pwc-archive/papers-with-abstracts huggingface dataset:
Leveraging Discourse Information Effectively for Authorship Attribution
Authors: Su Wang, Elisa Ferracane, Raymond J. Mooney
Date: 2017-09-07 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Authorship Attribution

We explore techniques to maximize the effectiveness of discourse information
in the task of authorship attribution. We present a novel method to embed
discourse features in a Convolutional Neural Network text classifier, which
achieves a state-of-the-art result by a substantial margin. We empirically
investigate several featurization methods to understand the conditions under
which discourse features contribute non-trivial performance gains, and analyze
discourse embeddings.
RNN-based Early Cyber-Attack Detection for the Tennessee Eastman Process
Authors: Pavel Filonov, Fedor Kitashov, Andrey Lavrentyev
Date: 2017-09-07 00:00:00
Tasks: Cyber Attack Detection, Fault Detection, Time Series, Time Series Analysis

An RNN-based forecasting approach is used to early detect anomalies in
industrial multivariate time series data from a simulated Tennessee Eastman
Process (TEP) with many cyber-attacks. This work continues a previously
proposed LSTM-based approach to the fault detection in simpler data. It is
considered necessary to adapt the RNN network to deal with data containing
stochastic, stationary, transitive and a rich variety of anomalous behaviours.
There is particular focus on early detection with special NAB-metric. A
comparison with the DPCA approach is provided. The generated data set is made
publicly available.
FingerNet: An Unified Deep Network for Fingerprint Minutiae Extraction
Authors: Yao Tang, Fei Gao, Jufu Feng, Yuhang Liu
Date: 2017-09-07 00:00:00

Minutiae extraction is of critical importance in automated fingerprint
recognition. Previous works on rolled/slap fingerprints failed on latent
fingerprints due to noisy ridge patterns and complex background noises. In this
paper, we propose a new way to design deep convolutional network combining
domain knowledge and the representation ability of deep learning. In terms of
orientation estimation, segmentation, enhancement and minutiae extraction,
several typical traditional methods performed well on rolled/slap fingerprints
are transformed into convolutional manners and integrated as an unified plain
network. We demonstrate that this pipeline is equivalent to a shallow network
with fixed weights. The network is then expanded to enhance its representation
ability and the weights are released to learn complex background variance from
data, while preserving end-to-end differentiability. Experimental results on
NIST SD27 latent database and FVC 2004 slap database demonstrate that the
proposed algorithm outperforms the state-of-the-art minutiae extraction
algorithms. Code is made publicly available at:
https://github.com/felixTY/FingerNet.
Rational coordination with no communication or conventions
Authors: Valentin Goranko, Antti Kuusisto, Raine Rönnholm
Date: 2017-06-22 00:00:00

We study pure coordination games where in every outcome, all players have identical payoffs, 'win' or 'lose'. We identify and discuss a range of 'purely rational principles' guiding the reasoning of rational players in such games and analyze which classes of coordination games can be solved by such players with no preplay communication or conventions. We observe that it is highly nontrivial to delineate a boundary between purely rational principles and other decision methods, such as conventions, for solving such coordination games.
Latent fingerprint minutia extraction using fully convolutional network
Authors: Yao Tang, Fei Gao, Jufu Feng
Date: 2016-09-30 00:00:00
Tasks: GPU

Minutiae play a major role in fingerprint identification. Extracting reliable
minutiae is difficult for latent fingerprints which are usually of poor
quality. As the limitation of traditional handcrafted features, a fully
convolutional network (FCN) is utilized to learn features directly from data to
overcome complex background noises. Raw fingerprints are mapped to a
correspondingly-sized minutia-score map with a fixed stride. And thus a large
number of minutiae will be extracted through a given threshold. Then small
regions centering at these minutia points are entered into a convolutional
neural network (CNN) to reclassify these minutiae and calculate their
orientations. The CNN shares convolutional layers with the fully convolutional
network to speed up. 0.45 second is used on average to detect one fingerprint
on a GPU. On the NIST SD27 database, we achieve 53\% recall rate and 53\%
precise rate that outperform many other algorithms. Our trained model is also
visualized to show that we have successfully extracted features preserving
ridge information of a latent fingerprint.
Approximating meta-heuristics with homotopic recurrent neural networks
Authors: Alessandro Bay, Biswa Sengupta
Date: 2017-09-07 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

