pwc-archive/papers-with-abstracts huggingface dataset:
Towards Efficient and Unbiased Implementation of Lipschitz Continuity in GANs
Authors: Zhiming Zhou, Jian Shen, Yuxuan Song, Wei-Nan Zhang, Yong Yu
Date: 2019-04-02 00:00:00

Lipschitz continuity recently becomes popular in generative adversarial
networks (GANs). It was observed that the Lipschitz regularized discriminator
leads to improved training stability and sample quality. The mainstream
implementations of Lipschitz continuity include gradient penalty and spectral
normalization. In this paper, we demonstrate that gradient penalty introduces
undesired bias, while spectral normalization might be over restrictive. We
accordingly propose a new method which is efficient and unbiased. Our
experiments verify our analysis and show that the proposed method is able to
achieve successful training in various situations where gradient penalty and
spectral normalization fail.
Person Identification with Visual Summary for a Safe Access to a Smart Home
Authors: Shahinur Alam, Mohammed Yeasin
Date: 2019-04-02 00:00:00
Tasks: Change Detection, Computational Efficiency, Person Identification

SafeAccess is an integrated system designed to provide easier and safer
access to a smart home for people with or without disabilities. The system is
designed to enhance safety and promote the independence of people with
disability (i.e., visually impaired). The key functionality of the system
includes the detection and identification of human and generating contextual
visual summary from the real-time video streams obtained from the cameras
placed in strategic locations around the house. In addition, the system
classifies human into groups (i.e. friends/families/caregiver versus
intruders/burglars/unknown). These features allow the user to grant/deny remote
access to the premises or ability to call emergency services. In this paper, we
focus on designing a prototype system for the smart home and building a robust
recognition engine that meets the system criteria and addresses speed,
accuracy, deployment and environmental challenges under a wide variety of
practical and real-life situations. To interact with the system, we implemented
a dialog enabled interface to create a personalized profile using face images
or video of friend/families/caregiver. To improve computational efficiency, we
apply change detection to filter out frames and use Faster-RCNN to detect the
human presence and extract faces using Multitask Cascaded Convolutional
Networks (MTCNN). Subsequently, we apply LBP/FaceNet to identify a person and
groups by matching extracted faces with the profile. SafeAccess sends a visual
summary to the users with an MMS containing a person's name if any match found
or as "Unknown", scene image, facial description, and contextual information.
SafeAccess identifies friends/families/caregiver versus intruders/unknown with
an average F-score 0.97 and generates a visual summary from 10 classes with an
average accuracy of 98.01%.
DeepLight: Learning Illumination for Unconstrained Mobile Mixed Reality
Authors: Chloe LeGendre, Wan-Chun Ma, Graham Fyffe, John Flynn, Laurent Charbonnel, Jay Busch, Paul Debevec
Date: 2019-04-02 00:00:00
Proceeding: CVPR 2019 6
Tasks: Mixed Reality

We present a learning-based method to infer plausible high dynamic range
(HDR), omnidirectional illumination given an unconstrained, low dynamic range
(LDR) image from a mobile phone camera with a limited field of view (FOV). For
training data, we collect videos of various reflective spheres placed within
the camera's FOV, leaving most of the background unoccluded, leveraging that
materials with diverse reflectance functions reveal different lighting cues in
a single exposure. We train a deep neural network to regress from the LDR
background image to HDR lighting by matching the LDR ground truth sphere images
to those rendered with the predicted illumination using image-based relighting,
which is differentiable. Our inference runs at interactive frame rates on a
mobile device, enabling realistic rendering of virtual objects into real scenes
for mobile mixed reality. Training on automatically exposed and white-balanced
videos, we improve the realism of rendered objects compared to the state-of-the
art methods for both indoor and outdoor scenes.
A Multi-Task Approach for Disentangling Syntax and Semantics in Sentence Representations
Authors: Mingda Chen, Qingming Tang, Sam Wiseman, Kevin Gimpel
Date: 2019-04-02 00:00:00
Proceeding: NAACL 2019 6
Tasks: Disentanglement, Semantic Similarity, Semantic Textual Similarity, Sentence

