pwc-archive/papers-with-abstracts huggingface dataset:
A Deep DUAL-PATH Network for Improved Mammogram Image Processing
Authors: Heyi Li, Dong-Dong Chen, William H. Nailon, Mike E. Davies, Dave Laurenson
Date: 2019-03-01 00:00:00
Tasks: General Classification

We present, for the first time, a novel deep neural network architecture
called \dcn with a dual-path connection between the input image and output
class label for mammogram image processing. This architecture is built upon
U-Net, which non-linearly maps the input data into a deep latent space. One
path of the \dcnn, the locality preserving learner, is devoted to
hierarchically extracting and exploiting intrinsic features of the input, while
the other path, called the conditional graph learner, focuses on modeling the
input-mask correlations. The learned mask is further used to improve
classification results, and the two learning paths complement each other. By
integrating the two learners our new architecture provides a simple but
effective way to jointly learn the segmentation and predict the class label.
Benefiting from the powerful expressive capacity of deep neural networks a more
discriminative representation can be learned, in which both the semantics and
structure are well preserved. Experimental results show that \dcn achieves the
best mammography segmentation and classification simultaneously, outperforming
recent state-of-the-art models.
Optimal Projection Guided Transfer Hashing for Image Retrieval
Authors: Ji Liu, Lei Zhang
Date: 2019-03-01 00:00:00
Tasks: Image Retrieval, Retrieval, Transfer Learning

Recently, learning to hash has been widely studied for image retrieval thanks
to the computation and storage efficiency of binary codes. For most existing
learning to hash methods, sufficient training images are required and used to
learn precise hashing codes. However, in some real-world applications, there
are not always sufficient training images in the domain of interest. In
addition, some existing supervised approaches need a amount of labeled data,
which is an expensive process in term of time, label and human expertise. To
handle such problems, inspired by transfer learning, we propose a simple yet
effective unsupervised hashing method named Optimal Projection Guided Transfer
Hashing (GTH) where we borrow the images of other different but related domain
i.e., source domain to help learn precise hashing codes for the domain of
interest i.e., target domain. Besides, we propose to seek for the maximum
likelihood estimation (MLE) solution of the hashing functions of target and
source domains due to the domain gap. Furthermore,an alternating optimization
method is adopted to obtain the two projections of target and source domains
such that the domain hashing disparity is reduced gradually. Extensive
experiments on various benchmark databases verify that our method outperforms
many state-of-the-art learning to hash methods. The implementation details are
available at https://github.com/liuji93/GTH.
A Framework for Detecting Event related Sentiments of a Community
Authors: Muhammad Aslam Jarwar
Date: 2019-03-01 00:00:00

Social media has revolutionized human communication and styles of
interaction. Due to its easiness and effective medium, people share and
exchange information, carry out discussion on various events, and express their
opinions. For effective policy making and understanding the response of a
community on different events, we need to monitor and analyze the social media.
In social media, there are some users who are more influential, for example, a
famous politician may have more influence than a common person. These
influential users belong to specific communities. The main object of this
research is to know the sentiments of a specific community on various events.
For detecting the event based sentiments of a community we propose a generic
framework. Our framework identifies the users of a specific community on
twitter. After identifying the users of a community, we fetch their tweets and
identify tweets belonging to specific events. The event based tweets are
pre-processed. Pre-processed tweets are then analyzed for detecting sentiments
of a community for specific events. Qualitative and quantitative evaluation
confirms the effectiveness and usefulness of our proposed framework.
Single Image Deblurring and Camera Motion Estimation with Depth Map
Authors: Liyuan Pan, Yuchao Dai, Miaomiao Liu
Date: 2019-03-01 00:00:00
Tasks: Deblurring, Image Deblurring, Motion Estimation, Single Image Deblurring