Much combinatorial optimisation problems constitute a non-polynomial (NP)
hard optimisation problem, i.e., they can not be solved in polynomial time. One
such problem is finding the shortest route between two nodes on a graph.
Meta-heuristic algorithms such as $A^{*}$ along with mixed-integer programming
(MIP) methods are often employed for these problems. Our work demonstrates that
it is possible to approximate solutions generated by a meta-heuristic algorithm
using a deep recurrent neural network. We compare different methodologies based
on reinforcement learning (RL) and recurrent neural networks (RNN) to gauge
their respective quality of approximation. We show the viability of recurrent
neural network solutions on a graph that has over 300 nodes and argue that a
sequence-to-sequence network rather than other recurrent networks has improved
approximation quality. Additionally, we argue that homotopy continuation --
that increases chances of hitting an extremum -- further improves the estimate
generated by a vanilla RNN.
Translating Terminological Expressions in Knowledge Bases with Neural Machine Translation
Authors: Mihael Arcan, Daniel Torregrosa, Paul Buitelaar
Date: 2017-09-07 00:00:00
Tasks: Domain Adaptation, Knowledge Graphs, Machine Translation, Translation

Our work presented in this paper focuses on the translation of terminological expressions represented in semantically structured resources, like ontologies or knowledge graphs. The challenge of translating ontology labels or terminological expressions documented in knowledge bases lies in the highly specific vocabulary and the lack of contextual information, which can guide a machine translation system to translate ambiguous words into the targeted domain. Due to these challenges, we evaluate the translation quality of domain-specific expressions in the medical and financial domain with statistical as well as with neural machine translation methods and experiment domain adaptation of the translation models with terminological expressions only. Furthermore, we perform experiments on the injection of external terminological expressions into the translation systems. Through these experiments, we observed a significant advantage in domain adaptation for the domain-specific resource in the medical and financial domain and the benefit of subword models over word-based neural machine translation models for terminology translation.
Semantic Preserving Embeddings for Generalized Graphs
Authors: Pedro Almagro-Blanco, Fernando Sancho-Caparrini
Date: 2017-09-07 00:00:00
Tasks: BIG-bench Machine Learning, Entity Retrieval, Retrieval

A new approach to the study of Generalized Graphs as semantic data structures
using machine learning techniques is presented. We show how vector
representations maintaining semantic characteristics of the original data can
be obtained from a given graph using neural encoding architectures and
considering the topological properties of the graph. Semantic features of these
new representations are tested by using some machine learning tasks and new
directions on efficient link discovery, entitity retrieval and long distance
query methodologies on large relational datasets are investigated using real
datasets.
  ----
  En este trabajo se presenta un nuevo enfoque en el contexto del aprendizaje
autom\'atico multi-relacional para el estudio de Grafos Generalizados. Se
muestra c\'omo se pueden obtener representaciones vectoriales que mantienen
caracter\'isticas sem\'anticas del grafo original utilizando codificadores
neuronales y considerando las propiedades topol\'ogicas del grafo. Adem\'as, se
eval\'uan las caracter\'isticas sem\'anticas capturadas por estas nuevas
representaciones y se investigan nuevas metodolog\'ias eficientes relacionadas
con Link Discovery, Entity Retrieval y consultas a larga distancia en grandes
conjuntos de datos relacionales haciendo uso de bases de datos reales.
Self-Normalizing Neural Networks
Authors: Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter
Date: 2017-06-08 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Astronomy, BIG-bench Machine Learning, Drug Discovery, Pulsar Prediction

