pwc-archive/papers-with-abstracts huggingface dataset:
Polarimetric Thermal to Visible Face Verification via Self-Attention Guided Synthesis
Authors: Xing Di, Benjamin S. Riggan, Shuowen Hu, Nathaniel J. Short, Vishal M. Patel
Date: 2019-04-15 00:00:00
Tasks: Face Verification, Generative Adversarial Network, Image Generation

Polarimetric thermal to visible face verification entails matching two images
that contain significant domain differences. Several recent approaches have
attempted to synthesize visible faces from thermal images for cross-modal
matching. In this paper, we take a different approach in which rather than
focusing only on synthesizing visible faces from thermal faces, we also propose
to synthesize thermal faces from visible faces. Our intuition is based on the
fact that thermal images also contain some discriminative information about the
person for verification. Deep features from a pre-trained Convolutional Neural
Network (CNN) are extracted from the original as well as the synthesized
images. These features are then fused to generate a template which is then used
for verification. The proposed synthesis network is based on the self-attention
generative adversarial network (SAGAN) which essentially allows efficient
attention-guided image synthesis. Extensive experiments on the ARL polarimetric
thermal face dataset demonstrate that the proposed method achieves
state-of-the-art performance.
Learning Twitter User Sentiments on Climate Change with Limited Labeled Data
Authors: Allison Koenecke, Jordi Feliu-Fabà
Date: 2019-04-15 00:00:00

While it is well-documented that climate change accepters and deniers have
become increasingly polarized in the United States over time, there has been no
large-scale examination of whether these individuals are prone to changing
their opinions as a result of natural external occurrences. On the
sub-population of Twitter users, we examine whether climate change sentiment
changes in response to five separate natural disasters occurring in the U.S. in
2018. We begin by showing that relevant tweets can be classified with over 75%
accuracy as either accepting or denying climate change when using our
methodology to compensate for limited labeled data; results are robust across
several machine learning models and yield geographic-level results in line with
prior research. We then apply RNNs to conduct a cohort-level analysis showing
that the 2018 hurricanes yielded a statistically significant increase in
average tweet sentiment affirming climate change. However, this effect does not
hold for the 2018 blizzard and wildfires studied, implying that Twitter users'
opinions on climate change are fairly ingrained on this subset of natural
disasters.
Multi-Head Multi-Layer Attention to Deep Language Representations for Grammatical Error Detection
Authors: Masahiro Kaneko, Mamoru Komachi
Date: 2019-04-15 00:00:00
Tasks: Grammatical Error Detection, Sentence

It is known that a deep neural network model pre-trained with large-scale
data greatly improves the accuracy of various tasks, especially when there are
resource constraints. However, the information needed to solve a given task can
vary, and simply using the output of the final layer is not necessarily
sufficient. Moreover, to our knowledge, exploiting large language
representation models to detect grammatical errors has not yet been studied. In
this work, we investigate the effect of utilizing information not only from the
final layer but also from intermediate layers of a pre-trained language
representation model to detect grammatical errors. We propose a multi-head
multi-layer attention model that determines the appropriate layers in
Bidirectional Encoder Representation from Transformers (BERT). The proposed
method achieved the best scores on three datasets for grammatical error
detection tasks, outperforming the current state-of-the-art method by 6.0
points on FCE, 8.2 points on CoNLL14, and 12.2 points on JFLEG in terms of
F_0.5. We also demonstrate that by using multi-head multi-layer attention, our
model can exploit a broader range of information for each token in a sentence
than a model that uses only the final layer's information.
How Widely Can Prediction Models be Generalized? Performance Prediction in Blended Courses
Authors: Niki Gitinabard, Yiqiao Xu, Sarah Heckman, Tiffany Barnes, Collin F. Lynch
Date: 2019-04-15 00:00:00
Tasks: Prediction

