pwc-archive/papers-with-abstracts huggingface dataset:
GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction
Authors: Baris Gecer, Stylianos Ploumpis, Irene Kotsia, Stefanos Zafeiriou
Date: 2019-02-15 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Face Reconstruction, Face Reconstruction, Generative Adversarial Network, Vocal Bursts Intensity Prediction

In the past few years, a lot of work has been done towards reconstructing the
3D facial structure from single images by capitalizing on the power of Deep
Convolutional Neural Networks (DCNNs). In the most recent works, differentiable
renderers were employed in order to learn the relationship between the facial
identity features and the parameters of a 3D morphable model for shape and
texture. The texture features either correspond to components of a linear
texture space or are learned by auto-encoders directly from in-the-wild images.
In all cases, the quality of the facial texture reconstruction of the
state-of-the-art methods is still not capable of modeling textures in high
fidelity. In this paper, we take a radically different approach and harness the
power of Generative Adversarial Networks (GANs) and DCNNs in order to
reconstruct the facial texture and shape from single images. That is, we
utilize GANs to train a very powerful generator of facial texture in UV space.
Then, we revisit the original 3D Morphable Models (3DMMs) fitting approaches
making use of non-linear optimization to find the optimal latent parameters
that best reconstruct the test image but under a new perspective. We optimize
the parameters with the supervision of pretrained deep identity features
through our end-to-end differentiable framework. We demonstrate excellent
results in photorealistic and identity preserving 3D face reconstructions and
achieve for the first time, to the best of our knowledge, facial texture
reconstruction with high-frequency details.
DeepFault: Fault Localization for Deep Neural Networks
Authors: Hasan Ferit Eniser, Simos Gerasimou, Alper Sen
Date: 2019-02-15 00:00:00
Tasks: Autonomous Vehicles, DNN Testing, Fault localization

Deep Neural Networks (DNNs) are increasingly deployed in safety-critical
applications including autonomous vehicles and medical diagnostics. To reduce
the residual risk for unexpected DNN behaviour and provide evidence for their
trustworthy operation, DNNs should be thoroughly tested. The DeepFault whitebox
DNN testing approach presented in our paper addresses this challenge by
employing suspiciousness measures inspired by fault localization to establish
the hit spectrum of neurons and identify suspicious neurons whose weights have
not been calibrated correctly and thus are considered responsible for
inadequate DNN performance. DeepFault also uses a suspiciousness-guided
algorithm to synthesize new inputs, from correctly classified inputs, that
increase the activation values of suspicious neurons. Our empirical evaluation
on several DNN instances trained on MNIST and CIFAR-10 datasets shows that
DeepFault is effective in identifying suspicious neurons. Also, the inputs
synthesized by DeepFault closely resemble the original inputs, exercise the
identified suspicious neurons and are highly adversarial.
Parameter Efficient Training of Deep Convolutional Neural Networks by Dynamic Sparse Reparameterization
Authors: Hesham Mostafa, Xin Wang
Date: 2019-02-15 00:00:00

Modern deep neural networks are typically highly overparameterized. Pruning techniques are able to remove a significant fraction of network parameters with little loss in accuracy. Recently, techniques based on dynamic reallocation of non-zero parameters have emerged, allowing direct training of sparse networks without having to pre-train a large dense model. Here we present a novel dynamic sparse reparameterization method that addresses the limitations of previous techniques such as high computational cost and the need for manual configuration of the number of free parameters allocated to each layer. We evaluate the performance of dynamic reallocation methods in training deep convolutional networks and show that our method outperforms previous static and dynamic reparameterization methods, yielding the best accuracy for a fixed parameter budget, on par with accuracies obtained by iteratively pruning a pre-trained dense model. We further investigated the mechanisms underlying the superior generalization performance of the resultant sparse networks. We found that neither the structure, nor the initialization of the non-zero parameters were sufficient to explain the superior performance. Rather, effective learning crucially depended on the continuous exploration of the sparse network structure space during training. Our work suggests that exploring structural degrees of freedom during training is more effective than adding extra parameters to the network.
From Dark Matter to Galaxies with Convolutional Networks
Authors: Xinyue Zhang, Yanfang Wang, Wei zhang, Yueqiu Sun, Siyu He, Gabriella Contardo, Francisco Villaescusa-Navarro, Shirley Ho
Date: 2019-02-15 00:00:00
Tasks: CPU

