pwc-archive/papers-with-abstracts huggingface dataset:
A Contextual-bandit-based Approach for Informed Decision-making in Clinical Trials
Authors: Yogatheesan Varatharajah, Brent Berry, Sanmi Koyejo, Ravishankar Iyer
Date: 2018-09-01 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Clinical trials involving multiple treatments utilize randomization of the
treatment assignments to enable the evaluation of treatment efficacies in an
unbiased manner. Such evaluation is performed in post hoc studies that usually
use supervised-learning methods that rely on large amounts of data collected in
a randomized fashion. That approach often proves to be suboptimal in that some
participants may suffer and even die as a result of having not received the
most appropriate treatments during the trial. Reinforcement-learning methods
improve the situation by making it possible to learn the treatment efficacies
dynamically during the course of the trial, and to adapt treatment assignments
accordingly. Recent efforts using \textit{multi-arm bandits}, a type of
reinforcement-learning methods, have focused on maximizing clinical outcomes
for a population that was assumed to be homogeneous. However, those approaches
have failed to account for the variability among participants that is becoming
increasingly evident as a result of recent clinical-trial-based studies. We
present a contextual-bandit-based online treatment optimization algorithm that,
in choosing treatments for new participants in the study, takes into account
not only the maximization of the clinical outcomes but also the patient
characteristics. We evaluated our algorithm using a real clinical trial dataset
from the International Stroke Trial. The results of our retrospective analysis
indicate that the proposed approach performs significantly better than either a
random assignment of treatments (the current gold standard) or a
multi-arm-bandit-based approach, providing substantial gains in the percentage
of participants who are assigned the most suitable treatments. The
contextual-bandit and multi-arm bandit approaches provide 72.63% and 64.34%
gains, respectively, compared to a random assignment.
Parameter Sharing Methods for Multilingual Self-Attentional Translation Models
Authors: Devendra Singh Sachan, Graham Neubig
Date: 2018-09-01 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, Translation

In multilingual neural machine translation, it has been shown that sharing a
single translation model between multiple languages can achieve competitive
performance, sometimes even leading to performance gains over bilingually
trained models. However, these improvements are not uniform; often multilingual
parameter sharing results in a decrease in accuracy due to translation models
not being able to accommodate different languages in their limited parameter
space. In this work, we examine parameter sharing techniques that strike a
happy medium between full sharing and individual training, specifically
focusing on the self-attentional Transformer model. We find that the full
parameter sharing approach leads to increases in BLEU scores mainly when the
target languages are from a similar language family. However, even in the case
where target languages are from different families where full parameter sharing
leads to a noticeable drop in BLEU scores, our proposed methods for partial
sharing of parameters can lead to substantial improvements in translation
accuracy.
Car Monitoring System in Apartment Garages by Small Autonomous Car using Deep Learning
Authors: Leonardo León, Felipe Moreno-Vera, Renato Castro, José Navío, Marco Capcha
Date: 2018-09-01 00:00:00

Currently, there is an increase in the number of Peruvian families living in apartments instead of houses for the lots of advantage; However, in some cases there are troubles such as robberies of goods that are usually left at the parking lots or the entrance of strangers that use the tenants parking lots (this last trouble sometimes is related to kidnappings or robberies in building apartments). Due to these problems, the use of a self-driving mini-car is proposed to implement a monitoring system of license plates in an underground garage inside a building using a deep learning model with the aim of recording the vehicles and identifying their owners if they were tenants or not. In addition, the small robot has its own location system using beacons that allow us to identify the position of the parking lot corresponding to each tenant of the building while the mini-car is on its way. Finally, one of the objectives of this work is to build a low-cost mini-robot that would replace expensive cameras or work together in order to keep safe the goods of tenants.
Activity Recognition on a Large Scale in Short Videos - Moments in Time Dataset
Authors: Ankit Shah, Harini Kesavamoorthy, Poorva Rane, Pramati Kalwad, Alexander Hauptmann, Florian Metze
Date: 2018-09-01 00:00:00
Tasks: Action Recognition, Activity Recognition, Temporal Action Localization, Temporal Localization

