pwc-archive/papers-with-abstracts huggingface dataset:
The Role of User Profile for Fake News Detection
Authors: Kai Shu, Xinyi Zhou, Suhang Wang, Reza Zafarani, Huan Liu
Date: 2019-04-30 00:00:00
Tasks: Fake News Detection, Feature Importance, News Classification

Consuming news from social media is becoming increasingly popular. Social
media appeals to users due to its fast dissemination of information, low cost,
and easy access. However, social media also enables the widespread of fake
news. Because of the detrimental societal effects of fake news, detecting fake
news has attracted increasing attention. However, the detection performance
only using news contents is generally not satisfactory as fake news is written
to mimic true news. Thus, there is a need for an in-depth understanding on the
relationship between user profiles on social media and fake news. In this
paper, we study the challenging problem of understanding and exploiting user
profiles on social media for fake news detection. In an attempt to understand
connections between user profiles and fake news, first, we measure users'
sharing behaviors on social media and group representative users who are more
likely to share fake and real news; then, we perform a comparative analysis of
explicit and implicit profile features between these user groups, which reveals
their potential to help differentiate fake news from real news. To exploit user
profile features, we demonstrate the usefulness of these user profile features
in a fake news classification task. We further validate the effectiveness of
these features through feature importance analysis. The findings of this work
lay the foundation for deeper exploration of user profile features of social
media and enhance the capabilities for fake news detection.
Object Contour and Edge Detection with RefineContourNet
Authors: Andre Peter Kelm, Vijesh Soorya Rao, Udo Zoelzer
Date: 2019-04-30 00:00:00
Tasks: Contour Detection, Edge Detection, Object

A ResNet-based multi-path refinement CNN is used for object contour detection. For this task, we prioritise the effective utilization of the high-level abstraction capability of a ResNet, which leads to state-of-the-art results for edge detection. Keeping our focus in mind, we fuse the high, mid and low-level features in that specific order, which differs from many other approaches. It uses the tensor with the highest-levelled features as the starting point to combine it layer-by-layer with features of a lower abstraction level until it reaches the lowest level. We train this network on a modified PASCAL VOC 2012 dataset for object contour detection and evaluate on a refined PASCAL-val dataset reaching an excellent performance and an Optimal Dataset Scale (ODS) of 0.752. Furthermore, by fine-training on the BSDS500 dataset we reach state-of-the-art results for edge-detection with an ODS of 0.824.
Multimodal Classification of Urban Micro-Events
Authors: Maarten Sukel, Stevan Rudinac, Marcel Worring
Date: 2019-04-30 00:00:00
Tasks: Classification, General Classification, Representation Learning

In this paper we seek methods to effectively detect urban micro-events. Urban
micro-events are events which occur in cities, have limited geographical
coverage and typically affect only a small group of citizens. Because of their
scale these are difficult to identify in most data sources. However, by using
citizen sensing to gather data, detecting them becomes feasible. The data
gathered by citizen sensing is often multimodal and, as a consequence, the
information required to detect urban micro-events is distributed over multiple
modalities. This makes it essential to have a classifier capable of combining
them. In this paper we explore several methods of creating such a classifier,
including early, late, hybrid fusion and representation learning using
multimodal graphs. We evaluate performance on a real world dataset obtained
from a live citizen reporting system. We show that a multimodal approach yields
higher performance than unimodal alternatives. Furthermore, we demonstrate that
our hybrid combination of early and late fusion with multimodal embeddings
performs best in classification of urban micro-events.
PYRO-NN: Python Reconstruction Operators in Neural Networks
Authors: Christopher Syben, Markus Michen, Bernhard Stimpel, Stephan Seitz, Stefan Ploner, Andreas K. Maier
Date: 2019-04-30 00:00:00
Tasks: CT Reconstruction, Deep Learning, Image Reconstruction

