pwc-archive/papers-with-abstracts huggingface dataset:
An Elementary Analysis of the Probability That a Binomial Random Variable Exceeds Its Expectation
Authors: Benjamin Doerr
Date: 2017-12-01 00:00:00

We give an elementary proof of the fact that a binomial random variable $X$
with parameters $n$ and $0.29/n \le p < 1$ with probability at least $1/4$
strictly exceeds its expectation. We also show that for $1/n \le p < 1 - 1/n$,
$X$ exceeds its expectation by more than one with probability at least
$0.0370$. Both probabilities approach $1/2$ when $np$ and $n(1-p)$ tend to
infinity.
Effective Neural Solution for Multi-Criteria Word Segmentation
Authors: Han He, Lei Wu, Hua Yan, Zhimin Gao, Yi Feng, George Townsend
Date: 2017-12-07 00:00:00
Tasks: Chinese Word Segmentation, Sentence

We present a simple yet elegant solution to train a single joint model on
multi-criteria corpora for Chinese Word Segmentation (CWS). Our novel design
requires no private layers in model architecture, instead, introduces two
artificial tokens at the beginning and ending of input sentence to specify the
required target criteria. The rest of the model including Long Short-Term
Memory (LSTM) layer and Conditional Random Fields (CRFs) layer remains
unchanged and is shared across all datasets, keeping the size of parameter
collection minimal and constant. On Bakeoff 2005 and Bakeoff 2008 datasets, our
innovative design has surpassed both single-criterion and multi-criteria
state-of-the-art learning results. To the best knowledge, our design is the
first one that has achieved the latest high performance on such large scale
datasets. Source codes and corpora of this paper are available on GitHub.
Dual Long Short-Term Memory Networks for Sub-Character Representation Learning
Authors: Han He, Lei Wu, Xiaokun Yang, Hua Yan, Zhimin Gao, Yi Feng, George Townsend
Date: 2017-12-23 00:00:00
Tasks: Chinese Word Segmentation, Representation Learning, Segmentation

Characters have commonly been regarded as the minimal processing unit in
Natural Language Processing (NLP). But many non-latin languages have
hieroglyphic writing systems, involving a big alphabet with thousands or
millions of characters. Each character is composed of even smaller parts, which
are often ignored by the previous work. In this paper, we propose a novel
architecture employing two stacked Long Short-Term Memory Networks (LSTMs) to
learn sub-character level representation and capture deeper level of semantic
meanings. To build a concrete study and substantiate the efficiency of our
neural architecture, we take Chinese Word Segmentation as a research case
example. Among those languages, Chinese is a typical case, for which every
character contains several components called radicals. Our networks employ a
shared radical level embedding to solve both Simplified and Traditional Chinese
Word Segmentation, without extra Traditional to Simplified Chinese conversion,
in such a highly end-to-end way the word segmentation can be significantly
simplified compared to the previous work. Radical level embeddings can also
capture deeper semantic meaning below character level and improve the system
performance of learning. By tying radical and character embeddings together,
the parameter count is reduced whereas semantic knowledge is shared and
transferred between two levels, boosting the performance largely. On 3 out of 4
Bakeoff 2005 datasets, our method surpassed state-of-the-art results by up to
0.4%. Our results are reproducible, source codes and corpora are available on
GitHub.
Semantic Segmentation via Highly Fused Convolutional Network with Multiple Soft Cost Functions
Authors: Tao Yang, Yan Wu, Junqiao Zhao, Linting Guan
Date: 2018-01-04 00:00:00
Tasks: Feature Upsampling, Image Segmentation, Semantic Segmentation