Blended courses that mix in-person instruction with online platforms are increasingly popular in secondary education. These tools record a rich amount of data on students' study habits and social interactions. Prior research has shown that these metrics are correlated with students' performance in face to face classes. However, predictive models for blended courses are still limited and have not yet succeeded at early prediction or cross-class predictions even for repeated offerings of the same course. In this work, we use data from two offerings of two different undergraduate courses to train and evaluate predictive models on student performance based upon persistent student characteristics including study habits and social interactions. We analyze the performance of these models on the same offering, on different offerings of the same course, and across courses to see how well they generalize. We also evaluate the models on different segments of the courses to determine how early reliable predictions can be made. This work tells us in part how much data is required to make robust predictions and how cross-class data may be used, or not, to boost model performance. The results of this study will help us better understand how similar the study habits, social activities, and the teamwork styles are across semesters for students in each performance category. These trained models also provide an avenue to improve our existing support platforms to better support struggling students early in the semester with the goal of providing timely intervention.
Characterizing the Variability in Face Recognition Accuracy Relative to Race
Authors: KS Krishnapriya, Kushal Vangara, Michael C. King, Vitor Albiero, Kevin Bowyer
Date: 2019-04-15 00:00:00
Tasks: Face Recognition, MORPH

Many recent news headlines have labeled face recognition technology as biased or racist. We report on a methodical investigation into differences in face recognition accuracy between African-American and Caucasian image cohorts of the MORPH dataset. We find that, for all four matchers considered, the impostor and the genuine distributions are statistically significantly different between cohorts. For a fixed decision threshold, the African-American image cohort has a higher false match rate and a lower false non-match rate. ROC curves compare verification rates at the same false match rate, but the different cohorts achieve the same false match rate at different thresholds. This means that ROC comparisons are not relevant to operational scenarios that use a fixed decision threshold. We show that, for the ResNet matcher, the two cohorts have approximately equal separation of impostor and genuine distributions. Using ICAO compliance as a standard of image quality, we find that the initial image cohorts have unequal rates of good quality images. The ICAO-compliant subsets of the original image cohorts show improved accuracy, with the main effect being to reducing the low-similarity tail of the genuine distributions.
Learning Probabilistic Multi-Modal Actor Models for Vision-Based Robotic Grasping
Authors: Mengyuan Yan, Adrian Li, Mrinal Kalakrishnan, Peter Pastor
Date: 2019-04-15 00:00:00
Tasks: Robotic Grasping

Many previous works approach vision-based robotic grasping by training a
value network that evaluates grasp proposals. These approaches require an
optimization process at run-time to infer the best action from the value
network. As a result, the inference time grows exponentially as the dimension
of action space increases. We propose an alternative method, by directly
training a neural density model to approximate the conditional distribution of
successful grasp poses from the input images. We construct a neural network
that combines Gaussian mixture and normalizing flows, which is able to
represent multi-modal, complex probability distributions. We demonstrate on
both simulation and real robot that the proposed actor model achieves similar
performance compared to the value network using the Cross-Entropy Method (CEM)
for inference, on top-down grasping with a 4 dimensional action space. Our
actor model reduces the inference time by 3 times compared to the
state-of-the-art CEM method. We believe that actor models will play an
important role when scaling up these approaches to higher dimensional action
spaces.
Natural Language Semantics With Pictures: Some Language & Vision Datasets and Potential Uses for Computational Semantics
Authors: David Schlangen
Date: 2019-04-15 00:00:00
Tasks: Relation

Propelling, and propelled by, the "deep learning revolution", recent years
have seen the introduction of ever larger corpora of images annotated with
natural language expressions. We survey some of these corpora, taking a
perspective that reverses the usual directionality, as it were, by viewing the
images as semantic annotation of the natural language expressions. We discuss
datasets that can be derived from the corpora, and tasks of potential interest
for computational semanticists that can be defined on those. In this, we make
use of relations provided by the corpora (namely, the link between expression
and image, and that between two expressions linked to the same image) and
relations that we can add (similarity relations between expressions, or between
images). Specifically, we show that in this way we can create data that can be
used to learn and evaluate lexical and compositional grounded semantics, and we
show that the "linked to same image" relation tracks a semantic implication
relation that is recognisable to annotators even in the absence of the linking
image as evidence. Finally, as an example of possible benefits of this
approach, we show that an exemplar-model-based approach to implication beats a
(simple) distributional space-based one on some derived datasets, while lending
itself to explainability.
A Realistic Dataset and Baseline Temporal Model for Early Drowsiness Detection
Authors: Reza Ghoddoosian, Marnim Galib, Vassilis Athitsos
Date: 2019-04-15 00:00:00