Cosmological surveys aim at answering fundamental questions about our
Universe, including the nature of dark matter or the reason of unexpected
accelerated expansion of the Universe. In order to answer these questions, two
important ingredients are needed: 1) data from observations and 2) a
theoretical model that allows fast comparison between observation and theory.
Most of the cosmological surveys observe galaxies, which are very difficult to
model theoretically due to the complicated physics involved in their formation
and evolution; modeling realistic galaxies over cosmological volumes requires
running computationally expensive hydrodynamic simulations that can cost
millions of CPU hours. In this paper, we propose to use deep learning to
establish a mapping between the 3D galaxy distribution in hydrodynamic
simulations and its underlying dark matter distribution. One of the major
challenges in this pursuit is the very high sparsity in the predicted galaxy
distribution. To this end, we develop a two-phase convolutional neural network
architecture to generate fast galaxy catalogues, and compare our results
against a standard cosmological technique. We find that our proposed approach
either outperforms or is competitive with traditional cosmological techniques.
Compared to the common methods used in cosmology, our approach also provides a
nice trade-off between time-consumption (comparable to fastest benchmark in the
literature) and the quality and accuracy of the predicted simulation. In
combination with current and upcoming data from cosmological observations, our
method has the potential to answer fundamental questions about our Universe
with the highest accuracy.
The excluded area of two-dimensional hard particles
Authors: Thomas Geigenfeind, Daniel de las Heras
Date: 2019-02-15 00:00:00
Tasks: Vocal Bursts Valence Prediction

The excluded area between a pair of two-dimensional hard particles with given
relative orientation is the region in which one particle cannot be located due
to the presence of the other particle. The magnitude of the excluded area as a
function of the relative particle orientation plays a major role in the
determination of the bulk phase behaviour of hard particles. We use principal
component analysis to identify the different types of excluded area
corresponding to randomly generated two-dimensional hard particles modeled as
non-self-intersecting polygons and star lines (line segments radiating from a
common origin). Only three principal components are required to have an
excellent representation of the value of the excluded area as a function of the
relative particle orientation. Independently of the particle shape, the minimum
value of the excluded area is always achieved when the particles are
antiparallel to each other. The property that affects the value of the excluded
area most strongly is the elongation of the particle shape. Principal component
analysis identifies four limiting cases of excluded areas with one to four
global minima at equispaced relative orientations. We study selected particle
shapes using Monte Carlo simulations.
Learning Topological Representation for Networks via Hierarchical Sampling
Authors: Guoji Fu, Chengbin Hou, Xin Yao
Date: 2019-02-15 00:00:00
Tasks: Link Prediction, Representation Learning

The topological information is essential for studying the relationship
between nodes in a network. Recently, Network Representation Learning (NRL),
which projects a network into a low-dimensional vector space, has been shown
their advantages in analyzing large-scale networks. However, most existing NRL
methods are designed to preserve the local topology of a network, they fail to
capture the global topology. To tackle this issue, we propose a new NRL
framework, named HSRL, to help existing NRL methods capture both the local and
global topological information of a network. Specifically, HSRL recursively
compresses an input network into a series of smaller networks using a
community-awareness compressing strategy. Then, an existing NRL method is used
to learn node embeddings for each compressed network. Finally, the node
embeddings of the input network are obtained by concatenating the node
embeddings from all compressed networks. Empirical studies for link prediction
on five real-world datasets demonstrate the advantages of HSRL over
state-of-the-art methods.
Author Profiling for Hate Speech Detection
Authors: Pushkar Mishra, Marco del Tredici, Helen Yannakoudakis, Ekaterina Shutova
Date: 2019-02-14 00:00:00
Tasks: 16k, Author Profiling, Hate Speech Detection