Semantic image segmentation is one of the most challenged tasks in computer
vision. In this paper, we propose a highly fused convolutional network, which
consists of three parts: feature downsampling, combined feature upsampling and
multiple predictions. We adopt a strategy of multiple steps of upsampling and
combined feature maps in pooling layers with its corresponding unpooling
layers. Then we bring out multiple pre-outputs, each pre-output is generated
from an unpooling layer by one-step upsampling. Finally, we concatenate these
pre-outputs to get the final output. As a result, our proposed network makes
highly use of the feature information by fusing and reusing feature maps. In
addition, when training our model, we add multiple soft cost functions on
pre-outputs and final outputs. In this way, we can reduce the loss reduction
when the loss is back propagated. We evaluate our model on three major
segmentation datasets: CamVid, PASCAL VOC and ADE20K. We achieve a
state-of-the-art performance on CamVid dataset, as well as considerable
improvements on PASCAL VOC dataset and ADE20K dataset
Text Extraction and Retrieval from Smartphone Screenshots: Building a Repository for Life in Media
Authors: Agnese Chiatti, Mu Jung Cho, Anupriya Gagneja, Xiao Yang, Miriam Brinberg, Katie Roehrick, Sagnik Ray Choudhury, Nilam Ram, Byron Reeves, C. Lee Giles
Date: 2018-01-04 00:00:00
Tasks: Image Retrieval, Optical Character Recognition, Optical Character Recognition (OCR), Retrieval

Daily engagement in life experiences is increasingly interwoven with mobile
device use. Screen capture at the scale of seconds is being used in behavioral
studies and to implement "just-in-time" health interventions. The increasing
psychological breadth of digital information will continue to make the actual
screens that people view a preferred if not required source of data about life
experiences. Effective and efficient Information Extraction and Retrieval from
digital screenshots is a crucial prerequisite to successful use of screen data.
In this paper, we present the experimental workflow we exploited to: (i)
pre-process a unique collection of screen captures, (ii) extract unstructured
text embedded in the images, (iii) organize image text and metadata based on a
structured schema, (iv) index the resulting document collection, and (v) allow
for Image Retrieval through a dedicated vertical search engine application. The
adopted procedure integrates different open source libraries for traditional
image processing, Optical Character Recognition (OCR), and Image Retrieval. Our
aim is to assess whether and how state-of-the-art methodologies can be applied
to this novel data set. We show how combining OpenCV-based pre-processing
modules with a Long short-term memory (LSTM) based release of Tesseract OCR,
without ad hoc training, led to a 74% character-level accuracy of the extracted
text. Further, we used the processed repository as baseline for a dedicated
Image Retrieval system, for the immediate use and application for behavioral
and prevention scientists. We discuss issues of Text Information Extraction and
Retrieval that are particular to the screenshot image case and suggest
important future work.
PixelLink: Detecting Scene Text via Instance Segmentation
Authors: Dan Deng, Haifeng Liu, Xuelong. Li, Deng Cai
Date: 2018-01-04 00:00:00
Tasks: Instance Segmentation, regression, Scene Text Detection, Segmentation, Semantic Segmentation, Text Classification, Text Detection

Most state-of-the-art scene text detection algorithms are deep learning based
methods that depend on bounding box regression and perform at least two kinds
of predictions: text/non-text classification and location regression.
Regression plays a key role in the acquisition of bounding boxes in these
methods, but it is not indispensable because text/non-text prediction can also
be considered as a kind of semantic segmentation that contains full location
information in itself. However, text instances in scene images often lie very
close to each other, making them very difficult to separate via semantic
segmentation. Therefore, instance segmentation is needed to address this
problem. In this paper, PixelLink, a novel scene text detection algorithm based
on instance segmentation, is proposed. Text instances are first segmented out
by linking pixels within the same instance together. Text bounding boxes are
then extracted directly from the segmentation result without location
regression. Experiments show that, compared with regression-based methods,
PixelLink can achieve better or comparable performance on several benchmarks,
while requiring many fewer training iterations and less training data.
Learning automata based SVM for intrusion detection
Authors: Chong Di
Date: 2018-01-04 00:00:00
Tasks: Dimensionality Reduction, Intrusion Detection