Moments capture a huge part of our lives. Accurate recognition of these
moments is challenging due to the diverse and complex interpretation of the
moments. Action recognition refers to the act of classifying the desired
action/activity present in a given video. In this work, we perform experiments
on Moments in Time dataset to recognize accurately activities occurring in 3
second clips. We use state of the art techniques for visual, auditory and
spatio temporal localization and develop method to accurately classify the
activity in the Moments in Time dataset. Our novel approach of using Visual
Based Textual features and fusion techniques performs well providing an overall
89.23 % Top - 5 accuracy on the 20 classes - a significant improvement over the
Baseline TRN model.
A Machine Learning Driven IoT Solution for Noise Classification in Smart Cities
Authors: Yasser Alsouda, Sabri Pllana, Arianit Kurti
Date: 2018-09-01 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification

We present a machine learning based method for noise classification using a
low-power and inexpensive IoT unit. We use Mel-frequency cepstral coefficients
for audio feature extraction and supervised classification algorithms (that is,
support vector machine and k-nearest neighbors) for noise classification. We
evaluate our approach experimentally with a dataset of about 3000 sound samples
grouped in eight sound classes (such as, car horn, jackhammer, or street
music). We explore the parameter space of support vector machine and k-nearest
neighbors algorithms to estimate the optimal parameter values for
classification of sound samples in the dataset under study. We achieve a noise
classification accuracy in the range 85% -- 100%. Training and testing of our
k-nearest neighbors (k = 1) implementation on Raspberry Pi Zero W is less than
a second for a dataset with features of more than 3000 sound samples.
Sleep Stage Classification: Scalability Evaluations of Distributed Approaches
Authors: Serife Acikalin, Suleyman Eken, Ahmet Sayar
Date: 2018-09-01 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), General Classification

Processing and analyzing of massive clinical data are resource intensive and
time consuming with traditional analytic tools. Electroencephalogram (EEG) is
one of the major technologies in detecting and diagnosing various brain
disorders, and produces huge volume big data to process. In this study, we
propose a big data framework to diagnose sleep disorders by classifying the
sleep stages from EEG signals. The framework is developed with open source
SparkMlib Libraries. We also tested and evaluated the proposed framework by
measuring the scalabilities of well-known classification algorithms on
physionet sleep records.
VoxSegNet: Volumetric CNNs for Semantic Part Segmentation of 3D Shapes
Authors: Zongji Wang, Feng Lu
Date: 2018-09-01 00:00:00
Tasks: Segmentation

Voxel is an important format to represent geometric data, which has been
widely used for 3D deep learning in shape analysis due to its generalization
ability and regular data format. However, fine-grained tasks like part
segmentation require detailed structural information, which increases voxel
resolution and thus causes other issues such as the exhaustion of computational
resources. In this paper, we propose a novel volumetric convolutional neural
network, which could extract discriminative features encoding detailed
information from voxelized 3D data under a limited resolution. To this purpose,
a spatial dense extraction (SDE) module is designed to preserve the spatial
resolution during the feature extraction procedure, alleviating the loss of
detail caused by sub-sampling operations such as max-pooling. An attention
feature aggregation (AFA) module is also introduced to adaptively select
informative features from different abstraction scales, leading to segmentation
with both semantic consistency and high accuracy of details. Experiment results
on the large-scale dataset demonstrate the effectiveness of our method in 3D
shape part segmentation.
Finding the Answers with Definition Models
Authors: Jack Parry
Date: 2018-09-01 00:00:00
Tasks: Language Modeling, Language Modelling, Machine Translation, Translation