Drowsiness can put lives of many drivers and workers in danger. It is
important to design practical and easy-to-deploy real-world systems to detect
the onset of drowsiness.In this paper, we address early drowsiness detection,
which can provide early alerts and offer subjects ample time to react. We
present a large and public real-life dataset of 60 subjects, with video
segments labeled as alert, low vigilant, or drowsy. This dataset consists of
around 30 hours of video, with contents ranging from subtle signs of drowsiness
to more obvious ones. We also benchmark a temporal model for our dataset, which
has low computational and storage demands. The core of our proposed method is a
Hierarchical Multiscale Long Short-Term Memory (HM-LSTM) network, that is fed
by detected blink features in sequence. Our experiments demonstrate the
relationship between the sequential blink features and drowsiness. In the
experimental results, our baseline method produces higher accuracy than human
judgment.
Tracing Forum Posts to MOOC Content using Topic Analysis
Authors: Alexander William Wong, Ken Wong, Abram Hindle
Date: 2019-04-15 00:00:00
Tasks: Clustering, Topic Models

Massive Open Online Courses are educational programs that are open and
accessible to a large number of people through the internet. To facilitate
learning, MOOC discussion forums exist where students and instructors
communicate questions, answers, and thoughts related to the course.
  The primary objective of this paper is to investigate tracing discussion
forum posts back to course lecture videos and readings using topic analysis. We
utilize both unsupervised and supervised variants of Latent Dirichlet
Allocation (LDA) to extract topics from course material and classify forum
posts. We validate our approach on posts bootstrapped from five Coursera
courses and determine that topic models can be used to map student discussion
posts back to the underlying course lecture or reading. Labeled LDA outperforms
unsupervised Hierarchical Dirichlet Process LDA and base LDA for our
traceability task. This research is useful as it provides an automated approach
for clustering student discussions by course material, enabling instructors to
quickly evaluate student misunderstanding of content and clarify materials
accordingly.
Automatic adaptation of object detectors to new domains using self-training
Authors: Aruni RoyChowdhury, Prithvijit Chakrabarty, Ashish Singh, SouYoung Jin, Huaizu Jiang, Liangliang Cao, Erik Learned-Miller
Date: 2019-04-15 00:00:00
Proceeding: CVPR 2019 6
Tasks: Domain Adaptation, Knowledge Distillation, Pedestrian Detection, Unsupervised Domain Adaptation

This work addresses the unsupervised adaptation of an existing object
detector to a new target domain. We assume that a large number of unlabeled
videos from this domain are readily available. We automatically obtain labels
on the target data by using high-confidence detections from the existing
detector, augmented with hard (misclassified) examples acquired by exploiting
temporal cues using a tracker. These automatically-obtained labels are then
used for re-training the original model. A modified knowledge distillation loss
is proposed, and we investigate several ways of assigning soft-labels to the
training examples from the target domain. Our approach is empirically evaluated
on challenging face and pedestrian detection tasks: a face detector trained on
WIDER-Face, which consists of high-quality images crawled from the web, is
adapted to a large-scale surveillance data set; a pedestrian detector trained
on clear, daytime images from the BDD-100K driving data set is adapted to all
other scenarios such as rainy, foggy, night-time. Our results demonstrate the
usefulness of incorporating hard examples obtained from tracking, the advantage
of using soft-labels via distillation loss versus hard-labels, and show
promising performance as a simple method for unsupervised domain adaptation of
object detectors, with minimal dependence on hyper-parameters.
Fast Inference in Capsule Networks Using Accumulated Routing Coefficients
Authors: Zhen Zhao, Ashley Kleinhans, Gursharan Sandhu, Ishan Patel, K. P. Unnikrishnan
Date: 2019-04-15 00:00:00
Tasks: Object, Rotated MNIST

