important role in influencing the sentiment polarity and contribute
significantly to the performance of the system. In this paper, some of the
associated information/meta-data are explored and investigated in the sentiment
text. Based on the analysis results presented here, there is scope for further
assessment and utilization of the meta-information as features in text
categorization, ranking text document, identification of spam documents and
polarity classification problems.
Managing Overstaying Electric Vehicles in Park-and-Charge Facilities
Authors: Arpita Biswas, Ragavendran Gopalakrishnan, Partha Dutta
Date: 2016-04-19 00:00:00

With the increase in adoption of Electric Vehicles (EVs), proper utilization
of the charging infrastructure is an emerging challenge for service providers.
Overstaying of an EV after a charging event is a key contributor to low
utilization. Since overstaying is easily detectable by monitoring the power
drawn from the charger, managing this problem primarily involves designing an
appropriate "penalty" during the overstaying period. Higher penalties do
discourage overstaying; however, due to uncertainty in parking duration, less
people would find such penalties acceptable, leading to decreased utilization
(and revenue). To analyze this central trade-off, we develop a novel framework
that integrates models for realistic user behavior into queueing dynamics to
locate the optimal penalty from the points of view of utilization and revenue,
for different values of the external charging demand. Next, when the model
parameters are unknown, we show how an online learning algorithm, such as UCB,
can be adapted to learn the optimal penalty. Our experimental validation, based
on charging data from London, shows that an appropriate penalty can increase
both utilization and revenue while significantly reducing overstaying.
The Artificial Mind's Eye: Resisting Adversarials for Convolutional Neural Networks using Internal Projection
Authors: Harm Berntsen, Wouter Kuijper, Tom Heskes
Date: 2016-04-15 00:00:00

We introduce a novel artificial neural network architecture that integrates
robustness to adversarial input in the network structure. The main idea of our
approach is to force the network to make predictions on what the given instance
of the class under consideration would look like and subsequently test those
predictions. By forcing the network to redraw the relevant parts of the image
and subsequently comparing this new image to the original, we are having the
network give a "proof" of the presence of the object.
Pixel-level Encoding and Depth Layering for Instance-level Semantic Labeling
Authors: Jonas Uhrig, Marius Cordts, Uwe Franke, Thomas Brox
Date: 2016-04-18 00:00:00
Tasks: Instance Segmentation, Semantic Segmentation

Recent approaches for instance-aware semantic labeling have augmented
convolutional neural networks (CNNs) with complex multi-task architectures or
computationally expensive graphical models. We present a method that leverages
a fully convolutional network (FCN) to predict semantic labels, depth and an
instance-based encoding using each pixel's direction towards its corresponding
instance center. Subsequently, we apply low-level computer vision techniques to
generate state-of-the-art instance segmentation on the street scene datasets
KITTI and Cityscapes. Our approach outperforms existing works by a large margin
and can additionally predict absolute distances of individual instances from a
monocular image as well as a pixel-level semantic labeling.
Causality on Cross-Sectional Data: Stable Specification Search in Constrained Structural Equation Modeling
Authors: Ridho Rahmadi, Perry Groot, Marianne Heins, Hans Knoop, Tom Heskes
Date: 2015-06-18 00:00:00
Tasks: Causal Discovery

Causal modeling has long been an attractive topic for many researchers and in
recent decades there has seen a surge in theoretical development and discovery
algorithms. Generally discovery algorithms can be divided into two approaches:
constraint-based and score-based. The constraint-based approach is able to
detect common causes of the observed variables but the use of independence
tests makes it less reliable. The score-based approach produces a result that
is easier to interpret as it also measures the reliability of the inferred
causal relationships, but it is unable to detect common confounders of the
observed variables. A drawback of both score-based and constrained-based
approaches is the inherent instability in structure estimation. With finite
samples small changes in the data can lead to completely different optimal
structures. The present work introduces a new hypothesis-free score-based
causal discovery algorithm, called stable specification search, that is robust
for finite samples based on recent advances in stability selection using
subsampling and selection algorithms. Structure search is performed over
Structural Equation Models. Our approach uses exploratory search but allows
incorporation of prior background knowledge. We validated our approach on one
simulated data set, which we compare to the known ground truth, and two
real-world data sets for Chronic Fatigue Syndrome and Attention Deficit
Hyperactivity Disorder, which we compare to earlier medical studies. The
results on the simulated data set show significant improvement over alternative
approaches and the results on the real-word data sets show consistency with the
hypothesis driven models constructed by medical experts.
Multi-modal dictionary learning for image separation with application in art investigation
Authors: Nikos Deligiannis, Joao F. C. Mota, Bruno Cornelis, Miguel R. D. Rodrigues, Ingrid Daubechies
Date: 2016-07-14 00:00:00
Tasks: Dictionary Learning

In support of art investigation, we propose a new source separation method
that unmixes a single X-ray scan acquired from double-sided paintings. In this
problem, the X-ray signals to be separated have similar morphological
characteristics, which brings previous source separation methods to their
limits. Our solution is to use photographs taken from the front and back-side
of the panel to drive the separation process. The crux of our approach relies
on the coupling of the two imaging modalities (photographs and X-rays) using a
novel coupled dictionary learning framework able to capture both common and
disparate features across the modalities using parsimonious representations;
the common component models features shared by the multi-modal images, whereas
the innovation component captures modality-specific information. As such, our
model enables the formulation of appropriately regularized convex optimization
procedures that lead to the accurate separation of the X-rays. Our dictionary
learning framework can be tailored both to a single- and a multi-scale
framework, with the latter leading to a significant performance improvement.
Moreover, to improve further on the visual quality of the separated images, we
propose to train coupled dictionaries that ignore certain parts of the painting
corresponding to craquelure. Experimentation on synthetic and real data - taken
from digital acquisition of the Ghent Altarpiece (1432) - confirms the
superiority of our method against the state-of-the-art morphological component
analysis technique that uses either fixed or trained dictionaries to perform
image separation.
MAGMA: Multi-level accelerated gradient mirror descent algorithm for large-scale convex composite minimization
Authors: Vahan Hovhannisyan, Panos Parpas, Stefanos Zafeiriou
Date: 2015-09-18 00:00:00
Tasks: Face Recognition