We propose a generative model for a sentence that uses two latent variables,
with one intended to represent the syntax of the sentence and the other to
represent its semantics. We show we can achieve better disentanglement between
semantic and syntactic representations by training with multiple losses,
including losses that exploit aligned paraphrastic sentences and word-order
information. We also investigate the effect of moving from bag-of-words to
recurrent neural network modules. We evaluate our models as well as several
popular pretrained embeddings on standard semantic similarity tasks and novel
syntactic similarity tasks. Empirically, we find that the model with the best
performing syntactic and semantic representations also gives rise to the most
disentangled representations.
Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches
Authors: Shane Storks, Qiaozi Gao, Joyce Y. Chai
Date: 2019-04-02 00:00:00
Tasks: Natural Language Inference

In the NLP community, recent years have seen a surge of research activities that address machines' ability to perform deep language understanding which goes beyond what is explicitly stated in text, rather relying on reasoning and knowledge of the world. Many benchmark tasks and datasets have been created to support the development and evaluation of such natural language inference ability. As these benchmarks become instrumental and a driving force for the NLP research community, this paper aims to provide an overview of recent benchmarks, relevant knowledge resources, and state-of-the-art learning and inference approaches in order to support a better understanding of this growing field.
Curls & Whey: Boosting Black-Box Adversarial Attacks
Authors: Yucheng Shi, Siyu Wang, Yahong Han
Date: 2019-04-02 00:00:00
Proceeding: CVPR 2019 6
Tasks: Adversarial Attack, Diversity

Image classifiers based on deep neural networks suffer from harassment caused
by adversarial examples. Two defects exist in black-box iterative attacks that
generate adversarial examples by incrementally adjusting the noise-adding
direction for each step. On the one hand, existing iterative attacks add noises
monotonically along the direction of gradient ascent, resulting in a lack of
diversity and adaptability of the generated iterative trajectories. On the
other hand, it is trivial to perform adversarial attack by adding excessive
noises, but currently there is no refinement mechanism to squeeze redundant
noises. In this work, we propose Curls & Whey black-box attack to fix the above
two defects. During Curls iteration, by combining gradient ascent and descent,
we `curl' up iterative trajectories to integrate more diversity and
transferability into adversarial examples. Curls iteration also alleviates the
diminishing marginal effect in existing iterative attacks. The Whey
optimization further squeezes the `whey' of noises by exploiting the robustness
of adversarial perturbation. Extensive experiments on Imagenet and
Tiny-Imagenet demonstrate that our approach achieves impressive decrease on
noise magnitude in l2 norm. Curls & Whey attack also shows promising
transferability against ensemble models as well as adversarially trained
models. In addition, we extend our attack to the targeted misclassification,
effectively reducing the difficulty of targeted attacks under black-box
condition.
Learning Mixtures of Smooth Product Distributions: Identifiability and Algorithm
Authors: Nikos Kargas, Nicholas D. Sidiropoulos
Date: 2019-04-02 00:00:00

We study the problem of learning a mixture model of non-parametric product
distributions. The problem of learning a mixture model is that of finding the
component distributions along with the mixing weights using observed samples
generated from the mixture. The problem is well-studied in the parametric
setting, i.e., when the component distributions are members of a parametric
family -- such as Gaussian distributions. In this work, we focus on
multivariate mixtures of non-parametric product distributions and propose a
two-stage approach which recovers the component distributions of the mixture
under a smoothness condition. Our approach builds upon the identifiability
properties of the canonical polyadic (low-rank) decomposition of tensors, in
tandem with Fourier and Shannon-Nyquist sampling staples from signal
processing. We demonstrate the effectiveness of the approach on synthetic and
real datasets.
Thickened 2D Networks for Efficient 3D Medical Image Segmentation
Authors: Qihang Yu, Yingda Xia, Lingxi Xie, Elliot K. Fishman, Alan L. Yuille
Date: 2019-04-02 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