Inspired by a previous attempt to answer crossword questions using neural
networks (Hill, Cho, Korhonen, & Bengio, 2015), this dissertation implements
extensions to improve the performance of this existing definition model on the
task of answering crossword questions. A discussion and evaluation of the
original implementation finds that there are some ways in which the recurrent
neural model could be extended. Insights from related fields neural language
modeling and neural machine translation provide the justification and means
required for these extensions. Two extensions are applied to the LSTM encoder,
first taking the average of LSTM states across the sequence and secondly using
a bidirectional LSTM, both implementations serve to improve model performance
on a definitions and crossword test set. In order to improve performance on
crossword questions, the training data is increased to include crossword
questions and answers, and this serves to improve results on definitions as
well as crossword questions. The final experiments are conducted using sub-word
unit segmentation, first on the source side and then later preliminary
experimentation is conducted to facilitate character-level output. Initially,
an exact reproduction of the baseline results proves unsuccessful. Despite
this, the extensions improve performance, allowing the definition model to
surpass the performance of the recurrent neural network variants of the
previous work (Hill, et al., 2015).
A Multilingual Information Extraction Pipeline for Investigative Journalism
Authors: Gregor Wiedemann, Seid Muhie Yimam, Chris Biemann
Date: 2018-09-01 00:00:00
Proceeding: EMNLP 2018 11
Tasks: Entity Extraction using GAN

We introduce an advanced information extraction pipeline to automatically
process very large collections of unstructured textual data for the purpose of
investigative journalism. The pipeline serves as a new input processor for the
upcoming major release of our New/s/leak 2.0 software, which we develop in
cooperation with a large German news organization. The use case is that
journalists receive a large collection of files up to several Gigabytes
containing unknown contents. Collections may originate either from official
disclosures of documents, e.g. Freedom of Information Act requests, or
unofficial data leaks. Our software prepares a visually-aided exploration of
the collection to quickly learn about potential stories contained in the data.
It is based on the automatic extraction of entities and their co-occurrence in
documents. In contrast to comparable projects, we focus on the following three
major requirements particularly serving the use case of investigative
journalism in cross-border collaborations: 1) composition of multiple
state-of-the-art NLP tools for entity extraction, 2) support of multi-lingual
document sets up to 40 languages, 3) fast and easy-to-use extraction of
full-text, metadata and entities from various file formats.
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
Authors: Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Chen Change Loy, Yu Qiao, Xiaoou Tang
Date: 2018-09-01 00:00:00
Tasks: Face Hallucination, Generative Adversarial Network, Image Super-Resolution, Super-Resolution, Video Super-Resolution

The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work
that is capable of generating realistic textures during single image
super-resolution. However, the hallucinated details are often accompanied with
unpleasant artifacts. To further enhance the visual quality, we thoroughly
study three key components of SRGAN - network architecture, adversarial loss
and perceptual loss, and improve each of them to derive an Enhanced SRGAN
(ESRGAN). In particular, we introduce the Residual-in-Residual Dense Block
(RRDB) without batch normalization as the basic network building unit.
Moreover, we borrow the idea from relativistic GAN to let the discriminator
predict relative realness instead of the absolute value. Finally, we improve
the perceptual loss by using the features before activation, which could
provide stronger supervision for brightness consistency and texture recovery.
Benefiting from these improvements, the proposed ESRGAN achieves consistently
better visual quality with more realistic and natural textures than SRGAN and
won the first place in the PIRM2018-SR Challenge. The code is available at
https://github.com/xinntao/ESRGAN .
Evaluation of Neural Networks for Image Recognition Applications: Designing a 0-1 MILP Model of a CNN to create adversarials
Authors: Lucas Schelkes
Date: 2018-09-01 00:00:00
Tasks: Self-Driving Cars

Image Recognition is a central task in computer vision with applications
ranging across search, robotics, self-driving cars and many others. There are
three purposes of this document: 1. We follow up on (Fischetti & Jo, December,
2017) and show how standard convolutional neural network can be optimized to a
more sophisticated capsule architecture. 2. We introduce a MILP model based on
CNN to create adversarials. 3. We compare and evaluate each network for image
recognition tasks.
Iris Recognition with a Database of Iris Images Obtained in Visible Light Using Smartphone Camera
Authors: Mateusz Trokielewicz
Date: 2018-09-01 00:00:00
Tasks: Image Segmentation, Iris Recognition, Semantic Segmentation

