pwc-archive/papers-with-abstracts huggingface dataset:
I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images
Authors: Codruta O. Ancuti, Cosmin Ancuti, Radu Timofte, Christophe De Vleeschouwer
Date: 2018-04-13 00:00:00
Tasks: Image Dehazing, SSIM

Image dehazing has become an important computational imaging topic in the
recent years. However, due to the lack of ground truth images, the comparison
of dehazing methods is not straightforward, nor objective. To overcome this
issue we introduce a new dataset -named I-HAZE- that contains 35 image pairs of
hazy and corresponding haze-free (ground-truth) indoor images. Different from
most of the existing dehazing databases, hazy images have been generated using
real haze produced by a professional haze machine. For easy color calibration
and improved assessment of dehazing algorithms, each scene include a MacBeth
color checker. Moreover, since the images are captured in a controlled
environment, both haze-free and hazy images are captured under the same
illumination conditions. This represents an important advantage of the I-HAZE
dataset that allows us to objectively compare the existing image dehazing
techniques using traditional image quality metrics such as PSNR and SSIM.
Regularized Singular Value Decomposition and Application to Recommender System
Authors: Shuai Zheng, Chris Ding, Feiping Nie
Date: 2018-04-13 00:00:00
Tasks: BIG-bench Machine Learning, Recommendation Systems

Singular value decomposition (SVD) is the mathematical basis of principal
component analysis (PCA). Together, SVD and PCA are one of the most widely used
mathematical formalism/decomposition in machine learning, data mining, pattern
recognition, artificial intelligence, computer vision, signal processing, etc.
In recent applications, regularization becomes an increasing trend. In this
paper, we present a regularized SVD (RSVD), present an efficient computational
algorithm, and provide several theoretical analysis. We show that although RSVD
is non-convex, it has a closed-form global optimal solution. Finally, we apply
RSVD to the application of recommender system and experimental result show that
RSVD outperforms SVD significantly.
Sí o no, què penses? Catalonian Independence and Linguistic Identity on Social Media
Authors: Ian Stewart, Yuval Pinter, Jacob Eisenstein
Date: 2018-04-13 00:00:00

Political identity is often manifested in language variation, but the
relationship between the two is still relatively unexplored from a quantitative
perspective. This study examines the use of Catalan, a language local to the
semi-autonomous region of Catalonia in Spain, on Twitter in discourse related
to the 2017 independence referendum. We corroborate prior findings that
pro-independence tweets are more likely to include the local language than
anti-independence tweets. We also find that Catalan is used more often in
referendum-related discourse than in other contexts, contrary to prior findings
on language variation. This suggests a strong role for the Catalan language in
the expression of Catalonian political identity.
Compare and Contrast: Learning Prominent Visual Differences
Authors: Steven Chen, Kristen Grauman
Date: 2018-03-31 00:00:00
Proceeding: CVPR 2018 6
Tasks: Attribute, Image Classification

Relative attribute models can compare images in terms of all detected
properties or attributes, exhaustively predicting which image is fancier, more
natural, and so on without any regard to ordering. However, when humans compare
images, certain differences will naturally stick out and come to mind first.
These most noticeable differences, or prominent differences, are likely to be
described first. In addition, many differences, although present, may not be
mentioned at all. In this work, we introduce and model prominent differences, a
rich new functionality for comparing images. We collect instance-level
annotations of most noticeable differences, and build a model trained on
relative attribute features that predicts prominent differences for unseen
pairs. We test our model on the challenging UT-Zap50K shoes and LFW10 faces
datasets, and outperform an array of baseline methods. We then demonstrate how
our prominence model improves two vision tasks, image search and description
generation, enabling more natural communication between people and vision
systems.
Fine-Grained Head Pose Estimation Without Keypoints
Authors: Nataniel Ruiz, Eunji Chong, James M. Rehg
Date: 2017-10-02 00:00:00
Tasks: Face Alignment, Gaze Estimation, Head Pose Estimation, Pose Estimation