Camera shake during exposure is a major problem in hand-held photography, as
it causes image blur that destroys details in the captured images.~In the real
world, such blur is mainly caused by both the camera motion and the complex
scene structure.~While considerable existing approaches have been proposed
based on various assumptions regarding the scene structure or the camera
motion, few existing methods could handle the real 6 DoF camera motion.~In this
paper, we propose to jointly estimate the 6 DoF camera motion and remove the
non-uniform blur caused by camera motion by exploiting their underlying
geometric relationships, with a single blurry image and its depth map (either
direct depth measurements, or a learned depth map) as input.~We formulate our
joint deblurring and 6 DoF camera motion estimation as an energy minimization
problem which is solved in an alternative manner. Our model enables the
recovery of the 6 DoF camera motion and the latent clean image, which could
also achieve the goal of generating a sharp sequence from a single blurry
image. Experiments on challenging real-world and synthetic datasets demonstrate
that image blur from camera shake can be well addressed within our proposed
framework.
KT-Speech-Crawler: Automatic Dataset Construction for Speech Recognition from YouTube Videos
Authors: Egor Lakomkin, Sven Magg, Cornelius Weber, Stefan Wermter
Date: 2019-03-01 00:00:00
Proceeding: EMNLP 2018 11
Tasks: speech-recognition, Speech Recognition

In this paper, we describe KT-Speech-Crawler: an approach for automatic
dataset construction for speech recognition by crawling YouTube videos. We
outline several filtering and post-processing steps, which extract samples that
can be used for training end-to-end neural speech recognition systems. In our
experiments, we demonstrate that a single-core version of the crawler can
obtain around 150 hours of transcribed speech within a day, containing an
estimated 3.5% word error rate in the transcriptions. Automatically collected
samples contain reading and spontaneous speech recorded in various conditions
including background noise and music, distant microphone recordings, and a
variety of accents and reverberation. When training a deep neural network on
speech recognition, we observed around 40\% word error rate reduction on the
Wall Street Journal dataset by integrating 200 hours of the collected samples
into the training set. The demo (http://emnlp-demo.lakomkin.me/) and the
crawler code (https://github.com/EgorLakomkin/KTSpeechCrawler) are publicly
available.
Evaluation Mechanism of Collective Intelligence for Heterogeneous Agents Group
Authors: Anna Dai, Zhifeng Zhao, Honggang Zhang, Rongpeng Li, Yugeng Zhou
Date: 2019-03-01 00:00:00
Tasks: Decision Making

Collective intelligence is manifested when multiple agents coherently work in observation, interaction, decision-making and action. In this paper, we define and quantify the intelligence level of heterogeneous agents group with the improved Anytime Universal Intelligence Test(AUIT), based on an extension of the existing evaluation of homogeneous agents group. The relationship of intelligence level with agents composition, group size, spatial complexity and testing time is analyzed. The intelligence level of heterogeneous agents groups is compared with the homogeneous ones to analyze the effects of heterogeneity on collective intelligence. Our work will help to understand the essence of collective intelligence more deeply and reveal the effect of various key factors on group intelligence level.
Non-linear ICA based on Cramer-Wold metric
Authors: Przemysław Spurek, Aleksandra Nowak, Jacek Tabor, Łukasz Maziarka, Stanisław Jastrzębski
Date: 2019-03-01 00:00:00

Non-linear source separation is a challenging open problem with many
applications. We extend a recently proposed Adversarial Non-linear ICA (ANICA)
model, and introduce Cramer-Wold ICA (CW-ICA). In contrast to ANICA we use a
simple, closed--form optimization target instead of a discriminator--based
independence measure. Our results show that CW-ICA achieves comparable results
to ANICA, while foregoing the need for adversarial training.
Outcome-Driven Clustering of Acute Coronary Syndrome Patients using Multi-Task Neural Network with Attention
Authors: Eryu Xia, Xin Du, Jing Mei, Wen Sun, Suijun Tong, Zhiqing Kang, Jian Sheng, Jian Li, Changsheng Ma, Jian-Zeng Dong, Shaochun Li
Date: 2019-03-01 00:00:00
Tasks: Classification, Clustering, Feature Importance, General Classification