There has been a debate in 3D medical image segmentation on whether to use 2D or 3D networks, where both pipelines have advantages and disadvantages. 2D methods enjoy a low inference time and greater transfer-ability while 3D methods are superior in performance for hard targets requiring contextual information. This paper investigates efficient 3D segmentation from another perspective, which uses 2D networks to mimic 3D segmentation. To compensate the lack of contextual information in 2D manner, we propose to thicken the 2D network inputs by feeding multiple slices as multiple channels into 2D networks and thus 3D contextual information is incorporated. We also put forward to use early-stage multiplexing and slice sensitive attention to solve the confusion problem of information loss which occurs when 2D networks face thickened inputs. With this design, we achieve a higher performance while maintaining a lower inference latency on a few abdominal organs from CT scans, in particular when the organ has a peculiar 3D shape and thus strongly requires contextual information, demonstrating our method's effectiveness and ability in capturing 3D information. We also point out that "thickened" 2D inputs pave a new method of 3D segmentation, and look forward to more efforts in this direction. Experiments on segmenting a few abdominal targets in particular blood vessels which require strong 3D contexts demonstrate the advantages of our approach.
Surgical Gesture Recognition with Optical Flow only
Authors: Duygu Sarikaya, Pierre Jannin
Date: 2019-04-01 00:00:00
Tasks: Gesture Recognition, Optical Flow Estimation, Surgical Gesture Recognition

In this paper, we address the open research problem of surgical gesture recognition using motion cues from video data only. We adapt Optical flow ConvNets initially proposed by Simonyan et al.. While Simonyan uses both RGB frames and dense optical flow, we use only dense optical flow representations as input to emphasize the role of motion in surgical gesture recognition, and present it as a robust alternative to kinematic data. We also overcome one of the limitations of Optical flow ConvNets by initializing our model with cross modality pre-training. A large number of promising studies that address surgical gesture recognition highly rely on kinematic data which requires additional recording devices. To our knowledge, this is the first paper that addresses surgical gesture recognition using dense optical flow information only. We achieve competitive results on JIGSAWS dataset, moreover, our model achieves more robust results with less standard deviation, which suggests optical flow information can be used as an alternative to kinematic data for the recognition of surgical gestures.
Generative predecessor models for sample-efficient imitation learning
Authors: Yannick Schroecker, Mel Vecerik, Jonathan Scholz
Date: 2019-04-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Imitation Learning, Robot Manipulation

We propose Generative Predecessor Models for Imitation Learning (GPRIL), a
novel imitation learning algorithm that matches the state-action distribution
to the distribution observed in expert demonstrations, using generative models
to reason probabilistically about alternative histories of demonstrated states.
We show that this approach allows an agent to learn robust policies using only
a small number of expert demonstrations and self-supervised interactions with
the environment. We derive this approach from first principles and compare it
empirically to a state-of-the-art imitation learning method, showing that it
outperforms or matches its performance on two simulated robot manipulation
tasks and demonstrate significantly higher sample efficiency by applying the
algorithm on a real robot.
Benchmarking Approximate Inference Methods for Neural Structured Prediction
Authors: Lifu Tu, Kevin Gimpel
Date: 2019-04-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Benchmarking, Prediction, Structured Prediction

Exact structured inference with neural network scoring functions is computationally challenging but several methods have been proposed for approximating inference. One approach is to perform gradient descent with respect to the output structure directly (Belanger and McCallum, 2016). Another approach, proposed recently, is to train a neural network (an "inference network") to perform inference (Tu and Gimpel, 2018). In this paper, we compare these two families of inference methods on three sequence labeling datasets. We choose sequence labeling because it permits us to use exact inference as a benchmark in terms of speed, accuracy, and search error. Across datasets, we demonstrate that inference networks achieve a better speed/accuracy/search error trade-off than gradient descent, while also being faster than exact inference at similar accuracy levels. We find further benefit by combining inference networks and gradient descent, using the former to provide a warm start for the latter.
Filling Factors of Sunspots in SODISM Images
Authors: Amro F. Alasta, Abdrazag Algamudi, Fatma Almesrati, Mustapha Meftah, Rami Qahwaji
Date: 2019-04-01 00:00:00