We present a method for fast inference in Capsule Networks (CapsNets) by
taking advantage of a key insight regarding the routing coefficients that link
capsules between adjacent network layers. Since the routing coefficients are
responsible for assigning object parts to wholes, and an object whole generally
contains similar intra-class and dissimilar inter-class parts, the routing
coefficients tend to form a unique signature for each object class. For fast
inference, a network is first trained in the usual manner using examples from
the training dataset. Afterward, the routing coefficients associated with the
training examples are accumulated offline and used to create a set of "master"
routing coefficients. During inference, these master routing coefficients are
used in place of the dynamically calculated routing coefficients. Our method
effectively replaces the for-loop iterations in the dynamic routing procedure
with a single matrix multiply operation, providing a significant boost in
inference speed. Compared with the dynamic routing procedure, fast inference
decreases the test accuracy for the MNIST, Background MNIST, Fashion MNIST, and
Rotated MNIST datasets by less than 0.5% and by approximately 5% for CIFAR10.
CryptoNN: Training Neural Networks over Encrypted Data
Authors: Runhua Xu, James B. D. Joshi, Chao Li
Date: 2019-04-15 00:00:00
Tasks: BIG-bench Machine Learning, Privacy Preserving

Emerging neural networks based machine learning techniques such as deep
learning and its variants have shown tremendous potential in many application
domains. However, they raise serious privacy concerns due to the risk of
leakage of highly privacy-sensitive data when data collected from users is used
to train neural network models to support predictive tasks. To tackle such
serious privacy concerns, several privacy-preserving approaches have been
proposed in the literature that use either secure multi-party computation (SMC)
or homomorphic encryption (HE) as the underlying mechanisms. However, neither
of these cryptographic approaches provides an efficient solution towards
constructing a privacy-preserving machine learning model, as well as supporting
both the training and inference phases. To tackle the above issue, we propose a
CryptoNN framework that supports training a neural network model over encrypted
data by using the emerging functional encryption scheme instead of SMC or HE.
We also construct a functional encryption scheme for basic arithmetic
computation to support the requirement of the proposed CryptoNN framework. We
present performance evaluation and security analysis of the underlying crypto
scheme and show through our experiments that CryptoNN achieves accuracy that is
similar to those of the baseline neural network models on the MNIST dataset.
RHR-Net: A Residual Hourglass Recurrent Neural Network for Speech Enhancement
Authors: Jalal Abdulbaqi, Yue Gu, Ivan Marsic
Date: 2019-04-15 00:00:00
Tasks: Speech Enhancement

Most current speech enhancement models use spectrogram features that require
an expensive transformation and result in phase information loss. Previous work
has overcome these issues by using convolutional networks to learn long-range
temporal correlations across high-resolution waveforms. These models, however,
are limited by memory-intensive dilated convolution and aliasing artifacts from
upsampling. We introduce an end-to-end fully-recurrent hourglass-shaped neural
network architecture with residual connections for waveform-based
single-channel speech enhancement. Our model can efficiently capture long-range
temporal dependencies by reducing the features resolution without information
loss. Experimental results show that our model outperforms state-of-the-art
approaches in six evaluation metrics.
Latent Code and Text-based Generative Adversarial Networks for Soft-text Generation
Authors: Md. Akmal Haidar, Mehdi Rezagholizadeh, Alan Do-Omri, Ahmad Rashid
Date: 2019-04-15 00:00:00
Proceeding: NAACL 2019 6
Tasks: Text Generation