The rapid growth of social media in recent years has fed into some highly
undesirable phenomena such as proliferation of abusive and offensive language
on the Internet. Previous research suggests that such hateful content tends to
come from users who share a set of common stereotypes and form communities
around them. The current state-of-the-art approaches to hate speech detection
are oblivious to user and community information and rely entirely on textual
(i.e., lexical and semantic) cues. In this paper, we propose a novel approach
to this problem that incorporates community-based profiling features of Twitter
users. Experimenting with a dataset of 16k tweets, we show that our methods
significantly outperform the current state of the art in hate speech detection.
Further, we conduct a qualitative analysis of model characteristics. We release
our code, pre-trained models and all the resources used in the public domain.
Openbots
Authors: Dennis Assenmacher, Lena Adam, Lena Frischlich, Heike Trautmann, Christian Grimme
Date: 2019-02-14 00:00:00

Social bots have recently gained attention in the context of public opinion
manipulation on social media platforms. While a lot of research effort has been
put into the classification and detection of such (semi-)automated programs, it
is still unclear how sophisticated those bots actually are, which platforms
they target, and where they originate from. To answer these questions, we
gathered repository data from open source collaboration platforms to identify
the status-quo as well as trends of publicly available bot code. Our findings
indicate that most of the code on collaboration platforms is of supportive
nature and provides modules of automation instead of fully fledged social bot
programs. Hence, the cost (in terms of additional programming effort) for
building social bots with the goal of topic-specific manipulation is higher
than assumed and that methods in context of machine- or deep-learning currently
only play a minor role. However, our approach can be applied as multifaceted
knowledge discovery framework to monitor trends in public bot code evolution to
detect new developments and streams.
Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification
Authors: Fenyu Hu, Yanqiao Zhu, Shu Wu, Liang Wang, Tieniu Tan
Date: 2019-02-13 00:00:00
Tasks: Classification, General Classification, Node Classification

Graph convolutional networks (GCNs) have been successfully applied in node classification tasks of network mining. However, most of these models based on neighborhood aggregation are usually shallow and lack the "graph pooling" mechanism, which prevents the model from obtaining adequate global information. In order to increase the receptive field, we propose a novel deep Hierarchical Graph Convolutional Network (H-GCN) for semi-supervised node classification. H-GCN first repeatedly aggregates structurally similar nodes to hyper-nodes and then refines the coarsened graph to the original to restore the representation for each node. Instead of merely aggregating one- or two-hop neighborhood information, the proposed coarsening procedure enlarges the receptive field for each node, hence more global information can be captured. The proposed H-GCN model shows strong empirical performance on various public benchmark graph datasets, outperforming state-of-the-art methods and acquiring up to 5.9% performance improvement in terms of accuracy. In addition, when only a few labeled samples are provided, our model gains substantial improvements.
Mobile Artificial Intelligence Technology for Detecting Macula Edema and Subretinal Fluid on OCT Scans: Initial Results from the DATUM alpha Study
Authors: Stephen G. Odaibo, Mikelson MomPremier, Richard Y. Hwang, Salman J. Yousuf, Steven L. Williams, Joshua Grant
Date: 2019-02-08 00:00:00
Tasks: Diagnostic, Specificity