Cluster analysis aims at separating patients into phenotypically heterogenous
groups and defining therapeutically homogeneous patient subclasses. It is an
important approach in data-driven disease classification and subtyping. Acute
coronary syndrome (ACS) is a syndrome due to sudden decrease of coronary artery
blood flow, where disease classification would help to inform therapeutic
strategies and provide prognostic insights. Here we conducted outcome-driven
cluster analysis of ACS patients, which jointly considers treatment and patient
outcome as indicators for patient state. Multi-task neural network with
attention was used as a modeling framework, including learning of the patient
state, cluster analysis, and feature importance profiling. Seven patient
clusters were discovered. The clusters have different characteristics, as well
as different risk profiles to the outcome of in-hospital major adverse cardiac
events. The results demonstrate cluster analysis using outcome-driven
multi-task neural network as promising for patient classification and
subtyping.
Should All Temporal Difference Learning Use Emphasis?
Authors: Xiang Gu, Sina Ghiassian, Richard S. Sutton
Date: 2019-03-01 00:00:00
Tasks: All

Emphatic Temporal Difference (ETD) learning has recently been proposed as a
convergent off-policy learning method. ETD was proposed mainly to address
convergence issues of conventional Temporal Difference (TD) learning under
off-policy training but it is different from conventional TD learning even
under on-policy training. A simple counterexample provided back in 2017 pointed
to a potential class of problems where ETD converges but TD diverges. In this
paper, we empirically show that ETD converges on a few other well-known
on-policy experiments whereas TD either diverges or performs poorly. We also
show that ETD outperforms TD on the mountain car prediction problem. Our
results, together with a similar pattern observed under off-policy training in
prior works, suggest that ETD might be a good substitute over conventional TD.
Proximal algorithms for constrained composite optimization, with applications to solving low-rank SDPs
Authors: Yu Bai, John Duchi, Song Mei
Date: 2019-03-01 00:00:00

We study a family of (potentially non-convex) constrained optimization
problems with convex composite structure. Through a novel analysis of
non-smooth geometry, we show that proximal-type algorithms applied to exact
penalty formulations of such problems exhibit local linear convergence under a
quadratic growth condition, which the compositional structure we consider
ensures. The main application of our results is to low-rank semidefinite
optimization with Burer-Monteiro factorizations. We precisely identify the
conditions for quadratic growth in the factorized problem via structures in the
semidefinite problem, which could be of independent interest for understanding
matrix factorization.
Lung CT Imaging Sign Classification through Deep Learning on Small Data
Authors: Guocai He
Date: 2019-03-01 00:00:00
Tasks: Binary Classification, Computed Tomography (CT), General Classification, Generative Adversarial Network, Small Data Image Classification, Specificity

The annotated medical images are usually expensive to be collected. This
paper proposes a deep learning method on small data to classify Common Imaging
Signs of Lung diseases (CISL) in computed tomography (CT) images. We explore
both the real data and the data generated by Generative Adversarial Network
(GAN) to improve the reliability and the generalization of learning. First, we
use GAN to generate a large number of CISLs from small annotated data, which
are difficult to be distinguished from real counterparts. These generated
samples are used to pre-train a Convolutional Neural Network (CNN) for
classifying CISLs. Second, we fine-tune the CNN classification model with real
data. Experiments were conducted on the LISS database of CISLs. We successfully
convinced radiologists that our generated CISLs samples were real for 56.7% of
our experiments. The pre-trained CNN model achieves 88.4% of mean accuracy of
binary classification, and after fine-tuning, the mean accuracy is
significantly increased to 95.0%. For multi-classification of all types of
CISLs and normal tissues, through the two stages of training, the mean
accuracy, sensitivity and specificity are up to about 91.83%, 92.73% and 99.0%,
respectively. To our knowledge, this is the best result achieved on the LISS
database, which demonstrates that the proposed method is effective and
promising for fulfilling deep learning on small data.
Distributed Variational Bayesian Algorithms for Extended Object Tracking
Authors: Junhao Hua, Chun-Guang Li
Date: 2019-03-01 00:00:00
Tasks: Object, Object Tracking