Text generation with generative adversarial networks (GANs) can be divided
into the text-based and code-based categories according to the type of signals
used for discrimination. In this work, we introduce a novel text-based approach
called Soft-GAN to effectively exploit GAN setup for text generation. We
demonstrate how autoencoders (AEs) can be used for providing a continuous
representation of sentences, which we will refer to as soft-text. This soft
representation will be used in GAN discrimination to synthesize similar
soft-texts. We also propose hybrid latent code and text-based GAN (LATEXT-GAN)
approaches with one or more discriminators, in which a combination of the
latent code and the soft-text is used for GAN discriminations. We perform a
number of subjective and objective experiments on two well-known datasets (SNLI
and Image COCO) to validate our techniques. We discuss the results using
several evaluation metrics and show that the proposed techniques outperform the
traditional GAN-based text-generation methods.
Brain Tumor Segmentation on MRI with Missing Modalities
Authors: Yan Shen, Mingchen Gao
Date: 2019-04-15 00:00:00
Tasks: Brain Tumor Segmentation, Domain Adaptation, Segmentation, Tumor Segmentation

Brain Tumor Segmentation from magnetic resonance imaging (MRI) is a critical
technique for early diagnosis. However, rather than having complete four
modalities as in BraTS dataset, it is common to have missing modalities in
clinical scenarios. We design a brain tumor segmentation algorithm that is
robust to the absence of any modality. Our network includes a
channel-independent encoding path and a feature-fusion decoding path. We use
self-supervised training through channel dropout and also propose a novel
domain adaptation method on feature maps to recover the information from the
missing channel. Our results demonstrate that the quality of the segmentation
depends on which modality is missing. Furthermore, we also discuss and
visualize the contribution of each modality to the segmentation results. Their
contributions are along well with the expert screening routine.
The 1/5-th Rule with Rollbacks: On Self-Adjustment of the Population Size in the $(1+(λ,λ))$ GA
Authors: Anton Bassin, Maxim Buzdalov
Date: 2019-04-15 00:00:00
Tasks: Evolutionary Algorithms

Self-adjustment of parameters can significantly improve the performance of
evolutionary algorithms. A notable example is the $(1+(\lambda,\lambda))$
genetic algorithm, where the adaptation of the population size helps to achieve
the linear runtime on the OneMax problem. However, on problems which interfere
with the assumptions behind the self-adjustment procedure, its usage can lead
to performance degradation compared to static parameter choices. In particular,
the one fifth rule, which guides the adaptation in the example above, is able
to raise the population size too fast on problems which are too far away from
the perfect fitness-distance correlation.
  We propose a modification of the one fifth rule in order to have less
negative impact on the performance in scenarios when the original rule reduces
the performance. Our modification, while still having a good performance on
OneMax, both theoretically and in practice, also shows better results on linear
functions with random weights and on random satisfiable MAX-SAT instances.
A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal MRI
Authors: Hongming Li, Mohamad Habes, David A. Wolk, Yong Fan
Date: 2019-04-15 00:00:00
Tasks: Deep Learning, Prognosis

Introduction: It is challenging at baseline to predict when and which
individuals who meet criteria for mild cognitive impairment (MCI) will
ultimately progress to Alzheimer's disease (AD) dementia. Methods: A deep
learning method is developed and validated based on MRI scans of 2146 subjects
(803 for training and 1343 for validation) to predict MCI subjects' progression
to AD dementia in a time-to-event analysis setting. Results: The deep learning
time-to-event model predicted individual subjects' progression to AD dementia
with a concordance index (C-index) of 0.762 on 439 ADNI testing MCI subjects
with follow-up duration from 6 to 78 months (quartiles: [24, 42, 54]) and a
C-index of 0.781 on 40 AIBL testing MCI subjects with follow-up duration from
18-54 months (quartiles: [18, 36,54]). The predicted progression risk also
clustered individual subjects into subgroups with significant differences in
their progression time to AD dementia (p<0.0002). Improved performance for
predicting progression to AD dementia (C-index=0.864) was obtained when the
deep learning based progression risk was combined with baseline clinical
measures. Conclusion: Our method provides a cost effective and accurate means
for prognosis and potentially to facilitate enrollment in clinical trials with
individuals likely to progress within a specific temporal period.