As an indispensable defensive measure of network security, the intrusion
detection is a process of monitoring the events occurring in a computer system
or network and analyzing them for signs of possible incidents. It is a
classifier to judge the event is normal or malicious. The information used for
intrusion detection contains some redundant features which would increase the
difficulty of training the classifier for intrusion detection and increase the
time of making predictions. To simplify the training process and improve the
efficiency of the classifier, it is necessary to remove these dispensable
features. in this paper, we propose a novel LA-SVM scheme to automatically
remove redundant features focusing on intrusion detection. This is the first
application of learning automata for solving dimension reduction problems. The
simulation results indicate that the LA-SVM scheme achieves a higher accuracy
and is more efficient in making predictions compared with traditional SVM.
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Authors: Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine
Date: 2018-01-04 00:00:00
Proceeding: ICML 2018 7
Tasks: Continuous Control, Decision Making, Deep Reinforcement Learning, Omniverse Isaac Gym, OpenAI Gym, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A platform for Applied Reinforcement Learning (Applied RL)
Object segmentation in depth maps with one user click and a synthetically trained fully convolutional network
Authors: Matthieu Grard, Romain Brégier, Florian Sella, Emmanuel Dellandréa, Liming Chen
Date: 2018-01-04 00:00:00
Tasks: Instance Segmentation, Object, object-detection, Object Detection, Scene Understanding, Semantic Segmentation

With more and more household objects built on planned obsolescence and
consumed by a fast-growing population, hazardous waste recycling has become a
critical challenge. Given the large variability of household waste, current
recycling platforms mostly rely on human operators to analyze the scene,
typically composed of many object instances piled up in bulk. Helping them by
robotizing the unitary extraction is a key challenge to speed up this tedious
process. Whereas supervised deep learning has proven very efficient for such
object-level scene understanding, e.g., generic object detection and
segmentation in everyday scenes, it however requires large sets of per-pixel
labeled images, that are hardly available for numerous application contexts,
including industrial robotics. We thus propose a step towards a practical
interactive application for generating an object-oriented robotic grasp,
requiring as inputs only one depth map of the scene and one user click on the
next object to extract. More precisely, we address in this paper the middle
issue of object seg-mentation in top views of piles of bulk objects given a
pixel location, namely seed, provided interactively by a human operator. We
propose a twofold framework for generating edge-driven instance segments.
First, we repurpose a state-of-the-art fully convolutional object contour
detector for seed-based instance segmentation by introducing the notion of
edge-mask duality with a novel patch-free and contour-oriented loss function.
Second, we train one model using only synthetic scenes, instead of manually
labeled training data. Our experimental results show that considering edge-mask
duality for training an encoder-decoder network, as we suggest, outperforms a
state-of-the-art patch-based network in the present application context.
DeepTriage: Exploring the Effectiveness of Deep Learning for Bug Triaging
Authors: Senthil Mani, Anush Sankaran, Rahul Aralikatte
Date: 2018-01-04 00:00:00
Tasks: Deep Learning

For a given software bug report, identifying an appropriate developer who
could potentially fix the bug is the primary task of a bug triaging process. A
bug title (summary) and a detailed description is present in most of the bug
tracking systems. Automatic bug triaging algorithm can be formulated as a
classification problem, with the bug title and description as the input,
mapping it to one of the available developers (classes). The major challenge is
that the bug description usually contains a combination of free unstructured
text, code snippets, and stack trace making the input data noisy. The existing
bag-of-words (BOW) feature models do not consider the syntactical and
sequential word information available in the unstructured text. We propose a
novel bug report representation algorithm using an attention based deep
bidirectional recurrent neural network (DBRNN-A) model that learns a syntactic
and semantic feature from long word sequences in an unsupervised manner.
Instead of BOW features, the DBRNN-A based bug representation is then used for
training the classifier. Using an attention mechanism enables the model to
learn the context representation over a long word sequence, as in a bug report.
To provide a large amount of data to learn the feature learning model, the
unfixed bug reports (~70% bugs in an open source bug tracking system) are
leveraged, which were completely ignored in the previous studies. Another
contribution is to make this research reproducible by making the source code
available and creating a public benchmark dataset of bug reports from three
open source bug tracking system: Google Chromium (383,104 bug reports), Mozilla
Core (314,388 bug reports), and Mozilla Firefox (162,307 bug reports).
Experimentally we compare our approach with BOW model and machine learning
approaches and observe that DBRNN-A provides a higher rank-10 average accuracy.
ICFVR 2017: 3rd International Competition on Finger Vein Recognition
Authors: Yi Zhang, Houjun Huang, Haifeng Zhang, Liao Ni, Wei Xu, Nasir Uddin Ahmed, Md. Shakil Ahmed, Yilun Jin, Yingjie Chen, Jingxuan Wen, Wenxin Li
Date: 2018-01-04 00:00:00
Tasks: Finger Vein Recognition

