pwc-archive/papers-with-abstracts huggingface dataset:
Exploiting Partially Annotated Data for Temporal Relation Extraction
Authors: Qiang Ning, Zhongzhi Yu, Chuchu Fan, Dan Roth
Date: 2018-04-18 00:00:00
Tasks: Relation, Relation Extraction, Temporal Relation Extraction

Annotating temporal relations (TempRel) between events described in natural
language is known to be labor intensive, partly because the total number of
TempRels is quadratic in the number of events. As a result, only a small number
of documents are typically annotated, limiting the coverage of various
lexical/semantic phenomena. In order to improve existing approaches, one
possibility is to make use of the readily available, partially annotated data
(P as in partial) that cover more documents. However, missing annotations in P
are known to hurt, rather than help, existing systems. This work is a case
study in exploring various usages of P for TempRel extraction. Results show
that despite missing annotations, P is still a useful supervision signal for
this task within a constrained bootstrapping learning framework. The system
described in this system is publicly available.
Object Tracking in Satellite Videos Based on a Multi-Frame Optical Flow Tracker
Authors: Bo Du, Shihan Cai, Chen Wu, Liangpei Zhang, DaCheng Tao
Date: 2018-04-25 00:00:00
Tasks: Object, Object Tracking, Optical Flow Estimation

Object tracking is a hot topic in computer vision. Thanks to the booming of
the very high resolution (VHR) remote sensing techniques, it is now possible to
track targets of interests in satellite videos. However, since the targets in
the satellite videos are usually too small compared with the entire image, and
too similar with the background, most state-of-the-art algorithms failed to
track the target in satellite videos with a satisfactory accuracy. Due to the
fact that optical flow shows the great potential to detect even the slight
movement of the targets, we proposed a multi-frame optical flow tracker (MOFT)
for object tracking in satellite videos. The Lucas-Kanade optical flow method
was fused with the HSV color system and integral image to track the targets in
the satellite videos, while multi-frame difference method was utilized in the
optical flow tracker for a better interpretation. The experiments with three
VHR remote sensing satellite video datasets indicate that compared with
state-of-the-art object tracking algorithms, the proposed method can track the
target more accurately.
Deep Neural Network Based Subspace Learning of Robotic Manipulator Workspace Mapping
Authors: Peiyuan Liao
Date: 2018-04-24 00:00:00

The manipulator workspace mapping is an important problem in robotics and has
attracted significant attention in the community. However, most of the
pre-existing algorithms have expensive time complexity due to the reliance on
sophisticated kinematic equations. To solve this problem, this paper introduces
subspace learning (SL), a variant of subspace embedding, where a set of robot
and scope parameters is mapped to the corresponding workspace by a deep neural
network (DNN). Trained on a large dataset of around $\mathbf{6\times 10^4}$
samples obtained from a MATLAB$^\circledR$ implementation of a classical method
and sampling of designed uniform distributions, the experiments demonstrate
that the embedding significantly reduces run-time from $\mathbf{5.23 \times
10^3}$ s of traditional discretization method to $\mathbf{0.224}$ s, with high
accuracies (average F-measure is $\mathbf{0.9665}$ with batch gradient descent
and resilient backpropagation).
Robust Anomaly-Based Ship Proposals Detection from Pan-sharpened High-Resolution Satellite Image
Authors: Viet Hung Luu, Nguyen Hoang Hoa Luong, Quang Hung Bui, Thi Nhat Thanh Nguyen
Date: 2018-04-25 00:00:00
Tasks: Quantization

