pwc-archive/papers-with-abstracts huggingface dataset:
Mining Dual Emotion for Fake News Detection
Authors: Xueyao Zhang, Juan Cao, Xirong Li, Qiang Sheng, Lei Zhong, Kai Shu
Date: 2019-03-05 00:00:00
Tasks: Fake News Detection

Emotion plays an important role in detecting fake news online. When leveraging emotional signals, the existing methods focus on exploiting the emotions of news contents that conveyed by the publishers (i.e., publisher emotion). However, fake news often evokes high-arousal or activating emotions of people, so the emotions of news comments aroused in the crowd (i.e., social emotion) should not be ignored. Furthermore, it remains to be explored whether there exists a relationship between publisher emotion and social emotion (i.e., dual emotion), and how the dual emotion appears in fake news. In this paper, we verify that dual emotion is distinctive between fake and real news and propose Dual Emotion Features to represent dual emotion and the relationship between them for fake news detection. Further, we exhibit that our proposed features can be easily plugged into existing fake news detectors as an enhancement. Extensive experiments on three real-world datasets (one in English and the others in Chinese) show that our proposed feature set: 1) outperforms the state-of-the-art task-related emotional features; 2) can be well compatible with existing fake news detectors and effectively improve the performance of detecting fake news.
Multi-Agent Learning in Network Zero-Sum Games is a Hamiltonian System
Authors: James P. Bailey, Georgios Piliouras
Date: 2019-03-05 00:00:00

Zero-sum games are natural, if informal, analogues of closed physical systems
where no energy/utility can enter or exit. This analogy can be extended even
further if we consider zero-sum network (polymatrix) games where multiple
agents interact in a closed economy. Typically, (network) zero-sum games are
studied from the perspective of Nash equilibria. Nevertheless, this comes in
contrast with the way we typically think about closed physical systems, e.g.,
Earth-moon systems which move perpetually along recurrent trajectories of
constant energy.
  We establish a formal and robust connection between multi-agent systems and
Hamiltonian dynamics -- the same dynamics that describe conservative systems in
physics. Specifically, we show that no matter the size, or network structure of
such closed economies, even if agents use different online learning dynamics
from the standard class of Follow-the-Regularized-Leader, they yield
Hamiltonian dynamics. This approach generalizes the known connection to
Hamiltonians for the special case of replicator dynamics in two agent zero-sum
games developed by Hofbauer. Moreover, our results extend beyond zero-sum
settings and provide a type of a Rosetta stone (see e.g. Table 1) that helps to
translate results and techniques between online optimization, convex analysis,
games theory, and physics.
Improve Object Detection by Data Enhancement based on Generative Adversarial Nets
Authors: Wei Jiang, Na Ying
Date: 2019-03-05 00:00:00
Tasks: Object, object-detection, Object Detection

The accuracy of the object detection model depends on whether the anchor
boxes effectively trained. Because of the small number of GT boxes or object
target is invariant in the training phase, cannot effectively train anchor
boxes. Improving detection accuracy by extending the dataset is an effective
way. We propose a data enhancement method based on the foreground-background
separation model. While this model uses a binary image of object target random
perturb original dataset image. Perturbation methods include changing the color
channel of the object, adding salt noise to the object, and enhancing contrast.
The main contribution of this paper is to propose a data enhancement method
based on GAN and improve detection accuracy of DSSD. Results are shown on both
PASCAL VOC2007 and PASCAL VOC2012 dataset. Our model with 321x321 input
achieves 78.7% mAP on the VOC2007 test, 76.6% mAP on the VOC2012 test.
L 1-norm double backpropagation adversarial defense
Authors: Ismaïla Seck, Gaëlle Loosli, Stephane Canu
Date: 2019-03-05 00:00:00
Tasks: Adversarial Defense