Purpose: Recently, several attempts were conducted to transfer deep learning
to medical image reconstruction. An increasingly number of publications follow
the concept of embedding the CT reconstruction as a known operator into a
neural network. However, most of the approaches presented lack an efficient CT
reconstruction framework fully integrated into deep learning environments. As a
result, many approaches are forced to use workarounds for mathematically
unambiguously solvable problems. Methods: PYRO-NN is a generalized framework to
embed known operators into the prevalent deep learning framework Tensorflow.
The current status includes state-of-the-art parallel-, fan- and cone-beam
projectors and back-projectors accelerated with CUDA provided as Tensorflow
layers. On top, the framework provides a high level Python API to conduct FBP
and iterative reconstruction experiments with data from real CT systems.
Results: The framework provides all necessary algorithms and tools to design
end-to-end neural network pipelines with integrated CT reconstruction
algorithms. The high level Python API allows a simple use of the layers as
known from Tensorflow. To demonstrate the capabilities of the layers, the
framework comes with three baseline experiments showing a cone-beam short scan
FDK reconstruction, a CT reconstruction filter learning setup, and a TV
regularized iterative reconstruction. All algorithms and tools are referenced
to a scientific publication and are compared to existing non deep learning
reconstruction frameworks. The framework is available as open-source software
at \url{https://github.com/csyben/PYRO-NN}. Conclusions: PYRO-NN comes with the
prevalent deep learning framework Tensorflow and allows to setup end-to-end
trainable neural networks in the medical image reconstruction context. We
believe that the framework will be a step towards reproducible research
Learning Fair Representations via an Adversarial Framework
Authors: Rui Feng, Yang Yang, Yuehan Lyu, Chenhao Tan, Yizhou Sun, Chunping Wang
Date: 2019-04-30 00:00:00
Tasks: Classification, Fairness, General Classification

Fairness has become a central issue for our research community as
classification algorithms are adopted in societally critical domains such as
recidivism prediction and loan approval. In this work, we consider the
potential bias based on protected attributes (e.g., race and gender), and
tackle this problem by learning latent representations of individuals that are
statistically indistinguishable between protected groups while sufficiently
preserving other information for classification. To do that, we develop a
minimax adversarial framework with a generator to capture the data distribution
and generate latent representations, and a critic to ensure that the
distributions across different protected groups are similar. Our framework
provides a theoretical guarantee with respect to statistical parity and
individual fairness. Empirical results on four real-world datasets also show
that the learned representation can effectively be used for classification
tasks such as credit risk prediction while obstructing information related to
protected groups, especially when removing protected attributes is not
sufficient for fair classification.
Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Data
Authors: Xin Du, Lei Sun, Wouter Duivesteijn, Alexander Nikolaev, Mykola Pechenizkiy
Date: 2019-04-30 00:00:00
Tasks: Causal Inference, Representation Learning, Selection bias, Sociology

Learning causal effects from observational data greatly benefits a variety of domains such as health care, education and sociology. For instance, one could estimate the impact of a new drug on specific individuals to assist the clinic plan and improve the survival rate. In this paper, we focus on studying the problem of estimating Conditional Average Treatment Effect (CATE) from observational data. The challenges for this problem are two-fold: on the one hand, we have to derive a causal estimator to estimate the causal quantity from observational data, where there exists confounding bias; on the other hand, we have to deal with the identification of CATE when the distribution of covariates in treatment and control groups are imbalanced. To overcome these challenges, we propose a neural network framework called Adversarial Balancing-based representation learning for Causal Effect Inference (ABCEI), based on the recent advances in representation learning. To ensure the identification of CATE, ABCEI uses adversarial learning to balance the distributions of covariates in treatment and control groups in the latent representation space, without any assumption on the form of the treatment selection/assignment function. In addition, during the representation learning and balancing process, highly predictive information from the original covariate space might be lost. ABCEI can tackle this information loss problem by preserving useful information for predicting causal effects under the regularization of a mutual information estimator. The experimental results show that ABCEI is robust against treatment selection bias, and matches/outperforms the state-of-the-art approaches. Our experiments show promising results on several datasets, representing different health care domains among others.
Coevo: a collaborative design platform with artificial agents
Authors: Gerard Serra, David Miralles
Date: 2019-04-30 00:00:00
Tasks: Transfer Learning