Estimating the head pose of a person is a crucial problem that has a large
amount of applications such as aiding in gaze estimation, modeling attention,
fitting 3D models to video and performing face alignment. Traditionally head
pose is computed by estimating some keypoints from the target face and solving
the 2D to 3D correspondence problem with a mean human head model. We argue that
this is a fragile method because it relies entirely on landmark detection
performance, the extraneous head model and an ad-hoc fitting step. We present
an elegant and robust way to determine pose by training a multi-loss
convolutional neural network on 300W-LP, a large synthetically expanded
dataset, to predict intrinsic Euler angles (yaw, pitch and roll) directly from
image intensities through joint binned pose classification and regression. We
present empirical tests on common in-the-wild pose benchmark datasets which
show state-of-the-art results. Additionally we test our method on a dataset
usually used for pose estimation using depth and start to close the gap with
state-of-the-art depth pose methods. We open-source our training and testing
code as well as release our pre-trained models.
Machine Learning in Astronomy: A Case Study in Quasar-Star Classification
Authors: Mohammed Viquar, Suryoday Basak, Ariruna Dasgupta, Surbhi Agrawal, Snehanshu Saha
Date: 2018-04-13 00:00:00
Tasks: Astronomy, BIG-bench Machine Learning, General Classification

We present the results of various automated classification methods, based on
machine learning (ML), of objects from data releases 6 and 7 (DR6 and DR7) of
the Sloan Digital Sky Survey (SDSS), primarily distinguishing stars from
quasars. We provide a careful scrutiny of approaches available in the
literature and have highlighted the pitfalls in those approaches based on the
nature of data used for the study. The aim is to investigate the
appropriateness of the application of certain ML methods. The manuscript argues
convincingly in favor of the efficacy of asymmetric AdaBoost to classify
photometric data. The paper presents a critical review of existing study and
puts forward an application of asymmetric AdaBoost, as an offspring of that
exercise.
Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution
Authors: Ying Qu, Hairong Qi, Chiman Kwan
Date: 2018-04-13 00:00:00
Proceeding: CVPR 2018 6
Tasks: Decoder, Hyperspectral Image Super-Resolution, Image Super-Resolution, Super-Resolution

In many computer vision applications, obtaining images of high resolution in
both the spatial and spectral domains are equally important. However, due to
hardware limitations, one can only expect to acquire images of high resolution
in either the spatial or spectral domains. This paper focuses on hyperspectral
image super-resolution (HSI-SR), where a hyperspectral image (HSI) with low
spatial resolution (LR) but high spectral resolution is fused with a
multispectral image (MSI) with high spatial resolution (HR) but low spectral
resolution to obtain HR HSI. Existing deep learning-based solutions are all
supervised that would need a large training set and the availability of HR HSI,
which is unrealistic. Here, we make the first attempt to solving the HSI-SR
problem using an unsupervised encoder-decoder architecture that carries the
following uniquenesses. First, it is composed of two encoder-decoder networks,
coupled through a shared decoder, in order to preserve the rich spectral
information from the HSI network. Second, the network encourages the
representations from both modalities to follow a sparse Dirichlet distribution
which naturally incorporates the two physical constraints of HSI and MSI.
Third, the angular difference between representations are minimized in order to
reduce the spectral distortion. We refer to the proposed architecture as
unsupervised Sparse Dirichlet-Net, or uSDN. Extensive experimental results
demonstrate the superior performance of uSDN as compared to the
state-of-the-art.
Pieces of Eight: 8-bit Neural Machine Translation
Authors: Jerry Quinn, Miguel Ballesteros
Date: 2018-04-13 00:00:00
Proceeding: NAACL 2018 6
Tasks: Machine Translation, Quantization, Translation

Neural machine translation has achieved levels of fluency and adequacy that
would have been surprising a short time ago. Output quality is extremely
relevant for industry purposes, however it is equally important to produce
results in the shortest time possible, mainly for latency-sensitive
applications and to control cloud hosting costs. In this paper we show the
effectiveness of translating with 8-bit quantization for models that have been
trained using 32-bit floating point values. Results show that 8-bit translation
makes a non-negligible impact in terms of speed with no degradation in accuracy
and adequacy.
Neologisms on Facebook
Authors: Nikita Muravyev, Alexander Panchenko, Sergei Obiedkov
Date: 2018-04-13 00:00:00
Tasks: Marketing