Adversarial examples are a challenging open problem for deep neural networks.
We propose in this paper to add a penalization term that forces the decision
function to be at in some regions of the input space, such that it becomes, at
least locally, less sensitive to attacks. Our proposition is theoretically
motivated and shows on a first set of carefully conducted experiments that it
behaves as expected when used alone, and seems promising when coupled with
adversarial training.
Deep Learning Based Motion Planning For Autonomous Vehicle Using Spatiotemporal LSTM Network
Authors: Zhengwei Bai, Baigen Cai, Wei Shangguan, Linguo Chai
Date: 2019-03-05 00:00:00
Tasks: Motion Planning

Motion Planning, as a fundamental technology of automatic navigation for the
autonomous vehicle, is still an open challenging issue in the real-life traffic
situation and is mostly applied by the model-based approaches. However, due to
the complexity of the traffic situations and the uncertainty of the edge cases,
it is hard to devise a general motion planning system for the autonomous
vehicle. In this paper, we proposed a motion planning model based on deep
learning (named as spatiotemporal LSTM network), which is able to generate a
real-time reflection based on spatiotemporal information extraction. To be
specific, the model based on spatiotemporal LSTM network has three main
structure. Firstly, the Convolutional Long-short Term Memory (Conv-LSTM) is
used to extract hidden features through sequential image data. Then, the 3D
Convolutional Neural Network(3D-CNN) is applied to extract the spatiotemporal
information from the multi-frame feature information. Finally, the fully
connected neural networks are used to construct a control model for autonomous
vehicle steering angle. The experiments demonstrated that the proposed method
can generate a robust and accurate visual motion planning results for the
autonomous vehicle.
Applying Active Diagnosis to Space Systems by On-Board Control Procedures
Authors: Elodie Chanthery, Louise Travé-Massuyès, Yannick Pencolé, Régis De Ferluc, Brice Dellandrea
Date: 2019-03-05 00:00:00
Tasks: Management

The instrumentation of real systems is often designed for control purposes
and control inputs are designed to achieve nominal control objectives. Hence,
the available measurements may not be sufficient to isolate faults with
certainty and diagnoses are ambiguous. Active diagnosis formulates a planning
problem to generate a sequence of actions that, applied to the system, enforce
diagnosability and allow to iteratively refine ambiguous diagnoses. This paper
analyses the requirements for applying active diagnosis to space systems and
proposes ActHyDiag as an effective framework to solve this problem. It presents
the results of applying ActHyDiag to a real space case study and of
implementing the generated plans in the form of On-Board Control Procedures.
The case study is a redundant Spacewire Network where up to 6 instruments,
monitored and controlled by the on-board software hosted in the Satellite
Management Unit, are transferring science data to a mass memory unit through
Spacewire routers. Experiments have been conducted on a real physical benchmark
developed by Thales Alenia Space and demonstrate the effectiveness of the plans
proposed by ActHyDiag.
The Complexity of Morality: Checking Markov Blanket Consistency with DAGs via Morality
Authors: Yang Li, Kevin Korb, Lloyd Allison
Date: 2019-03-05 00:00:00

A family of Markov blankets in a faithful Bayesian network satisfies the
symmetry and consistency properties. In this paper, we draw a bijection between
families of consistent Markov blankets and moral graphs. We define the new
concepts of weak recursive simpliciality and perfect elimination kits. We prove
that they are equivalent to graph morality. In addition, we prove that morality
can be decided in polynomial time for graphs with maximum degree less than $5$,
but the problem is NP-complete for graphs with higher maximum degrees.
EdgeStereo: An Effective Multi-Task Learning Network for Stereo Matching and Edge Detection
Authors: Xiao Song, Xu Zhao, Liangji Fang, Hanwen Hu
Date: 2019-03-05 00:00:00
Tasks: Disparity Estimation, Edge Detection, Multi-Task Learning, Stereo Matching, Stereo Matching Hand