Pre-screening of ship proposals is now employed by top ship detectors to
avoid exhaustive search across image. In very high resolution (VHR) optical
image, ships appeared as a cluster of abnormal bright pixels in open sea
clutter (noise-like background). Anomaly-based detector utilizing Panchromatic
(PAN) data has been widely used in many researches to detect ships, however,
still facing two main drawbacks: 1) detection rate tend to be low particularly
when a ship is low contrast and 2) these models require a high manual
configuration to select a threshold value best separate ships from sea surface
background. This paper aims at further investigation of anomaly-based model to
solve those issues. First, pan-sharpened Multi Spectral (MS) data is
incorporated together with PAN to enhance ship discrimination. Second, we
propose an improved anomaly-based model combining both global intensity anomaly
and local texture anomaly map. Regarding noise appeared due to the present of
sea clutter and because of pan-sharpen process, texture abnormality suppression
term based on quantization theory is introduced. Experimental results on
VNREDSat-1 VHR optical satellite images suggest that the pan-sharpened
near-infrared (P-NIR) band can improve discrimination of ships from surrounding
waters. Compared to state-of-the-art anomaly-based detectors, our proposed
anomaly-based model on the combination of PAN and P-NIR data cannot only
achieved highest ship detection's recall rate (91.14% and 45.9% on
high-contrast and low-contrast dataset respectively) but also robust to
different automatic threshold selection techniques.
Hierarchical RNN for Information Extraction from Lawsuit Documents
Authors: Xi Rao, Zhenxing Ke
Date: 2018-04-25 00:00:00

Every lawsuit document contains the information about the party's claim,
court's analysis, decision and others, and all of this information are helpful
to understand the case better and predict the judge's decision on similar case
in the future. However, the extraction of these information from the document
is difficult because the language is too complicated and sentences varied at
length. We treat this problem as a task of sequence labeling, and this paper
presents the first research to extract relevant information from the civil
lawsuit document in China with the hierarchical RNN framework.
Real-Time Inference of User Types to Assist with More Inclusive Social Media Activism Campaigns
Authors: Habib Karbasian, Hemant Purohit, Rajat Handa, Aqdas Malik, Aditya Johri
Date: 2018-04-25 00:00:00

Social media provides a mechanism for people to engage with social causes
across a range of issues. It also provides a strategic tool to those looking to
advance a cause to exchange, promote or publicize their ideas. In such
instances, AI can be either an asset if used appropriately or a barrier. One of
the key issues for a workforce diversity campaign is to understand in real-time
who is participating - specifically, whether the participants are individuals
or organizations, and in case of individuals, whether they are male or female.
In this paper, we present a study to demonstrate a case for AI for social good
that develops a model to infer in real-time the different user types
participating in a cause-driven hashtag campaign on Twitter,
ILookLikeAnEngineer (ILLAE). A generic framework is devised to classify a
Twitter user into three classes: organization, male and female in a real-time
manner. The framework is tested against two datasets (ILLAE and a general
dataset) and outperforms the baseline binary classifiers for categorizing
organization/individual and male/female. The proposed model can be applied to
future social cause-driven campaigns to get real-time insights on the
macro-level social behavior of participants.
Gender Bias in Coreference Resolution
Authors: Rachel Rudinger, Jason Naradowsky, Brian Leonard, Benjamin Van Durme
Date: 2018-04-25 00:00:00
Proceeding: NAACL 2018 6
Tasks: coreference-resolution, Coreference Resolution

We present an empirical study of gender bias in coreference resolution
systems. We first introduce a novel, Winograd schema-style set of minimal pair
sentences that differ only by pronoun gender. With these "Winogender schemas,"
we evaluate and confirm systematic gender bias in three publicly-available
coreference resolution systems, and correlate this bias with real-world and
textual gender statistics.
Driver Gaze Zone Estimation using Convolutional Neural Networks: A General Framework and Ablative Analysis
Authors: Sourabh Vora, Akshay Rangesh, Mohan M. Trivedi
Date: 2018-02-08 00:00:00
Tasks: Autonomous Vehicles

Driver gaze has been shown to be an excellent surrogate for driver attention
in intelligent vehicles. With the recent surge of highly autonomous vehicles,
driver gaze can be useful for determining the handoff time to a human driver.
While there has been significant improvement in personalized driver gaze zone
estimation systems, a generalized system which is invariant to different
subjects, perspectives and scales is still lacking. We take a step towards this
generalized system using Convolutional Neural Networks (CNNs). We finetune 4
popular CNN architectures for this task, and provide extensive comparisons of
their outputs. We additionally experiment with different input image patches,
and also examine how image size affects performance. For training and testing
the networks, we collect a large naturalistic driving dataset comprising of 11
long drives, driven by 10 subjects in two different cars. Our best performing
model achieves an accuracy of 95.18% during cross-subject testing,
outperforming current state of the art techniques for this task. Finally, we
evaluate our best performing model on the publicly available Columbia Gaze
Dataset comprising of images from 56 subjects with varying head pose and gaze
directions. Without any training, our model successfully encodes the different
gaze directions on this diverse dataset, demonstrating good generalization
capabilities.
Seq2Seq-Vis: A Visual Debugging Tool for Sequence-to-Sequence Models
Authors: Hendrik Strobelt, Sebastian Gehrmann, Michael Behrisch, Adam Perer, Hanspeter Pfister, Alexander M. Rush
Date: 2018-04-25 00:00:00
Tasks: Translation