In recent years, finger vein recognition has become an important sub-field in
biometrics and been applied to real-world applications. The development of
finger vein recognition algorithms heavily depends on large-scale real-world
data sets. In order to motivate research on finger vein recognition, we
released the largest finger vein data set up to now and hold finger vein
recognition competitions based on our data set every year. In 2017,
International Competition on Finger Vein Recognition(ICFVR) is held jointly
with IJCB 2017. 11 teams registered and 10 of them joined the final evaluation.
The winner of this year dramatically improved the EER from 2.64% to 0.483%
compared to the winner of last year. In this paper, we introduce the process
and results of ICFVR 2017 and give insights on development of state-of-art
finger vein recognition algorithms.
Selection Problems in the Presence of Implicit Bias
Authors: Jon Kleinberg, Manish Raghavan
Date: 2018-01-04 00:00:00
Tasks: Decision Making

Over the past two decades, the notion of implicit bias has come to serve as
an important component in our understanding of discrimination in activities
such as hiring, promotion, and school admissions. Research on implicit bias
posits that when people evaluate others -- for example, in a hiring context --
their unconscious biases about membership in particular groups can have an
effect on their decision-making, even when they have no deliberate intention to
discriminate against members of these groups. A growing body of experimental
work has pointed to the effect that implicit bias can have in producing adverse
outcomes.
  Here we propose a theoretical model for studying the effects of implicit bias
on selection decisions, and a way of analyzing possible procedural remedies for
implicit bias within this model. A canonical situation represented by our model
is a hiring setting: a recruiting committee is trying to choose a set of
finalists to interview among the applicants for a job, evaluating these
applicants based on their future potential, but their estimates of potential
are skewed by implicit bias against members of one group. In this model, we
show that measures such as the Rooney Rule, a requirement that at least one of
the finalists be chosen from the affected group, can not only improve the
representation of this affected group, but also lead to higher payoffs in
absolute terms for the organization performing the recruiting. However,
identifying the conditions under which such measures can lead to improved
payoffs involves subtle trade-offs between the extent of the bias and the
underlying distribution of applicant characteristics, leading to novel
theoretical questions about order statistics in the presence of probabilistic
side information.
Deep Learning Reconstruction for 9-View Dual Energy CT Baggage Scanner
Authors: Yoseob Han, Jingu Kang, Jong Chul Ye
Date: 2018-01-04 00:00:00
Tasks: 3D Reconstruction, Computed Tomography (CT), CT Reconstruction, Deep Learning

For homeland and transportation security applications, 2D X-ray explosive
detection system (EDS) have been widely used, but they have limitations in
recognizing 3D shape of the hidden objects. Among various types of 3D computed
tomography (CT) systems to address this issue, this paper is interested in a
stationary CT using fixed X-ray sources and detectors. However, due to the
limited number of projection views, analytic reconstruction algorithms produce
severe streaking artifacts. Inspired by recent success of deep learning
approach for sparse view CT reconstruction, here we propose a novel image and
sinogram domain deep learning architecture for 3D reconstruction from very
sparse view measurement. The algorithm has been tested with the real data from
a prototype 9-view dual energy stationary CT EDS carry-on baggage scanner
developed by GEMSS Medical Systems, Korea, which confirms the superior
reconstruction performance over the existing approaches.
Approximate Ranking from Pairwise Comparisons
Authors: Reinhard Heckel, Max Simchowitz, Kannan Ramchandran, Martin J. Wainwright
Date: 2018-01-04 00:00:00