Recently, leveraging on the development of end-to-end convolutional neural networks (CNNs), deep stereo matching networks have achieved remarkable performance far exceeding traditional approaches. However, state-of-the-art stereo frameworks still have difficulties at finding correct correspondences in texture-less regions, detailed structures, small objects and near boundaries, which could be alleviated by geometric clues such as edge contours and corresponding constraints. To improve the quality of disparity estimates in these challenging areas, we propose an effective multi-task learning network, EdgeStereo, composed of a disparity estimation branch and an edge detection branch, which enables end-to-end predictions of both disparity map and edge map. To effectively incorporate edge cues, we propose the edge-aware smoothness loss and edge feature embedding for inter-task interactions. It is demonstrated that based on our unified model, edge detection task and stereo matching task can promote each other. In addition, we design a compact module called residual pyramid to replace the commonly-used multi-stage cascaded structures or 3-D convolution based regularization modules in current stereo matching networks. By the time of the paper submission, EdgeStereo achieves state-of-art performance on the FlyingThings3D dataset, KITTI 2012 and KITTI 2015 stereo benchmarks, outperforming other published stereo matching methods by a noteworthy margin. EdgeStereo also achieves comparable generalization performance for disparity estimation because of the incorporation of edge cues.
Improving Cross-Domain Chinese Word Segmentation with Word Embeddings
Authors: Yuxiao Ye, Yue Zhang, Weikang Li, Likun Qiu, Jian Sun
Date: 2019-03-05 00:00:00
Proceeding: NAACL 2019 6
Tasks: Chinese Word Segmentation, Segmentation, Word Embeddings

Cross-domain Chinese Word Segmentation (CWS) remains a challenge despite
recent progress in neural-based CWS. The limited amount of annotated data in
the target domain has been the key obstacle to a satisfactory performance. In
this paper, we propose a semi-supervised word-based approach to improving
cross-domain CWS given a baseline segmenter. Particularly, our model only
deploys word embeddings trained on raw text in the target domain, discarding
complex hand-crafted features and domain-specific dictionaries. Innovative
subsampling and negative sampling methods are proposed to derive word
embeddings optimized for CWS. We conduct experiments on five datasets in
special domains, covering domains in novels, medicine, and patent. Results show
that our model can obviously improve cross-domain CWS, especially in the
segmentation of domain-specific noun entities. The word F-measure increases by
over 3.0% on four datasets, outperforming state-of-the-art semi-supervised and
unsupervised cross-domain CWS approaches with a large margin. We make our code
and data available on Github.
Real-time Multiple People Hand Localization in 4D Point Clouds
Authors: Hao Jiang, Quanzeng You
Date: 2019-03-05 00:00:00

We propose novel real-time algorithm to localize hands and find their
associations with multiple people in the cluttered 4D volumetric data (dynamic
3D volumes). Different from the traditional multiple view approaches, which
find key points in 2D and then triangulate to recover the 3D locations, our
method directly processes the dynamic 3D data that involve both clutter and
crowd. The volumetric representation is more desirable than the partial
observations from different view points and enables more robust and accurate
results. However, due to the large amount of data in the volumetric
representation brute force 3D schemes are slow. In this paper, we propose novel
real-time methods to tackle the problem to achieve both higher accuracy and
faster speed than previous approaches. Our method detects the 3D bounding box
of each subject and localizes the hands of each person. We develop new 2D
features for fast candidate proposals and optimize the trajectory linking using
a new max-covering bipartite matching formulation, which is critical for robust
performance. We propose a novel decomposition method to reduce the key point
localization in each person 3D volume to a sequence of efficient 2D problems.
Our experiments show that the proposed method is faster than different
competing methods and it gives almost half the localization error.
Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment
Authors: Yifan Wu, Ezra Winston, Divyansh Kaushik, Zachary Lipton
Date: 2019-03-05 00:00:00
Proceeding: ICLR Workshop LLD 2019
Tasks: Domain Adaptation

Domain adaptation addresses the common problem when the target distribution
generating our test data drifts from the source (training) distribution. While
absent assumptions, domain adaptation is impossible, strict conditions, e.g.
covariate or label shift, enable principled algorithms. Recently-proposed
domain-adversarial approaches consist of aligning source and target encodings,
often motivating this approach as minimizing two (of three) terms in a
theoretical bound on target error. Unfortunately, this minimization can cause
arbitrary increases in the third term, e.g. they can break down under shifting
label distributions. We propose asymmetrically-relaxed distribution alignment,
a new approach that overcomes some limitations of standard domain-adversarial
algorithms. Moreover, we characterize precise assumptions under which our
algorithm is theoretically principled and demonstrate empirical benefits on
both synthetic and real datasets.
Using Big Five Personality Model to Detect Cultural Aspects in Crowds
Authors: Rodolfo Migon Favaretto, Leandro Dihl, Soraia Raupp Musse, Felipe Vilanova, Angelo Brandelli Costa
Date: 2019-03-05 00:00:00