Neural Sequence-to-Sequence models have proven to be accurate and robust for
many sequence prediction tasks, and have become the standard approach for
automatic translation of text. The models work in a five stage blackbox process
that involves encoding a source sequence to a vector space and then decoding
out to a new target sequence. This process is now standard, but like many deep
learning methods remains quite difficult to understand or debug. In this work,
we present a visual analysis tool that allows interaction with a trained
sequence-to-sequence model through each stage of the translation process. The
aim is to identify which patterns have been learned and to detect model errors.
We demonstrate the utility of our tool through several real-world large-scale
sequence-to-sequence use cases.
RGB-D-based Human Motion Recognition with Deep Learning: A Survey
Authors: Pichao Wang, Wanqing Li, Philip Ogunbona, Jun Wan, Sergio Escalera
Date: 2017-10-31 00:00:00
Tasks: Deep Learning

Human motion recognition is one of the most important branches of
human-centered research activities. In recent years, motion recognition based
on RGB-D data has attracted much attention. Along with the development in
artificial intelligence, deep learning techniques have gained remarkable
success in computer vision. In particular, convolutional neural networks (CNN)
have achieved great success for image-based tasks, and recurrent neural
networks (RNN) are renowned for sequence-based problems. Specifically, deep
learning methods based on the CNN and RNN architectures have been adopted for
motion recognition using RGB-D data. In this paper, a detailed overview of
recent advances in RGB-D-based motion recognition is presented. The reviewed
methods are broadly categorized into four groups, depending on the modality
adopted for recognition: RGB-based, depth-based, skeleton-based and
RGB+D-based. As a survey focused on the application of deep learning to
RGB-D-based motion recognition, we explicitly discuss the advantages and
limitations of existing techniques. Particularly, we highlighted the methods of
encoding spatial-temporal-structural information inherent in video sequence,
and discuss potential directions for future research.
Accelerated Reinforcement Learning
Authors: K. Lakshmanan
Date: 2017-10-23 00:00:00
Tasks: Policy Gradient Methods, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling, Stochastic Optimization

Policy gradient methods are widely used in reinforcement learning algorithms
to search for better policies in the parameterized policy space. They do
gradient search in the policy space and are known to converge very slowly.
Nesterov developed an accelerated gradient search algorithm for convex
optimization problems. This has been recently extended for non-convex and also
stochastic optimization. We use Nesterov's acceleration for policy gradient
search in the well-known actor-critic algorithm and show the convergence using
ODE method. We tested this algorithm on a scheduling problem. Here an incoming
job is scheduled into one of the four queues based on the queue lengths. We see
from experimental results that algorithm using Nesterov's acceleration has
significantly better performance compared to algorithm which do not use
acceleration. To the best of our knowledge this is the first time Nesterov's
acceleration has been used with actor-critic algorithm.
A Closer Look at Weak Label Learning for Audio Events
Authors: Ankit Shah, Anurag Kumar, Alexander G. Hauptmann, Bhiksha Raj
Date: 2018-04-24 00:00:00
Tasks: Audio Classification, Event Detection, Sound Event Detection, Weakly-supervised Learning