In this paper, we present a study of neologisms and loan words frequently
occurring in Facebook user posts. We have analyzed a dataset of several million
publically available posts written during 2006-2013 by Russian-speaking
Facebook users. From these, we have built a vocabulary of most frequent
lemmatized words missing from the OpenCorpora dictionary the assumption being
that many such words have entered common use only recently. This assumption is
certainly not true for all the words extracted in this way; for that reason, we
manually filtered the automatically obtained list in order to exclude
non-Russian or incorrectly lemmatized words, as well as words recorded by other
dictionaries or those occurring in texts from the Russian National Corpus. The
result is a list of 168 words that can potentially be considered neologisms. We
present an attempt at an etymological classification of these neologisms
(unsurprisingly, most of them have recently been borrowed from English, but
there are also quite a few new words composed of previously borrowed stems) and
identify various derivational patterns. We also classify words into several
large thematic areas, "internet", "marketing", and "multimedia" being among
those with the largest number of words. We believe that, together with the word
base collected in the process, they can serve as a starting point in further
studies of neologisms and lexical processes that lead to their acceptance into
the mainstream language.
A Deep Learning Approach to Fast, Format-Agnostic Detection of Malicious Web Content
Authors: Joshua Saxe, Richard Harang, Cody Wild, Hillary Sanders
Date: 2018-04-13 00:00:00

Malicious web content is a serious problem on the Internet today. In this
paper we propose a deep learning approach to detecting malevolent web pages.
While past work on web content detection has relied on syntactic parsing or on
emulation of HTML and Javascript to extract features, our approach operates
directly on a language-agnostic stream of tokens extracted directly from static
HTML files with a simple regular expression. This makes it fast enough to
operate in high-frequency data contexts like firewalls and web proxies, and
allows it to avoid the attack surface exposure of complex parsing and emulation
code. Unlike well-known approaches such as bag-of-words models, which ignore
spatial information, our neural network examines content at hierarchical
spatial scales, allowing our model to capture locality and yielding superior
accuracy compared to bag-of-words baselines. Our proposed architecture achieves
a 97.5% detection rate at a 0.1% false positive rate, and classifies
small-batched web pages at a rate of over 100 per second on commodity hardware.
The speed and accuracy of our approach makes it appropriate for deployment to
endpoints, firewalls, and web proxies.
Comparatives, Quantifiers, Proportions: A Multi-Task Model for the Learning of Quantities from Vision
Authors: Sandro Pezzelle, Ionut-Teodor Sorodoc, Raffaella Bernardi
Date: 2018-04-13 00:00:00
Proceeding: NAACL 2018 6

The present work investigates whether different quantification mechanisms
(set comparison, vague quantification, and proportional estimation) can be
jointly learned from visual scenes by a multi-task computational model. The
motivation is that, in humans, these processes underlie the same cognitive,
non-symbolic ability, which allows an automatic estimation and comparison of
set magnitudes. We show that when information about lower-complexity tasks is
available, the higher-level proportional task becomes more accurate than when
performed in isolation. Moreover, the multi-task model is able to generalize to
unseen combinations of target/non-target objects. Consistently with behavioral
evidence showing the interference of absolute number in the proportional task,
the multi-task model no longer works when asked to provide the number of target
objects in the scene.
Incorporating Dictionaries into Deep Neural Networks for the Chinese Clinical Named Entity Recognition
Authors: Qi. Wang, Yuhang Xia, Yangming Zhou, Tong Ruan, Daqi Gao, Ping He
Date: 2018-04-13 00:00:00
Tasks: Feature Engineering, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Task 2