The use of information technology in the study of human behavior is a subject
of great scientific interest. Cultural and personality aspects are factors that
influence how people interact with one another in a crowd. This paper presents
a methodology to detect cultural characteristics of crowds in video sequences.
Based on filmed sequences, pedestrians are detected, tracked and characterized.
Such information is then used to find out cultural differences in those videos,
based on the Big-five personality model. Regarding cultural differences of each
country, results indicate that this model generates coherent information when
compared to data provided in literature.
Convex Covariate Clustering for Classification
Authors: Daniel Andrade, Kenji Fukumizu, Yuzuru Okajima
Date: 2019-03-05 00:00:00
Tasks: Classification, Clustering, General Classification, Model Selection

Clustering, like covariate selection for classification, is an important step to compress and interpret the data. However, clustering of covariates is often performed independently of the classification step, which can lead to undesirable clustering results that harm interpretability and compression rate. Therefore, we propose a method that can cluster covariates while taking into account class label information of samples. We formulate the problem as a convex optimization problem which uses both, a-priori similarity information between covariates, and information from class-labeled samples. Like ordinary convex clustering [Chi and Lange, 2015], the proposed method offers a unique global minima making it insensitive to initialization. In order to solve the convex problem, we propose a specialized alternating direction method of multipliers (ADMM), which scales up to several thousands of variables. Furthermore, in order to circumvent computationally expensive cross-validation, we propose a model selection criterion based on approximating the marginal likelihood. Experiments on synthetic and real data confirm the usefulness of the proposed clustering method and the selection criterion.
Two-Stream Multi-Channel Convolutional Neural Network (TM-CNN) for Multi-Lane Traffic Speed Prediction Considering Traffic Volume Impact
Authors: Ruimin Ke, Wan Li, Zhiyong Cui, Yinhai Wang
Date: 2019-03-05 00:00:00
Tasks: Prediction

Traffic speed prediction is a critically important component of intelligent
transportation systems (ITS). Recently, with the rapid development of deep
learning and transportation data science, a growing body of new traffic speed
prediction models have been designed, which achieved high accuracy and
large-scale prediction. However, existing studies have two major limitations.
First, they predict aggregated traffic speed rather than lane-level traffic
speed; second, most studies ignore the impact of other traffic flow parameters
in speed prediction. To address these issues, we propose a two-stream
multi-channel convolutional neural network (TM-CNN) model for multi-lane
traffic speed prediction considering traffic volume impact. In this model, we
first introduce a new data conversion method that converts raw traffic speed
data and volume data into spatial-temporal multi-channel matrices. Then we
carefully design a two-stream deep neural network to effectively learn the
features and correlations between individual lanes, in the spatial-temporal
dimensions, and between speed and volume. Accordingly, a new loss function that
considers the volume impact in speed prediction is developed. A case study
using one-year data validates the TM-CNN model and demonstrates its
superiority. This paper contributes to two research areas: (1) traffic speed
prediction, and (2) multi-lane traffic flow study.
Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changes
Authors: Biwei Huang, Kun Zhang, Jiji Zhang, Joseph Ramsey, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf
Date: 2019-03-05 00:00:00
Tasks: Causal Discovery