Audio content analysis in terms of sound events is an important research
problem for a variety of applications. Recently, the development of weak
labeling approaches for audio or sound event detection (AED) and availability
of large scale weakly labeled dataset have finally opened up the possibility of
large scale AED. However, a deeper understanding of how weak labels affect the
learning for sound events is still missing from literature. In this work, we
first describe a CNN based approach for weakly supervised training of audio
events. The approach follows some basic design principle desirable in a
learning method relying on weakly labeled audio. We then describe important
characteristics, which naturally arise in weakly supervised learning of sound
events. We show how these aspects of weak labels affect the generalization of
models. More specifically, we study how characteristics such as label density
and corruption of labels affects weakly supervised training for audio events.
We also study the feasibility of directly obtaining weak labeled data from the
web without any manual label and compare it with a dataset which has been
manually labeled. The analysis and understanding of these factors should be
taken into picture in the development of future weak label learning methods.
Audioset, a large scale weakly labeled dataset for sound events is used in our
experiments.
On the Properties of MVR Chain Graphs
Authors: Mohammad Ali Javidian, Marco Valtorta
Date: 2018-03-09 00:00:00

Depending on the interpretation of the type of edges, a chain graph can
represent different relations between variables and thereby independence
models. Three interpretations, known by the acronyms LWF, MVR, and AMP, are
prevalent. Multivariate regression chain graphs (MVR CGs) were introduced by
Cox and Wermuth in 1993. We review Markov properties for MVR chain graphs and
propose an alternative global and local Markov property for them. Except for
pairwise Markov properties, we show that for MVR chain graphs all Markov
properties in the literature are equivalent for semi-graphoids. We derive a new
factorization formula for MVR chain graphs which is more explicit than and
different from the proposed factorizations for MVR chain graphs in the
literature. Finally, we provide a summary table comparing different features of
LWF, AMP, and MVR chain graphs.
Smart Surveillance as an Edge Network Service: from Harr-Cascade, SVM to a Lightweight CNN
Authors: Seyed Yahya Nikouei, Yu Chen, Sejun Song, Ronghua Xu, Baek-Young Choi, Timothy R. Faughnan
Date: 2018-04-24 00:00:00
Tasks: Cloud Computing, Edge-computing, Human Detection, object-detection, Object Detection

Edge computing efficiently extends the realm of information technology beyond
the boundary defined by cloud computing paradigm. Performing computation near
the source and destination, edge computing is promising to address the
challenges in many delay-sensitive applications, like real-time human
surveillance. Leveraging the ubiquitously connected cameras and smart mobile
devices, it enables video analytics at the edge. In recent years, many smart
video surveillance approaches are proposed for object detection and tracking by
using Artificial Intelligence (AI) and Machine Learning (ML) algorithms. This
work explores the feasibility of two popular human-objects detection schemes,
Harr-Cascade and HOG feature extraction and SVM classifier, at the edge and
introduces a lightweight Convolutional Neural Network (L-CNN) leveraging the
depthwise separable convolution for less computation, for human detection.
Single Board computers (SBC) are used as edge devices for tests and algorithms
are validated using real-world campus surveillance video streams and open data
sets. The experimental results are promising that the final algorithm is able
to track humans with a decent accuracy at a resource consumption affordable by
edge devices in real-time manner.
Opening the black box of neural nets: case studies in stop/top discrimination
Authors: Thomas Roxlo, Matthew Reece
Date: 2018-04-24 00:00:00
Tasks: General Classification

We introduce techniques for exploring the functionality of a neural network
and extracting simple, human-readable approximations to its performance. By
performing gradient ascent on the input space of the network, we are able to
produce large populations of artificial events which strongly excite a given
classifier. By studying the populations of these events, we then directly
produce what are essentially contour maps of the network's classification
function. Combined with a suite of tools for identifying the input dimensions
deemed most important by the network, we can utilize these maps to efficiently
interpret the dominant criteria by which the network makes its classification.
  As a test case, we study networks trained to discriminate supersymmetric stop
production in the dilepton channel from Standard Model backgrounds. In the case
of a heavy stop decaying to a light neutralino, we find individual neurons with
large mutual information with $m_{T2}^{\ell\ell}$, a human-designed variable
for optimizing the analysis. The network selects events with significant
missing $p_T$ oriented azimuthally away from both leptons, efficiently
rejecting $t\overline{t}$ background. In the case of a light stop with
three-body decays to $Wb{\widetilde \chi}$ and little phase space, we find
neurons that smoothly interpolate between a similar top-rejection strategy and
an ISR-tagging strategy allowing for more missing momentum. We also find that a
neural network trained on a stealth stop parameter point learns novel angular
correlations.
Real-Time Human Detection as an Edge Service Enabled by a Lightweight CNN
Authors: Seyed Yahya Nikouei, Yu Chen, Sejun Song, Ronghua Xu, Baek-Young Choi, Timothy R. Faughnan
Date: 2018-04-24 00:00:00
Tasks: Decision Making, Edge-computing, Human Detection, object-detection, Object Detection, Raspberry Pi 3