Artificial Intelligence (AI) is necessary to address the large and growing
deficit in retina and healthcare access globally. And mobile AI diagnostic
platforms running in the Cloud may effectively and efficiently distribute such
AI capability. Here we sought to evaluate the feasibility of Cloud-based mobile
artificial intelligence for detection of retinal disease. And to evaluate the
accuracy of a particular such system for detection of subretinal fluid (SRF)
and macula edema (ME) on OCT scans. A multicenter retrospective image analysis
was conducted in which board-certified ophthalmologists with fellowship
training in retina evaluated OCT images of the macula. They noted the presence
or absence of ME or SRF, then compared their assessment to that obtained from
Fluid Intelligence, a mobile AI app that detects SRF and ME on OCT scans.
Investigators consecutively selected retinal OCTs, while making effort to
balance the number of scans with retinal fluid and scans without. Exclusion
criteria included poor scan quality, ambiguous features, macula holes,
retinoschisis, and dense epiretinal membranes. Accuracy in the form of
sensitivity and specificity of the AI mobile App was determined by comparing
its assessments to those of the retina specialists. At the time of this
submission, five centers have completed their initial studies. This consists of
a total of 283 OCT scans of which 155 had either ME or SRF ("wet") and 128 did
not ("dry"). The sensitivity ranged from 82.5% to 97% with a weighted average
of 89.3%. The specificity ranged from 52% to 100% with a weighted average of
81.23%. CONCLUSION: Cloud-based Mobile AI technology is feasible for the
detection retinal disease. In particular, Fluid Intelligence (alpha version),
is sufficiently accurate as a screening tool for SRF and ME, especially in
underserved areas. Further studies and technology development is needed.
Reduce Noise in Computed Tomography Image using Adaptive Gaussian Filter
Authors: Rini Mayasari, Nono Heryana
Date: 2019-02-05 00:00:00

One image processing application that is very helpful for humans is to
improve image quality, poor image quality makes the image more difficult to
interpret because the information conveyed by the image is reduced. In the
process of the acquisition of medical images, the resulting image has decreased
quality (degraded) due to external factors and medical equipment used. For this
reason, it is necessary to have an image processing process to improve the
quality of medical images, so that later it is expected to help facilitate
medical personnel in analyzing and translating medical images, which will lead
to an improvement in the quality of diagnosis. In this study, an analysis will
be carried out to improve the quality of medical images with noise reduction
with the Gaussian Filter Method. Next, it is carried out, and tested against
medical images, in this case, the lung photo image. The test image is given
noise in the form of impulse salt & pepper and adaptive Gaussian then analyzed
its performance qualitatively by comparing the output filter image, noise
image, and the original image by naked eye.
A Novel Universal Solar Energy Predictor
Authors: Nirupam Bidikar, Kotoju Rajitha, P. Usha Supriya
Date: 2019-02-01 00:00:00

Solar energy is one of the most economical and clean sustainable energy
sources on the planet. However, the solar energy throughput is highly
unpredictable due to its dependency on a plethora of conditions including
weather, seasons, and other ecological/environmental conditions. Thus, the
solar energy prediction is an inevitable necessity to optimize solar energy and
also to improve the efficiency of solar energy systems. Conventionally, the
optimization of the solar energy is undertaken by subject matter experts using
their domain knowledge; although it is impractical for even the experts to tune
the solar systems on a continuous basis. We strongly believe that the power of
machine learning can be harnessed to better optimize the solar energy
production by learning the correlation between various conditions and solar
energy production from historical data which is typically readily available.
For this use, this paper predicts the daily total energy generation of an
installed solar program using the Naive Bayes classifier. In the forecast
procedure, one year historical dataset including daily moderate temperatures,
daily total sunshine duration, daily total global solar radiation and daily
total photovoltaic energy generation parameters are used as the categorical
valued features. By way of this Naive Bayes program the sensitivity and the
precision measures are improved for the photovoltaic energy prediction and also
the consequences of other solar characteristics on the solar energy production
have been assessed.
Fake News Detection on Social Media using Geometric Deep Learning
Authors: Federico Monti, Fabrizio Frasca, Davide Eynard, Damon Mannion, Michael M. Bronstein
Date: 2019-02-10 00:00:00
Tasks: Common Sense Reasoning, Deep Learning, Fact Checking, Fake News Detection, Graph Classification