This paper is concerned with the problem of distributed extended object
tracking, which aims to collaboratively estimate the state and extension of an
object by a network of nodes. In traditional tracking applications, most
approaches consider an object as a point source of measurements due to limited
sensor resolution capabilities. Recently, some studies consider the extended
objects, which are spatially structured, i.e., multiple resolution cells are
occupied by an object. In this setting, multiple measurements are generated by
each object per time step. In this paper, we present a Bayesian model for
extended object tracking problem in a sensor network. In this model, the object
extension is represented by a symmetric positive definite random matrix, and we
assume that the measurement noise exists but is unknown. Using this Bayesian
model, we first propose a novel centralized algorithm for extended object
tracking based on variational Bayesian methods. Then, we extend it to the
distributed scenario based on the alternating direction method of multipliers
(ADMM) technique. The proposed algorithms can simultaneously estimate the
extended object state (the kinematic state and extension) and the measurement
noise covariance. Simulations on both extended object tracking and group target
tracking are given to verify the effectiveness of the proposed model and
algorithms.
Pyramid Feature Attention Network for Saliency detection
Authors: Ting Zhao, Xiangqian Wu
Date: 2019-03-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Saliency Detection

Saliency detection is one of the basic challenges in computer vision. How to
extract effective features is a critical point for saliency detection. Recent
methods mainly adopt integrating multi-scale convolutional features
indiscriminately. However, not all features are useful for saliency detection
and some even cause interferences. To solve this problem, we propose Pyramid
Feature Attention network to focus on effective high-level context features and
low-level spatial structural features. First, we design Context-aware Pyramid
Feature Extraction (CPFE) module for multi-scale high-level feature maps to
capture rich context features. Second, we adopt channel-wise attention (CA)
after CPFE feature maps and spatial attention (SA) after low-level feature
maps, then fuse outputs of CA & SA together. Finally, we propose an edge
preservation loss to guide network to learn more detailed information in
boundary localization. Extensive evaluations on five benchmark datasets
demonstrate that the proposed method outperforms the state-of-the-art
approaches under different evaluation metrics.
Open Information Extraction from Question-Answer Pairs
Authors: Nikita Bhutani, Yoshihiko Suhara, Wang-Chiew Tan, Alon Halevy, H. V. Jagadish
Date: 2019-03-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Open Information Extraction, Sentence

Open Information Extraction (OpenIE) extracts meaningful structured tuples
from free-form text. Most previous work on OpenIE considers extracting data
from one sentence at a time. We describe NeurON, a system for extracting tuples
from question-answer pairs. Since real questions and answers often contain
precisely the information that users care about, such information is
particularly desirable to extend a knowledge base with.
  NeurON addresses several challenges. First, an answer text is often hard to
understand without knowing the question, and second, relevant information can
span multiple sentences. To address these, NeurON formulates extraction as a
multi-source sequence-to-sequence learning task, wherein it combines
distributed representations of a question and an answer to generate knowledge
facts. We describe experiments on two real-world datasets that demonstrate that
NeurON can find a significant number of new and interesting facts to extend a
knowledge base compared to state-of-the-art OpenIE methods.
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs
Authors: Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, Matt Gardner
Date: 2019-03-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Question Answering, Reading Comprehension, Semantic Parsing

Reading comprehension has recently seen rapid progress, with systems matching
humans on the most popular datasets for the task. However, a large body of work
has highlighted the brittleness of these systems, showing that there is much
work left to be done. We introduce a new English reading comprehension
benchmark, DROP, which requires Discrete Reasoning Over the content of
Paragraphs. In this crowdsourced, adversarially-created, 96k-question
benchmark, a system must resolve references in a question, perhaps to multiple
input positions, and perform discrete operations over them (such as addition,
counting, or sorting). These operations require a much more comprehensive
understanding of the content of paragraphs than what was necessary for prior
datasets. We apply state-of-the-art methods from both the reading comprehension
and semantic parsing literature on this dataset and show that the best systems
only achieve 32.7% F1 on our generalized accuracy metric, while expert human
performance is 96.0%. We additionally present a new model that combines reading
comprehension methods with simple numerical reasoning to achieve 47.0% F1.
Image-Based Geo-Localization Using Satellite Imagery
Authors: Sixing Hu, Gim Hee Lee
Date: 2019-03-01 00:00:00
Tasks: geo-localization