We present Coevo, an online platform that allows both humans and artificial
agents to design shapes that solve different tasks. Our goal is to explore
common shared design tools that can be used by humans and artificial agents in
a context of creation. This approach can provide a better knowledge transfer
and interaction with artificial agents since a common language of design is
defined. In this paper, we outline the main components of this platform and
discuss the definition of a human-centered language to enhance human-AI
collaboration in co-creation scenarios.
Effective and Efficient Indexing in Cross-Modal Hashing-Based Datasets
Authors: Sarawut Markchit, Chih-Yi Chiu
Date: 2019-04-30 00:00:00
Tasks: Cross-Modal Retrieval, Retrieval

To overcome the barrier of storage and computation, the hashing technique has been widely used for nearest neighbor search in multimedia retrieval applications recently. Particularly, cross-modal retrieval that searches across different modalities becomes an active but challenging problem. Although dozens of cross-modal hashing algorithms are proposed to yield compact binary codes, the exhaustive search is impractical for the real-time purpose, and Hamming distance computation suffers inaccurate results. In this paper, we propose a novel search method that utilizes a probability-based index scheme over binary hash codes in cross-modal retrieval. The proposed hash code indexing scheme exploits a few binary bits of the hash code as the index code. We construct an inverted index table based on index codes and train a neural network to improve the indexing accuracy and efficiency. Experiments are performed on two benchmark datasets for retrieval across image and text modalities, where hash codes are generated by three cross-modal hashing methods. Results show the proposed method effectively boost the performance on these hash methods.
Learning from Implicit Information in Natural Language Instructions for Robotic Manipulations
Authors: Ozan Arkan Can, Pedro Zuidberg Dos Martires, Andreas Persson, Julian Gaal, Amy Loutfi, Luc De Raedt, Deniz Yuret, Alessandro Saffiotti
Date: 2019-04-30 00:00:00
Proceeding: WS 2019 6

Human-robot interaction often occurs in the form of instructions given from a
human to a robot. For a robot to successfully follow instructions, a common
representation of the world and objects in it should be shared between humans
and the robot so that the instructions can be grounded. Achieving this
representation can be done via learning, where both the world representation
and the language grounding are learned simultaneously. However, in robotics
this can be a difficult task due to the cost and scarcity of data. In this
paper, we tackle the problem by separately learning the world representation of
the robot and the language grounding. While this approach can address the
challenges in getting sufficient data, it may give rise to inconsistencies
between both learned components. Therefore, we further propose Bayesian
learning to resolve such inconsistencies between the natural language grounding
and a robot's world representation by exploiting spatio-relational information
that is implicitly present in instructions given by a human. Moreover, we
demonstrate the feasibility of our approach on a scenario involving a robotic
arm in the physical world.
Model Comparison for Semantic Grouping
Authors: Francisco Vargas, Kamen Brestnichki, Nils Hammerla
Date: 2019-04-30 00:00:00
Tasks: model, Semantic Similarity, Semantic Textual Similarity, STS

We introduce a probabilistic framework for quantifying the semantic
similarity between two groups of embeddings. We formulate the task of semantic
similarity as a model comparison task in which we contrast a generative model
which jointly models two sentences versus one that does not. We illustrate how
this framework can be used for the Semantic Textual Similarity tasks using
clear assumptions about how the embeddings of words are generated. We apply
model comparison that utilises information criteria to address some of the
shortcomings of Bayesian model comparison, whilst still penalising model
complexity. We achieve competitive results by applying the proposed framework
with an appropriate choice of likelihood on the STS datasets.
A data-efficient geometrically inspired polynomial kernel for robot inverse dynamics
Authors: Alberto Dalla Libera, Ruggero Carli
Date: 2019-04-30 00:00:00