Received: 1st December 2018; Accepted: 18th February 2019; Published: 1st
April 2019 Abstract: The calculated filling factors (FFs) for a feature reflect
the fraction of the solar disc covered by that feature, and the assignment of
reference synthetic spectra. In this paper, the FFs, specified as a function of
radial position on the solar disc, are computed for each image in a tabular
form. The filling factor (FF) is an important parameter and is defined as the
fraction of area in a pixel covered with the magnetic field, whereas the rest
of the area in the pixel is field-free. However, this does not provide
extensive information about the experiments conducted on tens or hundreds of
such images. This is the first time that filling factors for SODISM images have
been catalogued in tabular formation. This paper presents a new method that
provides the means to detect sunspots on full-disk solar images recorded by the
Solar Diameter Imager and Surface Mapper (SODISM) on the PICARD satellite. The
method is a totally automated detection process that achieves a sunspot
recognition rate of 97.6%. The number of sunspots detected by this method
strongly agrees with the NOAA catalogue. The sunspot areas calculated by this
method have a 99% correlation with SOHO over the same period, and thus help to
calculate the filling factor for wavelength (W.L.) 607nm.
PAWS: Paraphrase Adversaries from Word Scrambling
Authors: Yuan Zhang, Jason Baldridge, Luheng He
Date: 2019-04-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Paraphrase Identification, Sentence, Translation

Existing paraphrase identification datasets lack sentence pairs that have
high lexical overlap without being paraphrases. Models trained on such data
fail to distinguish pairs like flights from New York to Florida and flights
from Florida to New York. This paper introduces PAWS (Paraphrase Adversaries
from Word Scrambling), a new dataset with 108,463 well-formed paraphrase and
non-paraphrase pairs with high lexical overlap. Challenging pairs are generated
by controlled word swapping and back translation, followed by fluency and
paraphrase judgments by human raters. State-of-the-art models trained on
existing datasets have dismal performance on PAWS (<40% accuracy); however,
including PAWS training data for these models improves their accuracy to 85%
while maintaining performance on existing tasks. In contrast, models that do
not capture non-local contextual information fail even with PAWS training
examples. As such, PAWS provides an effective instrument for driving further
progress on models that better exploit structure, context, and pairwise
comparisons.
Cyberthreat Detection from Twitter using Deep Neural Networks
Authors: Nuno Dionísio, Fernando Alves, Pedro M. Ferreira, Alysson Bessani
Date: 2019-04-01 00:00:00
Tasks: Management, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

To be prepared against cyberattacks, most organizations resort to security
information and event management systems to monitor their infrastructures.
These systems depend on the timeliness and relevance of the latest updates,
patches and threats provided by cyberthreat intelligence feeds. Open source
intelligence platforms, namely social media networks such as Twitter, are
capable of aggregating a vast amount of cybersecurity-related sources. To
process such information streams, we require scalable and efficient tools
capable of identifying and summarizing relevant information for specified
assets. This paper presents the processing pipeline of a novel tool that uses
deep neural networks to process cybersecurity information received from
Twitter. A convolutional neural network identifies tweets containing
security-related information relevant to assets in an IT infrastructure. Then,
a bidirectional long short-term memory network extracts named entities from
these tweets to form a security alert or to fill an indicator of compromise.
The proposed pipeline achieves an average 94% true positive rate and 91% true
negative rate for the classification task and an average F1-score of 92% for
the named entity recognition task, across three case study infrastructures.
ScriptNet: Neural Static Analysis for Malicious JavaScript Detection
Authors: Jack W. Stokes, Rakshit Agrawal, Geoff McDonald, Matthew Hausknecht
Date: 2019-04-01 00:00:00