Deep Learning has revolutionized vision via convolutional neural networks
(CNNs) and natural language processing via recurrent neural networks (RNNs).
However, success stories of Deep Learning with standard feed-forward neural
networks (FNNs) are rare. FNNs that perform well are typically shallow and,
therefore cannot exploit many levels of abstract representations. We introduce
self-normalizing neural networks (SNNs) to enable high-level abstract
representations. While batch normalization requires explicit normalization,
neuron activations of SNNs automatically converge towards zero mean and unit
variance. The activation function of SNNs are "scaled exponential linear units"
(SELUs), which induce self-normalizing properties. Using the Banach fixed-point
theorem, we prove that activations close to zero mean and unit variance that
are propagated through many network layers will converge towards zero mean and
unit variance -- even under the presence of noise and perturbations. This
convergence property of SNNs allows to (1) train deep networks with many
layers, (2) employ strong regularization, and (3) to make learning highly
robust. Furthermore, for activations not close to unit variance, we prove an
upper and lower bound on the variance, thus, vanishing and exploding gradients
are impossible. We compared SNNs on (a) 121 tasks from the UCI machine learning
repository, on (b) drug discovery benchmarks, and on (c) astronomy tasks with
standard FNNs and other machine learning methods such as random forests and
support vector machines. SNNs significantly outperformed all competing FNN
methods at 121 UCI tasks, outperformed all competing methods at the Tox21
dataset, and set a new record at an astronomy data set. The winning SNN
architectures are often very deep. Implementations are available at:
github.com/bioinf-jku/SNNs.
Interacting Attention-gated Recurrent Networks for Recommendation
Authors: Wenjie Pei, Jie Yang, Zhu Sun, Jie Zhang, Alessandro Bozzon, David M. J. Tax
Date: 2017-09-05 00:00:00

Capturing the temporal dynamics of user preferences over items is important
for recommendation. Existing methods mainly assume that all time steps in
user-item interaction history are equally relevant to recommendation, which
however does not apply in real-world scenarios where user-item interactions can
often happen accidentally. More importantly, they learn user and item dynamics
separately, thus failing to capture their joint effects on user-item
interactions. To better model user and item dynamics, we present the
Interacting Attention-gated Recurrent Network (IARN) which adopts the attention
model to measure the relevance of each time step. In particular, we propose a
novel attention scheme to learn the attention scores of user and item history
in an interacting way, thus to account for the dependencies between user and
item dynamics in shaping user-item interactions. By doing so, IARN can
selectively memorize different time steps of a user's history when predicting
her preferences over different items. Our model can therefore provide
meaningful interpretations for recommendation results, which could be further
enhanced by auxiliary features. Extensive validation on real-world datasets
shows that IARN consistently outperforms state-of-the-art methods.
Real-time convolutional networks for sonar image classification in low-power embedded systems
Authors: Matias Valdenegro-Toro
Date: 2017-09-07 00:00:00
Tasks: General Classification, image-classification, Image Classification

Deep Neural Networks have impressive classification performance, but this
comes at the expense of significant computational resources at inference time.
Autonomous Underwater Vehicles use low-power embedded systems for sonar image
perception, and cannot execute large neural networks in real-time. We propose
the use of max-pooling aggressively, and we demonstrate it with a Fire-based
module and a new Tiny module that includes max-pooling in each module. By
stacking them we build networks that achieve the same accuracy as bigger ones,
while reducing the number of parameters and considerably increasing
computational performance. Our networks can classify a 96x96 sonar image with
98.8 - 99.7 accuracy on only 41 to 61 milliseconds on a Raspberry Pi 2, which
corresponds to speedups of 28.6 - 19.7.
Improving Sonar Image Patch Matching via Deep Learning
Authors: Matias Valdenegro-Toro
Date: 2017-09-07 00:00:00
Tasks: Binary Classification, Deep Learning, object-detection, Object Detection, Patch Matching, Simultaneous Localization and Mapping