Edge computing allows more computing tasks to take place on the decentralized
nodes at the edge of networks. Today many delay sensitive, mission-critical
applications can leverage these edge devices to reduce the time delay or even
to enable real time, online decision making thanks to their onsite presence.
Human objects detection, behavior recognition and prediction in smart
surveillance fall into that category, where a transition of a huge volume of
video streaming data can take valuable time and place heavy pressure on
communication networks. It is widely recognized that video processing and
object detection are computing intensive and too expensive to be handled by
resource limited edge devices. Inspired by the depthwise separable convolution
and Single Shot Multi-Box Detector (SSD), a lightweight Convolutional Neural
Network (LCNN) is introduced in this paper. By narrowing down the classifier's
searching space to focus on human objects in surveillance video frames, the
proposed LCNN algorithm is able to detect pedestrians with an affordable
computation workload to an edge device. A prototype has been implemented on an
edge node (Raspberry PI 3) using openCV libraries, and satisfactory performance
is achieved using real world surveillance video streams. The experimental study
has validated the design of LCNN and shown it is a promising approach to
computing intensive applications at the edge.
MR fingerprinting Deep RecOnstruction NEtwork (DRONE)
Authors: Ouri Cohen, Bo Zhu, Matthew S. Rosen
Date: 2017-10-15 00:00:00
Tasks: Template Matching

PURPOSE: Demonstrate a novel fast method for reconstruction of
multi-dimensional MR Fingerprinting (MRF) data using Deep Learning methods.
  METHODS: A neural network (NN) is defined using the TensorFlow framework and
trained on simulated MRF data computed using the Bloch equations. The accuracy
of the NN reconstruction of noisy data is compared to conventional MRF template
matching as a function of training data size, and quantified in a both
simulated numerical brain phantom data and acquired data from the ISMRM/NIST
phantom. The utility of the method is demonstrated in a healthy subject in vivo
at 1.5 T.
  RESULTS: Network training required 10 minutes and once trained, data
reconstruction required approximately 10 ms. Reconstruction of simulated brain
data using the NN resulted in a root-mean-square error (RMSE) of 3.5 ms for T1
and 7.8 ms for T2. The RMSE for the NN trained on sparse dictionaries was
approximately 6 fold lower for T1 and 2 fold lower for T2 than conventional MRF
dot-product dictionary matching on the same dictionaries. Phantom measurements
yielded good agreement (R2=0.99) between the T1 and T2 estimated by the NN and
reference values from the ISMRM/NIST phantom.
  CONCLUSION: Reconstruction of MRF data with a NN is accurate, 300 fold faster
and more robust to noise and undersampling than conventional MRF dictionary
matching.
Large Scale Automated Reading of Frontal and Lateral Chest X-Rays using Dual Convolutional Neural Networks
Authors: Jonathan Rubin, Deepan Sanghavi, Claire Zhao, Kathy Lee, Ashequl Qadir, Minnan Xu-Wilson
Date: 2018-04-20 00:00:00

The MIMIC-CXR dataset is (to date) the largest released chest x-ray dataset
consisting of 473,064 chest x-rays and 206,574 radiology reports collected from
63,478 patients. We present the results of training and evaluating a collection
of deep convolutional neural networks on this dataset to recognize multiple
common thorax diseases. To the best of our knowledge, this is the first work
that trains CNNs for this task on such a large collection of chest x-ray
images, which is over four times the size of the largest previously released
chest x-ray corpus (ChestX-Ray14). We describe and evaluate individual CNN
models trained on frontal and lateral CXR view types. In addition, we present a
novel DualNet architecture that emulates routine clinical practice by
simultaneously processing both frontal and lateral CXR images obtained from a
radiological exam. Our DualNet architecture shows improved performance in
recognizing findings in CXR images when compared to applying separate baseline
frontal and lateral classifiers.