Malicious scripts are an important computer infection threat vector in the
wild. For web-scale processing, static analysis offers substantial computing
efficiencies. We propose the ScriptNet system for neural malicious JavaScript
detection which is based on static analysis. We use the Convoluted Partitioning
of Long Sequences (CPoLS) model, which processes Javascript files as byte
sequences. Lower layers capture the sequential nature of these byte sequences
while higher layers classify the resulting embedding as malicious or benign.
Unlike previously proposed solutions, our model variants are trained in an
end-to-end fashion allowing discriminative training even for the sequential
processing layers. Evaluating this model on a large corpus of 212,408
JavaScript files indicates that the best performing CPoLS model offers a 97.20%
true positive rate (TPR) for the first 60K byte subsequence at a false positive
rate (FPR) of 0.50%. The best performing CPoLS model significantly outperform
several baseline models.
The Impact of Extraneous Variables on the Performance of Recurrent Neural Network Models in Clinical Tasks
Authors: Eugene Laksana, Melissa Aczon, Long Ho, Cameron Carlin, David Ledbetter, Randall Wetzel
Date: 2019-04-01 00:00:00
Tasks: feature selection, ICU Mortality

Electronic Medical Records (EMR) are a rich source of patient information,
including measurements reflecting physiologic signs and administered therapies.
Identifying which variables are useful in predicting clinical outcomes can be
challenging. Advanced algorithms such as deep neural networks were designed to
process high-dimensional inputs containing variables in their measured form,
thus bypass separate feature selection or engineering steps. We investigated
the effect of extraneous input variables on the predictive performance of
Recurrent Neural Networks (RNN) by including in the input vector extraneous
variables randomly drawn from theoretical and empirical distributions. RNN
models using different input vectors (EMR variables; EMR and extraneous
variables; extraneous variables only) were trained to predict three clinical
outcomes: in-ICU mortality, 72-hour ICU re-admission, and 30-day ICU-free days.
The measured degradations of the RNN's predictive performance with the addition
of extraneous variables to EMR variables were negligible.
HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models
Authors: Sharon Zhou, Mitchell L. Gordon, Ranjay Krishna, Austin Narcomey, Li Fei-Fei, Michael S. Bernstein
Date: 2019-04-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Image Generation, Unconditional Image Generation

Generative models often use human evaluations to measure the perceived quality of their outputs. Automated metrics are noisy indirect proxies, because they rely on heuristics or pretrained embeddings. However, up until now, direct human evaluation strategies have been ad-hoc, neither standardized nor validated. Our work establishes a gold standard human benchmark for generative realism. We construct Human eYe Perceptual Evaluation (HYPE) a human benchmark that is (1) grounded in psychophysics research in perception, (2) reliable across different sets of randomly sampled outputs from a model, (3) able to produce separable model performances, and (4) efficient in cost and time. We introduce two variants: one that measures visual perception under adaptive time constraints to determine the threshold at which a model's outputs appear real (e.g. 250ms), and the other a less expensive variant that measures human error rate on fake and real images sans time constraints. We test HYPE across six state-of-the-art generative adversarial networks and two sampling techniques on conditional and unconditional image generation using four datasets: CelebA, FFHQ, CIFAR-10, and ImageNet. We find that HYPE can track model improvements across training epochs, and we confirm via bootstrap sampling that HYPE rankings are consistent and replicable.
ASSERT: Anti-Spoofing with Squeeze-Excitation and Residual neTworks
Authors: Cheng-I Lai, Nanxin Chen, Jesús Villalba, Najim Dehak
Date: 2019-04-01 00:00:00
Tasks: Feature Engineering, text-to-speech, Text to Speech, Voice Conversion

We present JHU's system submission to the ASVspoof 2019 Challenge:
Anti-Spoofing with Squeeze-Excitation and Residual neTworks (ASSERT).
Anti-spoofing has gathered more and more attention since the inauguration of
the ASVspoof Challenges, and ASVspoof 2019 dedicates to address attacks from
all three major types: text-to-speech, voice conversion, and replay. Built upon
previous research work on Deep Neural Network (DNN), ASSERT is a pipeline for
DNN-based approach to anti-spoofing. ASSERT has four components: feature
engineering, DNN models, network optimization and system combination, where the
DNN models are variants of squeeze-excitation and residual networks. We
conducted an ablation study of the effectiveness of each component on the
ASVspoof 2019 corpus, and experimental results showed that ASSERT obtained more
than 93% and 17% relative improvements over the baseline systems in the two
sub-challenges in ASVspooof 2019, ranking ASSERT one of the top performing
systems. Code and pretrained models will be made publicly available.