Matching sonar images with high accuracy has been a problem for a long time,
as sonar images are inherently hard to model due to reflections, noise and
viewpoint dependence. Autonomous Underwater Vehicles require good sonar image
matching capabilities for tasks such as tracking, simultaneous localization and
mapping (SLAM) and some cases of object detection/recognition. We propose the
use of Convolutional Neural Networks (CNN) to learn a matching function that
can be trained from labeled sonar data, after pre-processing to generate
matching and non-matching pairs. In a dataset of 39K training pairs, we obtain
0.91 Area under the ROC Curve (AUC) for a CNN that outputs a binary
classification matching decision, and 0.89 AUC for another CNN that outputs a
matching score. In comparison, classical keypoint matching methods like SIFT,
SURF, ORB and AKAZE obtain AUC 0.61 to 0.68. Alternative learning methods
obtain similar results, with a Random Forest Classifier obtaining AUC 0.79, and
a Support Vector Machine resulting in AUC 0.66.
Rotational Subgroup Voting and Pose Clustering for Robust 3D Object Recognition
Authors: Anders Glent Buch, Lilita Kiforenko, Dirk Kraft
Date: 2017-09-07 00:00:00
Proceeding: ICCV 2017 10
Tasks: 3D Object Recognition, Clustering, Density Estimation, Object Recognition, Pose Estimation

It is possible to associate a highly constrained subset of relative 6 DoF
poses between two 3D shapes, as long as the local surface orientation, the
normal vector, is available at every surface point. Local shape features can be
used to find putative point correspondences between the models due to their
ability to handle noisy and incomplete data. However, this correspondence set
is usually contaminated by outliers in practical scenarios, which has led to
many past contributions based on robust detectors such as the Hough transform
or RANSAC. The key insight of our work is that a single correspondence between
oriented points on the two models is constrained to cast votes in a 1 DoF
rotational subgroup of the full group of poses, SE(3). Kernel density
estimation allows combining the set of votes efficiently to determine a full 6
DoF candidate pose between the models. This modal pose with the highest density
is stable under challenging conditions, such as noise, clutter, and occlusions,
and provides the output estimate of our method.
  We first analyze the robustness of our method in relation to noise and show
that it handles high outlier rates much better than RANSAC for the task of 6
DoF pose estimation. We then apply our method to four state of the art data
sets for 3D object recognition that contain occluded and cluttered scenes. Our
method achieves perfect recall on two LIDAR data sets and outperforms competing
methods on two RGB-D data sets, thus setting a new standard for general 3D
object recognition using point cloud data.
To Learn or Not to Learn Features for Deformable Registration?
Authors: Aabhas Majumdar, Raghav Mehta, Jayanthi Sivaswamy
Date: 2017-09-04 00:00:00

Feature-based registration has been popular with a variety of features
ranging from voxel intensity to Self-Similarity Context (SSC). In this paper,
we examine the question on how features learnt using various Deep Learning (DL)
frameworks can be used for deformable registration and whether this feature
learning is necessary or not. We investigate the use of features learned by
different DL methods in the current state-of-the-art discrete registration
framework and analyze its performance on 2 publicly available datasets. We draw
insights into the type of DL framework useful for feature learning and the
impact, if any, of the complexity of different DL models and brain parcellation
methods on the performance of discrete registration. Our results indicate that
the registration performance with DL features and SSC are comparable and stable
across datasets whereas this does not hold for low level features.
Proceedings First Workshop on Formal Verification of Autonomous Vehicles
Authors: Lukas Bulwahn, Maryam Kamali, Sven Linker
Date: 2017-09-07 00:00:00
Tasks: Autonomous Vehicles

These are the proceedings of the workshop on Formal Verification of
Autonomous Vehicles, held on September 19th, 2017 in Turin, Italy, as an
affiliated workshop of the International Conference on integrated Formal
Methods (iFM 2017). The workshop aim is to bring together researchers from the
formal verification community that are developing formal methods for autonomous
vehicles as well as researchers working, e.g., in the area of control theory or
robotics, interested in applying verification techniques for designing and
developing of autonomous vehicles.
Integrating Specialized Classifiers Based on Continuous Time Markov Chain
Authors: Zhizhong Li, Dahua Lin
Date: 2017-09-07 00:00:00