This paper delivers a new database of iris images collected in visible light
using a mobile phone's camera and presents results of experiments involving
existing commercial and open-source iris recognition methods, namely: IriCore,
VeriEye, MIRLIN and OSIRIS. Several important observations are made.
  First, we manage to show that after simple preprocessing, such images offer
good visibility of iris texture even in heavily-pigmented irides. Second, for
all four methods, the enrollment stage is not much affected by the fact that
different type of data is used as input. This translates to zero or
close-to-zero Failure To Enroll, i.e., cases when templates could not be
extracted from the samples. Third, we achieved good matching accuracy, with
correct genuine match rate exceeding 94.5% for all four methods, while
simultaneously being able to maintain zero false match rate in every case.
Correct genuine match rate of over 99.5% was achieved using one of the
commercial methods, showing that such images can be used with the existing
biometric solutions with minimum additional effort required. Finally, the
experiments revealed that incorrect image segmentation is the most prevalent
cause of recognition accuracy decrease.
  To our best knowledge, this is the first database of iris images captured
using a mobile device, in which image quality exceeds this of a near-infrared
illuminated iris images, as defined in ISO/IEC 19794-6 and 29794-6 documents.
This database will be publicly available to all researchers.
Iris and periocular recognition in arabian race horses using deep convolutional neural networks
Authors: Mateusz Trokielewicz, Mateusz Szadkowski
Date: 2018-09-01 00:00:00
Tasks: Iris Recognition, Translation

This paper presents a study devoted to recognizing horses by means of their
iris and periocular features using deep convolutional neural networks (DCNNs).
Identification of race horses is crucial for animal identity confirmation prior
to racing. As this is usually done shortly before a race, fast and reliable
methods that are friendly and inflict no harm upon animals are important. Iris
recognition has been shown to work with horse irides, provided that algorithms
deployed for such task are fine-tuned for horse irides and input data is of
very high quality. In our work, we examine a possibility of utilizing deep
convolutional neural networks for a fusion of both iris and periocular region
features. With such methodology, ocular biometrics in horses could perform well
without employing complicated algorithms that require a lot of fine-tuning and
prior knowledge of the input image, while at the same time being rotation,
translation, and to some extent also image quality invariant. We were able to
achieve promising results, with EER=9.5% using two network architectures with
score-level fusion.
Database of iris images acquired in the presence of ocular pathologies and assessment of iris recognition reliability for disease-affected eyes
Authors: Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz
Date: 2018-09-01 00:00:00
Tasks: Iris Recognition

This paper presents a database of iris images collected from disease affected
eyes and an analysis related to the influence of ocular diseases on iris
recognition reliability. For that purpose we have collected a database of iris
images acquired for 91 different eyes during routine ophthalmology visits. This
collection gathers samples for healthy eyes as well as those with various eye
pathologies, including cataract, acute glaucoma, posterior and anterior
synechiae, retinal detachment, rubeosis iridis, corneal vascularization,
corneal grafting, iris damage and atrophy and corneal ulcers, haze or
opacities. To our best knowledge this is the first database of such kind that
will be made publicly available. In the analysis the data were divided into
five groups of samples presenting similar anticipated impact on iris
recognition: 1) healthy (no impact), 2) unaffected, clear iris (although the
illness was detected), 3) geometrically distorted irides, 4) distorted iris
tissue and 5) obstructed iris tissue. Three different iris recognition methods
(MIRLIN, VeriEye and OSIRIS) were then used to find differences in average
genuine and impostor comparison scores calculated for healthy eyes and those
impacted by a disease. Specifically, we obtained significantly worse genuine
comparison scores for all iris matchers and all disease-affected eyes when
compared to a group of healthy eyes, what have a high potential of impacting
false non-match rate.
Cataract influence on iris recognition performance
Authors: Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz
Date: 2018-09-01 00:00:00
Tasks: Image Segmentation, Iris Recognition, Semantic Segmentation