It is commonplace to encounter heterogeneous or nonstationary data, of which the underlying generating process changes across domains or over time. Such a distribution shift feature presents both challenges and opportunities for causal discovery. In this paper, we develop a framework for causal discovery from such data, called Constraint-based causal Discovery from heterogeneous/NOnstationary Data (CD-NOD), to find causal skeleton and directions and estimate the properties of mechanism changes. First, we propose an enhanced constraint-based procedure to detect variables whose local mechanisms change and recover the skeleton of the causal structure over observed variables. Second, we present a method to determine causal orientations by making use of independent changes in the data distribution implied by the underlying causal model, benefiting from information carried by changing distributions. After learning the causal structure, next, we investigate how to efficiently estimate the "driving force" of the nonstationarity of a causal mechanism. That is, we aim to extract from data a low-dimensional representation of changes. The proposed methods are nonparametric, with no hard restrictions on data distributions and causal mechanisms, and do not rely on window segmentation. Furthermore, we find that data heterogeneity benefits causal structure identification even with particular types of confounders. Finally, we show the connection between heterogeneity/nonstationarity and soft intervention in causal discovery. Experimental results on various synthetic and real-world data sets (task-fMRI and stock market data) are presented to demonstrate the efficacy of the proposed methods.
Distinguishing mirror from glass: A 'big data' approach to material perception
Authors: Hideki Tamura, Konrad E. Prokott, Roland W. Fleming
Date: 2019-03-05 00:00:00
Tasks: Diagnostic

Visually identifying materials is crucial for many tasks, yet material
perception remains poorly understood. Distinguishing mirror from glass is
particularly challenging as both materials derive their appearance from their
surroundings, yet we rarely experience difficulties telling them apart. Here we
took a 'big data' approach to uncovering the underlying visual cues and
processes, leveraging recent advances in neural network models of vision. We
trained thousands of convolutional neural networks on >750,000 simulated mirror
and glass objects, and compared their performance with human judgments, as well
as alternative classifiers based on 'hand-engineered' image features. For
randomly chosen images, all classifiers and humans performed with high
accuracy, and therefore correlated highly with one another. To tease the models
apart, we then painstakingly assembled a diagnostic image set for which humans
make highly systematic errors, allowing us to decouple accuracy from human-like
performance. A large-scale, systematic search through feedforward neural
architectures revealed that relatively shallow networks predicted human
judgments better than any other models. However, surprisingly, no network
correlated better than 0.6 with humans (below inter-human correlations). Thus,
although the model sets new standards for simulating human vision in a
challenging material perception task, the results cast doubt on recent claims
that such architectures are generally good models of human vision.
Deep Active Localization
Authors: Sai Krishna, Keehong Seo, Dhaivat Bhatt, Vincent Mai, Krishna Murthy, Liam Paull
Date: 2019-03-05 00:00:00
Tasks: OpenAI Gym, Reinforcement Learning

Active localization is the problem of generating robot actions that allow it
to maximally disambiguate its pose within a reference map. Traditional
approaches to this use an information-theoretic criterion for action selection
and hand-crafted perceptual models. In this work we propose an end-to-end
differentiable method for learning to take informative actions that is
trainable entirely in simulation and then transferable to real robot hardware
with zero refinement. The system is composed of two modules: a convolutional
neural network for perception, and a deep reinforcement learned planning
module. We introduce a multi-scale approach to the learned perceptual model
since the accuracy needed to perform action selection with reinforcement
learning is much less than the accuracy needed for robot control. We
demonstrate that the resulting system outperforms using the traditional
approach for either perception or planning. We also demonstrate our approaches
robustness to different map configurations and other nuisance parameters
through the use of domain randomization in training. The code is also
compatible with the OpenAI gym framework, as well as the Gazebo simulator.
Online Data Poisoning Attack
Authors: Xuezhou Zhang, Xiaojin Zhu, Laurent Lessard
Date: 2019-03-05 00:00:00
Tasks: Data Poisoning, Deep Reinforcement Learning, Model Predictive Control, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We study data poisoning attacks in the online setting where training items arrive sequentially, and the attacker may perturb the current item to manipulate online learning. Importantly, the attacker has no knowledge of future training items nor the data generating distribution. We formulate online data poisoning attack as a stochastic optimal control problem, and solve it with model predictive control and deep reinforcement learning. We also upper bound the suboptimality suffered by the attacker for not knowing the data generating distribution. Experiments validate our control approach in generating near-optimal attacks on both supervised and unsupervised learning tasks.