Specialized classifiers, namely those dedicated to a subset of classes, are
often adopted in real-world recognition systems. However, integrating such
classifiers is nontrivial. Existing methods, e.g. weighted average, usually
implicitly assume that all constituents of an ensemble cover the same set of
classes. Such methods can produce misleading predictions when used to combine
specialized classifiers. This work explores a novel approach. Instead of
combining predictions from individual classifiers directly, it first decomposes
the predictions into sets of pairwise preferences, treating them as transition
channels between classes, and thereon constructs a continuous-time Markov
chain, and use the equilibrium distribution of this chain as the final
prediction. This way allows us to form a coherent picture over all specialized
predictions. On large public datasets, the proposed method obtains considerable
improvement compared to mainstream ensemble methods, especially when the
classifier coverage is highly unbalanced.
Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors
Authors: Yu Zhao, Rennong Yang, Guillaume Chevalier, Maoguo Gong
Date: 2017-08-22 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

Human activity recognition (HAR) has become a popular topic in research
because of its wide application. With the development of deep learning, new
ideas have appeared to address HAR problems. Here, a deep network architecture
using residual bidirectional long short-term memory (LSTM) cells is proposed.
The advantages of the new network include that a bidirectional connection can
concatenate the positive time direction (forward state) and the negative time
direction (backward state). Second, residual connections between stacked cells
act as highways for gradients, which can pass underlying information directly
to the upper layer, effectively avoiding the gradient vanishing problem.
Generally, the proposed network shows improvements on both the temporal (using
bidirectional cells) and the spatial (residual connections stacked deeply)
dimensions, aiming to enhance the recognition rate. When tested with the
Opportunity data set and the public domain UCI data set, the accuracy was
increased by 4.78% and 3.68%, respectively, compared with previously reported
results. Finally, the confusion matrix of the public domain UCI data set was
analyzed.
Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario
Authors: Jill-Jênn Vie, Florian Yger, Ryan Lahfa, Basile Clement, Kévin Cocchi, Thomas Chalumeau, Hisashi Kashima
Date: 2017-09-03 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems, TAG

Item cold-start is a classical issue in recommender systems that affects
anime and manga recommendations as well. This problem can be framed as follows:
how to predict whether a user will like a manga that received few ratings from
the community? Content-based techniques can alleviate this issue but require
extra information, that is usually expensive to gather. In this paper, we use a
deep learning technique, Illustration2Vec, to easily extract tag information
from the manga and anime posters (e.g., sword, or ponytail). We propose BALSE
(Blended Alternate Least Squares with Explanation), a new model for
collaborative filtering, that benefits from this extra information to recommend
mangas. We show, using real data from an online manga recommender system called
Mangaki, that our model improves substantially the quality of recommendations,
especially for less-known manga, and is able to provide an interpretation of
the taste of the users.
Use Privacy in Data-Driven Systems: Theory and Experiments with Machine Learnt Programs
Authors: Anupam Datta, Matthew Fredrikson, Gihyuk Ko, Piotr Mardziel, Shayak Sen
Date: 2017-05-22 00:00:00
Tasks: General Classification

This paper presents an approach to formalizing and enforcing a class of use
privacy properties in data-driven systems. In contrast to prior work, we focus
on use restrictions on proxies (i.e. strong predictors) of protected
information types. Our definition relates proxy use to intermediate
computations that occur in a program, and identify two essential properties
that characterize this behavior: 1) its result is strongly associated with the
protected information type in question, and 2) it is likely to causally affect
the final output of the program. For a specific instantiation of this
definition, we present a program analysis technique that detects instances of
proxy use in a model, and provides a witness that identifies which parts of the
corresponding program exhibit the behavior. Recognizing that not all instances
of proxy use of a protected information type are inappropriate, we make use of
a normative judgment oracle that makes this inappropriateness determination for
a given witness. Our repair algorithm uses the witness of an inappropriate
proxy use to transform the model into one that provably does not exhibit proxy
use, while avoiding changes that unduly affect classification accuracy. Using a
corpus of social datasets, our evaluation shows that these algorithms are able
to detect proxy use instances that would be difficult to find using existing
techniques, and subsequently remove them while maintaining acceptable
classification performance.