Social media are nowadays one of the main news sources for millions of people
around the globe due to their low cost, easy access and rapid dissemination.
This however comes at the cost of dubious trustworthiness and significant risk
of exposure to 'fake news', intentionally written to mislead the readers.
Automatically detecting fake news poses challenges that defy existing
content-based analysis approaches. One of the main reasons is that often the
interpretation of the news requires the knowledge of political or social
context or 'common sense', which current NLP algorithms are still missing.
Recent studies have shown that fake and real news spread differently on social
media, forming propagation patterns that could be harnessed for the automatic
fake news detection. Propagation-based approaches have multiple advantages
compared to their content-based counterparts, among which is language
independence and better resilience to adversarial attacks. In this paper we
show a novel automatic fake news detection model based on geometric deep
learning. The underlying core algorithms are a generalization of classical CNNs
to graphs, allowing the fusion of heterogeneous data such as content, user
profile and activity, social graph, and news propagation. Our model was trained
and tested on news stories, verified by professional fact-checking
organizations, that were spread on Twitter. Our experiments indicate that
social network structure and propagation are important features allowing highly
accurate (92.7% ROC AUC) fake news detection. Second, we observe that fake news
can be reliably detected at an early stage, after just a few hours of
propagation. Third, we test the aging of our model on training and testing data
separated in time. Our results point to the promise of propagation-based
approaches for fake news detection as an alternative or complementary strategy
to content-based approaches.
Link Prediction via Higher-Order Motif Features
Authors: Ghadeer Abuoda, Gianmarco De Francisci Morales, Ashraf Aboulnaga
Date: 2019-02-08 00:00:00
Tasks: General Classification, Link Prediction, Prediction, Recommendation Systems

Link prediction requires predicting which new links are likely to appear in a graph. Being able to predict unseen links with good accuracy has important applications in several domains such as social media, security, transportation, and recommendation systems. A common approach is to use features based on the common neighbors of an unconnected pair of nodes to predict whether the pair will form a link in the future. In this paper, we present an approach for link prediction that relies on higher-order analysis of the graph topology, well beyond common neighbors. We treat the link prediction problem as a supervised classification problem, and we propose a set of features that depend on the patterns or motifs that a pair of nodes occurs in. By using motifs of sizes 3, 4, and 5, our approach captures a high level of detail about the graph topology within the neighborhood of the pair of nodes, which leads to a higher classification accuracy. In addition to proposing the use of motif-based features, we also propose two optimizations related to constructing the classification dataset from the graph. First, to ensure that positive and negative examples are treated equally when extracting features, we propose adding the negative examples to the graph as an alternative to the common approach of removing the positive ones. Second, we show that it is important to control for the shortest-path distance when sampling pairs of nodes to form negative examples, since the difficulty of prediction varies with the shortest-path distance. We experimentally demonstrate that using off-the-shelf classifiers with a well constructed classification dataset results in up to 10 percentage points increase in accuracy over prior topology-based and feature learning methods.
Spectrum-Diverse Neuroevolution with Unified Neural Models
Authors: Danilo Vasconcellos Vargas, Junichi Murata
Date: 2019-01-06 00:00:00
Tasks: Diversity

Learning algorithms are being increasingly adopted in various applications.
However, further expansion will require methods that work more automatically.
To enable this level of automation, a more powerful solution representation is
needed. However, by increasing the representation complexity a second problem
arises. The search space becomes huge and therefore an associated scalable and
efficient searching algorithm is also required. To solve both problems, first a
powerful representation is proposed that unifies most of the neural networks
features from the literature into one representation. Secondly, a new diversity
preserving method called Spectrum Diversity is created based on the new concept
of chromosome spectrum that creates a spectrum out of the characteristics and
frequency of alleles in a chromosome. The combination of Spectrum Diversity
with a unified neuron representation enables the algorithm to either surpass or
equal NeuroEvolution of Augmenting Topologies (NEAT) on all of the five classes
of problems tested. Ablation tests justifies the good results, showing the
importance of added new features in the unified neuron representation. Part of
the success is attributed to the novelty-focused evolution and good scalability
with chromosome size provided by Spectrum Diversity. Thus, this study sheds
light on a new representation and diversity preserving mechanism that should
impact algorithms and applications to come.
  To download the code please access the following
https://github.com/zweifel/Physis-Shard.