The problem of localization on a geo-referenced satellite map given a query ground view image is useful yet remains challenging due to the drastic change in viewpoint. To this end, in this paper we work on the extension of our earlier work on the Cross-View Matching Network (CVM-Net) for the ground-to-aerial image matching task since the traditional image descriptors fail due to the drastic viewpoint change. In particular, we show more extensive experimental results and analyses of the network architecture on our CVM-Net. Furthermore, we propose a Markov localization framework that enforces the temporal consistency between image frames to enhance the geo-localization results in the case where a video stream of ground view images is available. Experimental results show that our proposed Markov localization framework can continuously localize the vehicle within a small error on our Singapore dataset.
Characterizing Activity on the Deep and Dark Web
Authors: Nazgol Tavabi, Nathan Bartley, Andrés Abeliuk, Sandeep Soni, Emilio Ferrara, Kristina Lerman
Date: 2019-03-01 00:00:00

The deep and darkweb (d2web) refers to limited access web sites that require
registration, authentication, or more complex encryption protocols to access
them. These web sites serve as hubs for a variety of illicit activities: to
trade drugs, stolen user credentials, hacking tools, and to coordinate attacks
and manipulation campaigns. Despite its importance to cyber crime, the d2web
has not been systematically investigated. In this paper, we study a large
corpus of messages posted to 80 d2web forums over a period of more than a year.
We identify topics of discussion using LDA and use a non-parametric HMM to
model the evolution of topics across forums. Then, we examine the dynamic
patterns of discussion and identify forums with similar patterns. We show that
our approach surfaces hidden similarities across different forums and can help
identify anomalous events in this rich, heterogeneous data.
Chinese-Japanese Unsupervised Neural Machine Translation Using Sub-character Level Information
Authors: Longtu Zhang, Mamoru Komachi
Date: 2019-03-01 00:00:00
Tasks: Machine Translation, Translation

Unsupervised neural machine translation (UNMT) requires only monolingual data
of similar language pairs during training and can produce bi-directional
translation models with relatively good performance on alphabetic languages
(Lample et al., 2018). However, no research has been done to logographic
language pairs. This study focuses on Chinese-Japanese UNMT trained by data
containing sub-character (ideograph or stroke) level information which is
decomposed from character level data. BLEU scores of both character and
sub-character level systems were compared against each other and the results
showed that despite the effectiveness of UNMT on character level data,
sub-character level data could further enhance the performance, in which the
stroke level system outperformed the ideograph level system.
A Unified Neural Architecture for Instrumental Audio Tasks
Authors: Steven Spratley, Daniel Beck, Trevor Cohn
Date: 2019-03-01 00:00:00
Tasks: Information Retrieval, Music Information Retrieval, Retrieval, Super-Resolution, Transfer Learning

Within Music Information Retrieval (MIR), prominent tasks -- including
pitch-tracking, source-separation, super-resolution, and synthesis -- typically
call for specialised methods, despite their similarities. Conditional
Generative Adversarial Networks (cGANs) have been shown to be highly versatile
in learning general image-to-image translations, but have not yet been adapted
across MIR. In this work, we present an end-to-end supervisable architecture to
perform all aforementioned audio tasks, consisting of a WaveNet synthesiser
conditioned on the output of a jointly-trained cGAN spectrogram translator. In
doing so, we demonstrate the potential of such flexible techniques to unify MIR
tasks, promote efficient transfer learning, and converge research to the
improvement of powerful, general methods. Finally, to the best of our
knowledge, we present the first application of GANs to guided instrument
synthesis.