This paper presents the experimental study revealing weaker performance of
the automatic iris recognition methods for cataract-affected eyes when compared
to healthy eyes. There is little research on the topic, mostly incorporating
scarce databases that are often deficient in images representing more than one
illness. We built our own database, acquiring 1288 eye images of 37 patients of
the Medical University of Warsaw. Those images represent several common ocular
diseases, such as cataract, along with less ordinary conditions, such as iris
pattern alterations derived from illness or eye trauma. Images were captured in
near-infrared light (used in biometrics) and for selected cases also in visible
light (used in ophthalmological diagnosis). Since cataract is a disorder that
is most populated by samples in the database, in this paper we focus solely on
this illness. To assess the extent of the performance deterioration we use
three iris recognition methodologies (commercial and academic solutions) to
calculate genuine match scores for healthy eyes and those influenced by
cataract. Results show a significant degradation in iris recognition
reliability manifesting by worsening the genuine scores in all three matchers
used in this study (12% of genuine score increase for an academic matcher, up
to 175% of genuine score increase obtained for an example commercial matcher).
This increase in genuine scores affected the final false non-match rate in two
matchers. To our best knowledge this is the only study of such kind that
employs more than one iris matcher, and analyzes the iris image segmentation as
a potential source of decreased reliability.
Post-mortem Human Iris Recognition
Authors: Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz
Date: 2018-09-01 00:00:00
Tasks: Iris Recognition

This paper presents a unique analysis of post-mortem human iris recognition.
Post-mortem human iris images were collected at the university mortuary in
three sessions separated by approximately 11 hours, with the first session
organized from 5 to 7 hours after demise. Analysis performed for four
independent iris recognition methods shows that the common claim of the iris
being useless for biometric identification soon after death is not entirely
true. Since the pupil has a constant and neutral dilation after death (the so
called "cadaveric position"), this makes the iris pattern perfectly visible
from the standpoint of dilation. We found that more than 90% of irises are
still correctly recognized when captured a few hours after death, and that
serious iris deterioration begins approximately 22 hours later, since the
recognition rate drops to a range of 13.3-73.3% (depending on the method used)
when the cornea starts to be cloudy. There were only two failures to enroll
(out of 104 images) observed for only a single method (out of four employed in
this study). These findings show that the dynamics of post-mortem changes to
the iris that are important for biometric identification are much more moderate
than previously believed. To the best of our knowledge, this paper presents the
first experimental study of how iris recognition works after death, and we hope
that these preliminary findings will stimulate further research in this area.
Assessment of iris recognition reliability for eyes affected by ocular pathologies
Authors: Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz
Date: 2018-09-01 00:00:00
Tasks: Iris Recognition

This paper presents an analysis of how the iris recognition is impacted by
eye diseases and an appropriate dataset comprising 2996 iris images of 230
distinct eyes (including 184 illness-affected eyes representing more than 20
different eye conditions). The images were collected in near infrared and
visible light during a routine ophthalmological practice. The experimental
study shows four valuable results. First, the enrollment process is highly
sensitive to those eye conditions that make the iris obstructed or introduce
geometrical distortions. Second, even those conditions that do not produce
visible changes to the iris structure may increase the dissimilarity among
samples of the same eyes. Third, eye conditions affecting iris geometry, its
tissue structure or producing obstructions significantly decrease the iris
recognition reliability. Fourth, for eyes afflicted by a disease, the most
prominent effect of the disease on iris recognition is to cause segmentation
errors. To our knowledge this is the first database of iris images for
disease-affected eyes made publicly available to researchers, and the most
comprehensive study of what we can expect when the iris recognition is deployed
for non-healthy eyes.