Clinical Named Entity Recognition (CNER) aims to identify and classify
clinical terms such as diseases, symptoms, treatments, exams, and body parts in
electronic health records, which is a fundamental and crucial task for clinical
and translational research. In recent years, deep neural networks have achieved
significant success in named entity recognition and many other Natural Language
Processing (NLP) tasks. Most of these algorithms are trained end to end, and
can automatically learn features from large scale labeled datasets. However,
these data-driven methods typically lack the capability of processing rare or
unseen entities. Previous statistical methods and feature engineering practice
have demonstrated that human knowledge can provide valuable information for
handling rare and unseen cases. In this paper, we address the problem by
incorporating dictionaries into deep neural networks for the Chinese CNER task.
Two different architectures that extend the Bi-directional Long Short-Term
Memory (Bi-LSTM) neural network and five different feature representation
schemes are proposed to handle the task. Computational results on the CCKS-2017
Task 2 benchmark dataset show that the proposed method achieves the highly
competitive performance compared with the state-of-the-art deep learning
methods.
Robust and Globally Optimal Manhattan Frame Estimation in Near Real Time
Authors: Kyungdon Joo, Tae-Hyun Oh, Junsik Kim, In So Kweon
Date: 2016-05-12 00:00:00
Tasks: Clustering, Video Stabilization

Most man-made environments, such as urban and indoor scenes, consist of a set
of parallel and orthogonal planar structures. These structures are approximated
by the Manhattan world assumption, in which notion can be represented as a
Manhattan frame (MF). Given a set of inputs such as surface normals or
vanishing points, we pose an MF estimation problem as a consensus set
maximization that maximizes the number of inliers over the rotation search
space. Conventionally, this problem can be solved by a branch-and-bound
framework, which mathematically guarantees global optimality. However, the
computational time of the conventional branch-and-bound algorithms is rather
far from real-time. In this paper, we propose a novel bound computation method
on an efficient measurement domain for MF estimation, i.e., the extended
Gaussian image (EGI). By relaxing the original problem, we can compute the
bound with a constant complexity, while preserving global optimality.
Furthermore, we quantitatively and qualitatively demonstrate the performance of
the proposed method for various synthetic and real-world data. We also show the
versatility of our approach through three different applications: extension to
multiple MF estimation, 3D rotation based video stabilization, and vanishing
point estimation (line clustering).
Enhanced Word Representations for Bridging Anaphora Resolution
Authors: Yufang Hou
Date: 2018-03-13 00:00:00
Proceeding: NAACL 2018 6
Tasks: Bridging Anaphora Resolution, Semantic Similarity, Semantic Textual Similarity, Word Embeddings

Most current models of word representations(e.g.,GloVe) have successfully
captured fine-grained semantics. However, semantic similarity exhibited in
these word embeddings is not suitable for resolving bridging anaphora, which
requires the knowledge of associative similarity (i.e., relatedness) instead of
semantic similarity information between synonyms or hypernyms. We create word
embeddings (embeddings_PP) to capture such relatedness by exploring the
syntactic structure of noun phrases. We demonstrate that using embeddings_PP
alone achieves around 30% of accuracy for bridging anaphora resolution on the
ISNotes corpus. Furthermore, we achieve a substantial gain over the
state-of-the-art system (Hou et al., 2013) for bridging antecedent selection.
Roster Evaluation Based on Classifiers for the Nurse Rostering Problem
Authors: Roman Václavík, Přemysl Šůcha, Zdeněk Hanzálek
Date: 2018-04-13 00:00:00
Tasks: Scheduling

The personnel scheduling problem is a well-known NP-hard combinatorial
problem. Due to the complexity of this problem and the size of the real-world
instances, it is not possible to use exact methods, and thus heuristics,
meta-heuristics, or hyper-heuristics must be employed. The majority of
heuristic approaches are based on iterative search, where the quality of
intermediate solutions must be calculated. Unfortunately, this is
computationally highly expensive because these problems have many constraints
and some are very complex. In this study, we propose a machine learning
technique as a tool to accelerate the evaluation phase in heuristic approaches.
The solution is based on a simple classifier, which is able to determine
whether the changed solution (more precisely, the changed part of the solution)
is better than the original or not. This decision is made much faster than a
standard cost-oriented evaluation process. However, the classification process
cannot guarantee 100% correctness. Therefore, our approach, which is
illustrated using a tabu search algorithm in this study, includes a filtering
mechanism, where the classifier rejects the majority of the potentially bad
solutions and the remaining solutions are then evaluated in a standard manner.
We also show how the boosting algorithms can improve the quality of the final
solution compared with a simple classifier. We verified our proposed approach
and premises, based on standard and real-world benchmark instances, to
demonstrate the significant speedup obtained with comparable solution quality.
Convolutional Neural Networks for Skull-stripping in Brain MR Imaging using Consensus-based Silver standard Masks
Authors: Oeslle Lucena, Roberto Souza, Leticia Rittner, Richard Frayne, Roberto Lotufo
Date: 2018-04-13 00:00:00
Tasks: Skull Stripping