In this paper, we introduce a novel data-driven inverse dynamics estimator based on Gaussian Process Regression. Driven by the fact that the inverse dynamics can be described as a polynomial function on a suitable input space, we propose the use of a novel kernel, called Geometrically Inspired Polynomial Kernel (GIP). The resulting estimator behaves similarly to model-based approaches as concerns data efficiency. Indeed, we proved that the GIP kernel defines a finite-dimensional Reproducing Kernel Hilbert Space that contains the inverse dynamics function computed through the Rigid Body Dynamics. The proposed kernel is based on the recently introduced Multiplicative Polynomial Kernel, a redefinition of the classical polynomial kernel equipped with a set of parameters that allows for a higher regularization. We tested the proposed approach in a simulated environment, and also in real experiments with a UR10 robot. The obtained results confirm that, compared to other data-driven estimators, the proposed approach is more data-efficient and exhibits better generalization properties. Instead, with respect to model-based estimators, our approach requires less prior information and is not affected by model bias.
On Social Machines for Algorithmic Regulation
Authors: Nello Cristianini, Teresa Scantamburlo
Date: 2019-04-30 00:00:00
Tasks: Management

Autonomous mechanisms have been proposed to regulate certain aspects of
society and are already being used to regulate business organisations. We take
seriously recent proposals for algorithmic regulation of society, and we
identify the existing technologies that can be used to implement them, most of
them originally introduced in business contexts. We build on the notion of
'social machine' and we connect it to various ongoing trends and ideas,
including crowdsourced task-work, social compiler, mechanism design, reputation
management systems, and social scoring. After showing how all the building
blocks of algorithmic regulation are already well in place, we discuss possible
implications for human autonomy and social order. The main contribution of this
paper is to identify convergent social and technical trends that are leading
towards social regulation by algorithms, and to discuss the possible social,
political, and ethical consequences of taking this path.
Segmentation is All You Need
Authors: Zehua Cheng, Yuxiang Wu, Zhenghua Xu, Thomas Lukasiewicz, Weiyang Wang
Date: 2019-04-30 00:00:00
Tasks: All, Face Detection, Head Detection, Object, object-detection, Object Detection, Region Proposal, Robust Object Detection, Segmentation

Region proposal mechanisms are essential for existing deep learning approaches to object detection in images. Although they can generally achieve a good detection performance under normal circumstances, their recall in a scene with extreme cases is unacceptably low. This is mainly because bounding box annotations contain much environment noise information, and non-maximum suppression (NMS) is required to select target boxes. Therefore, in this paper, we propose the first anchor-free and NMS-free object detection model called weakly supervised multimodal annotation segmentation (WSMA-Seg), which utilizes segmentation models to achieve an accurate and robust object detection without NMS. In WSMA-Seg, multimodal annotations are proposed to achieve an instance-aware segmentation using weakly supervised bounding boxes; we also develop a run-data-based following algorithm to trace contours of objects. In addition, we propose a multi-scale pooling segmentation (MSP-Seg) as the underlying segmentation model of WSMA-Seg to achieve a more accurate segmentation and to enhance the detection accuracy of WSMA-Seg. Experimental results on multiple datasets show that the proposed WSMA-Seg approach outperforms the state-of-the-art detectors.
Performing Structured Improvisations with pre-trained Deep Learning Models
Authors: Pablo Samuel Castro
Date: 2019-04-30 00:00:00
Tasks: Deep Learning