Composite convex optimization models arise in several applications, and are
especially prevalent in inverse problems with a sparsity inducing norm and in
general convex optimization with simple constraints. The most widely used
algorithms for convex composite models are accelerated first order methods,
however they can take a large number of iterations to compute an acceptable
solution for large-scale problems. In this paper we propose to speed up first
order methods by taking advantage of the structure present in many applications
and in image processing in particular. Our method is based on multi-level
optimization methods and exploits the fact that many applications that give
rise to large scale models can be modelled using varying degrees of fidelity.
We use Nesterov's acceleration techniques together with the multi-level
approach to achieve $\mathcal{O}(1/\sqrt{\epsilon})$ convergence rate, where
$\epsilon$ denotes the desired accuracy. The proposed method has a better
convergence rate than any other existing multi-level method for convex
problems, and in addition has the same rate as accelerated methods, which is
known to be optimal for first-order methods. Moreover, as our numerical
experiments show, on large-scale face recognition problems our algorithm is
several times faster than the state of the art.
Using Recurrent Neural Network for Learning Expressive Ontologies
Authors: Giulio Petrucci, Chiara Ghidini, Marco Rospocher
Date: 2016-07-14 00:00:00
Tasks: Machine Translation, Question Answering, Sentiment Analysis, Translation

Recently, Neural Networks have been proven extremely effective in many
natural language processing tasks such as sentiment analysis, question
answering, or machine translation. Aiming to exploit such advantages in the
Ontology Learning process, in this technical report we present a detailed
description of a Recurrent Neural Network based system to be used to pursue
such goal.
EEG-informed attended speaker extraction from recorded speech mixtures with application in neuro-steered hearing prostheses
Authors: Simon Van Eyndhoven, Tom Francart, Alexander Bertrand
Date: 2016-02-18 00:00:00
Tasks: Denoising, EEG, Electroencephalogram (EEG), Speech Separation

OBJECTIVE: We aim to extract and denoise the attended speaker in a noisy,
two-speaker acoustic scenario, relying on microphone array recordings from a
binaural hearing aid, which are complemented with electroencephalography (EEG)
recordings to infer the speaker of interest. METHODS: In this study, we propose
a modular processing flow that first extracts the two speech envelopes from the
microphone recordings, then selects the attended speech envelope based on the
EEG, and finally uses this envelope to inform a multi-channel speech separation
and denoising algorithm. RESULTS: Strong suppression of interfering
(unattended) speech and background noise is achieved, while the attended speech
is preserved. Furthermore, EEG-based auditory attention detection (AAD) is
shown to be robust to the use of noisy speech signals. CONCLUSIONS: Our results
show that AAD-based speaker extraction from microphone array recordings is
feasible and robust, even in noisy acoustic environments, and without access to
the clean speech signals to perform EEG-based AAD. SIGNIFICANCE: Current
research on AAD always assumes the availability of the clean speech signals,
which limits the applicability in real settings. We have extended this research
to detect the attended speaker even when only microphone recordings with noisy
speech mixtures are available. This is an enabling ingredient for new
brain-computer interfaces and effective filtering schemes in neuro-steered
hearing prostheses. Here, we provide a first proof of concept for EEG-informed
attended speaker extraction and denoising.
Performing Highly Accurate Predictions Through Convolutional Networks for Actual Telecommunication Challenges
Authors: Jaime Zaratiegui, Ana Montoro, Federico Castanedo
Date: 2015-11-16 00:00:00
Tasks: BIG-bench Machine Learning, Feature Engineering

We investigated how the application of deep learning, specifically the use of
convolutional networks trained with GPUs, can help to build better predictive
models in telecommunication business environments, and fill this gap. In
particular, we focus on the non-trivial problem of predicting customer churn in
telecommunication operators. Our model, called WiseNet, consists of a
convolutional network and a novel encoding method that transforms customer
activity data and Call Detail Records (CDRs) into images. Experimental
evaluation with several machine learning classifiers supports the ability of
WiseNet for learning features when using structured input data. For this type
of telecommunication business problems, we found that WiseNet outperforms
machine learning models with hand-crafted features, and does not require the
labor-intensive step of feature engineering. Furthermore, the same model has
been applied without retraining to a different market, achieving consistent
results. This confirms the generalization property of WiseNet and the ability
to extract useful representations.
Adaptive Gray World-Based Color Normalization of Thin Blood Film Images
Authors: F. Boray Tek, Andrew G. Dempster, İzzet Kale
Date: 2016-07-14 00:00:00
Tasks: Color Normalization

This paper presents an effective color normalization method for thin blood
film images of peripheral blood specimens. Thin blood film images can easily be
separated to foreground (cell) and background (plasma) parts. The color of the
plasma region is used to estimate and reduce the differences arising from
different illumination conditions. A second stage normalization based on the
database-gray world algorithm transforms the color of the foreground objects to
match a reference color character. The quantitative experiments demonstrate the
effectiveness of the method and its advantages against two other general
purpose color correction methods: simple gray world and Retinex.