Convolutional neural networks (CNN) for medical imaging are constrained by
the number of annotated data required in the training stage. Usually, manual
annotation is considered to be the "gold standard". However, medical imaging
datasets that include expert manual segmentation are scarce as this step is
time-consuming, and therefore expensive. Moreover, single-rater manual
annotation is most often used in data-driven approaches making the network
optimal with respect to only that single expert. In this work, we propose a CNN
for brain extraction in magnetic resonance (MR) imaging, that is fully trained
with what we refer to as silver standard masks. Our method consists of 1)
developing a dataset with "silver standard" masks as input, and implementing
both 2) a tri-planar method using parallel 2D U-Net-based CNNs (referred to as
CONSNet) and 3) an auto-context implementation of CONSNet. The term CONSNet
refers to our integrated approach, i.e., training with silver standard masks
and using a 2D U-Net-based architecture. Our results showed that we
outperformed (i.e., larger Dice coefficients) the current state-of-the-art SS
methods. Our use of silver standard masks reduced the cost of manual
annotation, decreased inter-intra-rater variability, and avoided CNN
segmentation super-specialization towards one specific manual annotation
guideline that can occur when gold standard masks are used. Moreover, the usage
of silver standard masks greatly enlarges the volume of input annotated data
because we can relatively easily generate labels for unlabeled data. In
addition, our method has the advantage that, once trained, it takes only a few
seconds to process a typical brain image volume using modern hardware, such as
a high-end graphics processing unit. In contrast, many of the other competitive
methods have processing times in the order of minutes.
Online Fall Detection using Recurrent Neural Networks
Authors: Mirto Musci, Daniele De Martini, Nicola Blago, Tullio Facchinetti, Marco Piastra
Date: 2018-04-13 00:00:00

Unintentional falls can cause severe injuries and even death, especially if
no immediate assistance is given. The aim of Fall Detection Systems (FDSs) is
to detect an occurring fall. This information can be used to trigger the
necessary assistance in case of injury. This can be done by using either
ambient-based sensors, e.g. cameras, or wearable devices. The aim of this work
is to study the technical aspects of FDSs based on wearable devices and
artificial intelligence techniques, in particular Deep Learning (DL), to
implement an effective algorithm for on-line fall detection. The proposed
classifier is based on a Recurrent Neural Network (RNN) model with underlying
Long Short-Term Memory (LSTM) blocks. The method is tested on the publicly
available SisFall dataset, with extended annotation, and compared with the
results obtained by the SisFall authors.
An efficient deep convolutional laplacian pyramid architecture for CS reconstruction at low sampling ratios
Authors: Wenxue Cui, Heyao Xu, Xinwei Gao, Shengping Zhang, Feng Jiang, Debin Zhao
Date: 2018-04-13 00:00:00
Tasks: Blocking, compressed sensing, Compressive Sensing, Image Compression

The compressed sensing (CS) has been successfully applied to image
compression in the past few years as most image signals are sparse in a certain
domain. Several CS reconstruction models have been proposed and obtained
superior performance. However, these methods suffer from blocking artifacts or
ringing effects at low sampling ratios in most cases. To address this problem,
we propose a deep convolutional Laplacian Pyramid Compressed Sensing Network
(LapCSNet) for CS, which consists of a sampling sub-network and a
reconstruction sub-network. In the sampling sub-network, we utilize a
convolutional layer to mimic the sampling operator. In contrast to the fixed
sampling matrices used in traditional CS methods, the filters used in our
convolutional layer are jointly optimized with the reconstruction sub-network.
In the reconstruction sub-network, two branches are designed to reconstruct
multi-scale residual images and muti-scale target images progressively using a
Laplacian pyramid architecture. The proposed LapCSNet not only integrates
multi-scale information to achieve better performance but also reduces
computational cost dramatically. Experimental results on benchmark datasets
demonstrate that the proposed method is capable of reconstructing more details
and sharper edges against the state-of-the-arts methods.