The quality of outputs produced by deep generative models for music have seen
a dramatic improvement in the last few years. However, most deep learning
models perform in "offline" mode, with few restrictions on the processing time.
Integrating these types of models into a live structured performance poses a
challenge because of the necessity to respect the beat and harmony. Further,
these deep models tend to be agnostic to the style of a performer, which often
renders them impractical for live performance. In this paper we propose a
system which enables the integration of out-of-the-box generative models by
leveraging the musician's creativity and expertise.
CT-To-MR Conditional Generative Adversarial Networks for Ischemic Stroke Lesion Segmentation
Authors: Jonathan Rubin, S. Mazdak Abulnaga
Date: 2019-04-30 00:00:00
Tasks: Generative Adversarial Network, Image-to-Image Translation, Ischemic Stroke Lesion Segmentation, Lesion Segmentation, Translation

Infarcted brain tissue resulting from acute stroke readily shows up as
hyperintense regions within diffusion-weighted magnetic resonance imaging
(DWI). It has also been proposed that computed tomography perfusion (CTP) could
alternatively be used to triage stroke patients, given improvements in speed
and availability, as well as reduced cost. However, CTP has a lower signal to
noise ratio compared to MR. In this work, we investigate whether a conditional
mapping can be learned by a generative adversarial network to map CTP inputs to
generated MR DWI that more clearly delineates hyperintense regions due to
ischemic stroke. We detail the architectures of the generator and discriminator
and describe the training process used to perform image-to-image translation
from multi-modal CT perfusion maps to diffusion weighted MR outputs. We
evaluate the results both qualitatively by visual comparison of generated MR to
ground truth, as well as quantitatively by training fully convolutional neural
networks that make use of generated MR data inputs to perform ischemic stroke
lesion segmentation. Segmentation networks trained using generated CT-to-MR
inputs result in at least some improvement on all metrics used for evaluation,
compared with networks that only use CT perfusion input.
Detecting Reflections by Combining Semantic and Instance Segmentation
Authors: David Owen, Ping-Lin Chang
Date: 2019-04-30 00:00:00
Tasks: Instance Segmentation, Panoptic Segmentation, Segmentation, Semantic Segmentation

Reflections in natural images commonly cause false positives in automated
detection systems. These false positives can lead to significant impairment of
accuracy in the tasks of detection, counting and segmentation. Here, inspired
by the recent panoptic approach to segmentation, we show how fusing instance
and semantic segmentation can automatically identify reflection false
positives, without explicitly needing to have the reflective regions labelled.
We explore in detail how state of the art two-stage detectors suffer a loss of
broader contextual features, and hence are unable to learn to ignore these
reflections. We then present an approach to fuse instance and semantic
segmentations for this application, and subsequently show how this reduces
false positive detections in a real world surveillance data with a large number
of reflective surfaces. This demonstrates how panoptic segmentation and related
work, despite being in its infancy, can already be useful in real world
computer vision problems.
Non-Rigid Structure-From-Motion by Rank-One Basis Shapes
Authors: Sami S. Brandt, Hanno Ackermann
Date: 2019-04-30 00:00:00

In this paper, we show that the affine, non-rigid structure-from-motion
problem can be solved by rank-one, thus degenerate, basis shapes. It is a
natural reformulation of the classic low-rank method by Bregler et al., where
it was assumed that the deformable 3D structure is generated by a linear
combination of rigid basis shapes. The non-rigid shape will be decomposed into
the mean shape and the degenerate shapes, constructed from the right singular
vectors of the low-rank decomposition. The right singular vectors are affinely
back-projected into the 3D space, and the affine back-projections will also be
solved as part of the factorisation. By construction, a direct interpretation
for the right singular vectors of the low-rank decomposition will also follow:
they can be seen as principal components, hence, the first variant of our
method is referred to as Rank-1-PCA. The second variant, referred to as
Rank-1-ICA, additionally estimates the orthogonal transform which maps the
deformation modes into as statistically independent modes as possible. It has
the advantage of pinpointing statistically dependent subspaces related to, for
instance, lip movements on human faces. Moreover, in contrast to prior works,
no predefined dimensionality for the subspaces is imposed. The experiments on
several datasets show that the method achieves better results than the
state-of-the-art, it can be computed faster, and it provides an intuitive
interpretation for the deformation modes.