A common problem in machine learning is to rank a set of n items based on
pairwise comparisons. Here ranking refers to partitioning the items into sets
of pre-specified sizes according to their scores, which includes identification
of the top-k items as the most prominent special case. The score of a given
item is defined as the probability that it beats a randomly chosen other item.
Finding an exact ranking typically requires a prohibitively large number of
comparisons, but in practice, approximate rankings are often adequate.
Accordingly, we study the problem of finding approximate rankings from pairwise
comparisons. We analyze an active ranking algorithm that counts the number of
comparisons won, and decides whether to stop or which pair of items to compare
next, based on confidence intervals computed from the data collected in
previous steps. We show that this algorithm succeeds in recovering approximate
rankings using a number of comparisons that is close to optimal up to
logarithmic factors. We also present numerical results, showing that in
practice, approximation can drastically reduce the number of comparisons
required to estimate a ranking.
Deep Layer Aggregation
Authors: Fisher Yu, Dequan Wang, Evan Shelhamer, Trevor Darrell
Date: 2017-07-20 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image Classification

Visual recognition requires rich representations that span levels from low to
high, scales from small to large, and resolutions from fine to coarse. Even
with the depth of features in a convolutional network, a layer in isolation is
not enough: compounding and aggregating these representations improves
inference of what and where. Architectural efforts are exploring many
dimensions for network backbones, designing deeper or wider architectures, but
how to best aggregate layers and blocks across a network deserves further
attention. Although skip connections have been incorporated to combine layers,
these connections have been "shallow" themselves, and only fuse by simple,
one-step operations. We augment standard architectures with deeper aggregation
to better fuse information across layers. Our deep layer aggregation structures
iteratively and hierarchically merge the feature hierarchy to make networks
with better accuracy and fewer parameters. Experiments across architectures and
tasks show that deep layer aggregation improves recognition and resolution
compared to existing branching and merging schemes. The code is at
https://github.com/ucbdrive/dla.
Crossing Generative Adversarial Networks for Cross-View Person Re-identification
Authors: Chengyuan Zhang, Lin Wu, Yang Wang
Date: 2018-01-04 00:00:00
Tasks: Cross-Modal  Person Re-Identification, Generative Adversarial Network, Person Re-Identification

Person re-identification (\textit{re-id}) refers to matching pedestrians
across disjoint yet non-overlapping camera views. The most effective way to
match these pedestrians undertaking significant visual variations is to seek
reliably invariant features that can describe the person of interest
faithfully. Most of existing methods are presented in a supervised manner to
produce discriminative features by relying on labeled paired images in
correspondence. However, annotating pair-wise images is prohibitively expensive
in labors, and thus not practical in large-scale networked cameras. Moreover,
seeking comparable representations across camera views demands a flexible model
to address the complex distributions of images. In this work, we study the
co-occurrence statistic patterns between pairs of images, and propose to
crossing Generative Adversarial Network (Cross-GAN) for learning a joint
distribution for cross-image representations in a unsupervised manner. Given a
pair of person images, the proposed model consists of the variational
auto-encoder to encode the pair into respective latent variables, a proposed
cross-view alignment to reduce the view disparity, and an adversarial layer to
seek the joint distribution of latent representations. The learned latent
representations are well-aligned to reflect the co-occurrence patterns of
paired images. We empirically evaluate the proposed model against challenging
datasets, and our results show the importance of joint invariant features in
improving matching rates of person re-id with comparison to semi/unsupervised
state-of-the-arts.
Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems
Authors: Maziar Raissi, Paris Perdikaris, George Em. Karniadakis
Date: 2018-01-04 00:00:00

The process of transforming observed data into predictive mathematical models
of the physical world has always been paramount in science and engineering.
Although data is currently being collected at an ever-increasing pace, devising
meaningful models out of such observations in an automated fashion still
remains an open problem. In this work, we put forth a machine learning approach
for identifying nonlinear dynamical systems from data. Specifically, we blend
classical tools from numerical analysis, namely the multi-step time-stepping
schemes, with powerful nonlinear function approximators, namely deep neural
networks, to distill the mechanisms that govern the evolution of a given
data-set. We test the effectiveness of our approach for several benchmark
problems involving the identification of complex, nonlinear and chaotic
dynamics, and we demonstrate how this allows us to accurately learn the
dynamics, forecast future states, and identify basins of attraction. In
particular, we study the Lorenz system, the fluid flow behind a cylinder, the
Hopf bifurcation, and the Glycoltic oscillator model as an example of
complicated nonlinear dynamics typical of biological systems.