pwc-archive/papers-with-abstracts huggingface dataset:
Building a Knowledge Graph from Natural Language Definitions for Interpretable Text Entailment Recognition
Authors: Vivian S. Silva, André Freitas, Siegfried Handschuh
Date: 2018-06-20 00:00:00
Proceeding: LREC 2018 5
Tasks: World Knowledge

Natural language definitions of terms can serve as a rich source of
knowledge, but structuring them into a comprehensible semantic model is
essential to enable them to be used in semantic interpretation tasks. We
propose a method and provide a set of tools for automatically building a graph
world knowledge base from natural language definitions. Adopting a conceptual
model composed of a set of semantic roles for dictionary definitions, we
trained a classifier for automatically labeling definitions, preparing the data
to be later converted to a graph representation. WordNetGraph, a knowledge
graph built out of noun and verb WordNet definitions according to this
methodology, was successfully used in an interpretable text entailment
recognition approach which uses paths in this graph to provide clear
justifications for entailment decisions.
Combinatorial Testing for Deep Learning Systems
Authors: Lei Ma, Fuyuan Zhang, Minhui Xue, Bo Li, Yang Liu, Jianjun Zhao, Yadong Wang
Date: 2018-06-20 00:00:00
Tasks: Deep Learning, Defect Detection

Deep learning (DL) has achieved remarkable progress over the past decade and
been widely applied to many safety-critical applications. However, the
robustness of DL systems recently receives great concerns, such as adversarial
examples against computer vision systems, which could potentially result in
severe consequences. Adopting testing techniques could help to evaluate the
robustness of a DL system and therefore detect vulnerabilities at an early
stage. The main challenge of testing such systems is that its runtime state
space is too large: if we view each neuron as a runtime state for DL, then a DL
system often contains massive states, rendering testing each state almost
impossible. For traditional software, combinatorial testing (CT) is an
effective testing technique to reduce the testing space while obtaining
relatively high defect detection abilities. In this paper, we perform an
exploratory study of CT on DL systems. We adapt the concept in CT and propose a
set of coverage criteria for DL systems, as well as a CT coverage guided test
generation technique. Our evaluation demonstrates that CT provides a promising
avenue for testing DL systems. We further pose several open questions and
interesting directions for combinatorial testing of DL systems.
Semantic Relation Classification: Task Formalisation and Refinement
Authors: Vivian S. Silva, Manuela Hürliman, Brian Davis, Siegfried Handschuh, André Freitas
Date: 2018-06-20 00:00:00
Proceeding: WS 2016 12
Tasks: Classification, General Classification, Relation, Relation Classification

The identification of semantic relations between terms within texts is a
fundamental task in Natural Language Processing which can support applications
requiring a lightweight semantic interpretation model. Currently, semantic
relation classification concentrates on relations which are evaluated over
open-domain data. This work provides a critique on the set of abstract
relations used for semantic relation classification with regard to their
ability to express relationships between terms which are found in a
domain-specific corpora. Based on this analysis, this work proposes an
alternative semantic relation model based on reusing and extending the set of
abstract relations present in the DOLCE ontology. The resulting set of
relations is well grounded, allows to capture a wide range of relations and
could thus be used as a foundation for automatic classification of semantic
relations.
Weighted Abstract Dialectical Frameworks: Extended and Revised Report
Authors: Gerhard Brewka, Jörg Pührer, Hannes Strass, Johannes P. Wallner, Stefan Woltran
Date: 2018-06-20 00:00:00

Abstract Dialectical Frameworks (ADFs) generalize Dung's argumentation
frameworks allowing various relationships among arguments to be expressed in a
systematic way. We further generalize ADFs so as to accommodate arbitrary
acceptance degrees for the arguments. This makes ADFs applicable in domains
where both the initial status of arguments and their relationship are only
insufficiently specified by Boolean functions. We define all standard ADF
semantics for the weighted case, including grounded, preferred and stable
semantics. We illustrate our approach using acceptance degrees from the unit
interval and show how other valuation structures can be integrated. In each
case it is sufficient to specify how the generalized acceptance conditions are
represented by formulas, and to specify the information ordering underlying the
characteristic ADF operator. We also present complexity results for problems
related to weighted ADFs.
Using Neural Network for Identifying Clickbaits in Online News Media
Authors: Amin Omidvar, Hui Jiang, Aijun An
Date: 2018-06-20 00:00:00
Tasks: Clickbait Detection

Online news media sometimes use misleading headlines to lure users to open
the news article. These catchy headlines that attract users but disappointed
them at the end, are called Clickbaits. Because of the importance of automatic
clickbait detection in online medias, lots of machine learning methods were
proposed and employed to find the clickbait headlines. In this research, a
model using deep learning methods is proposed to find the clickbaits in
Clickbait Challenge 2017's dataset. The proposed model gained the first rank in
the Clickbait Challenge 2017 in terms of Mean Squared Error. Also, data
analytics and visualization techniques are employed to explore and discover the
provided dataset to get more insight from the data.
Categorization of Semantic Roles for Dictionary Definitions
Authors: Vivian S. Silva, Siegfried Handschuh, André Freitas
Date: 2018-06-20 00:00:00
Proceeding: WS 2016 12

Understanding the semantic relationships between terms is a fundamental task
in natural language processing applications. While structured resources that
can express those relationships in a formal way, such as ontologies, are still
scarce, a large number of linguistic resources gathering dictionary definitions
is becoming available, but understanding the semantic structure of natural
language definitions is fundamental to make them useful in semantic
interpretation tasks. Based on an analysis of a subset of WordNet's glosses, we
propose a set of semantic roles that compose the semantic structure of a
dictionary definition, and show how they are related to the definition's
syntactic configuration, identifying patterns that can be used in the
development of information extraction frameworks and semantic models.
Word Tagging with Foundational Ontology Classes: Extending the WordNet-DOLCE Mapping to Verbs
Authors: Vivian S. Silva, André Freitas, Siegfried Handschuh
Date: 2018-06-20 00:00:00

Semantic annotation is fundamental to deal with large-scale lexical
information, mapping the information to an enumerable set of categories over
which rules and algorithms can be applied, and foundational ontology classes
can be used as a formal set of categories for such tasks. A previous alignment
between WordNet noun synsets and DOLCE provided a starting point for
ontology-based annotation, but in NLP tasks verbs are also of substantial
importance. This work presents an extension to the WordNet-DOLCE noun mapping,
aligning verbs according to their links to nouns denoting perdurants,
transferring to the verb the DOLCE class assigned to the noun that best
represents that verb's occurrence. To evaluate the usefulness of this resource,
we implemented a foundational ontology-based semantic annotation framework,
that assigns a high-level foundational category to each word or phrase in a
text, and compared it to a similar annotation tool, obtaining an increase of
9.05% in accuracy.
Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification
Authors: Zhao Kang, Xiao Lu, Jin-Feng Yi, Zenglin Xu
Date: 2018-06-20 00:00:00
Tasks: Clustering, General Classification

Multiple kernel learning (MKL) method is generally believed to perform better
than single kernel method. However, some empirical studies show that this is
not always true: the combination of multiple kernels may even yield an even
worse performance than using a single kernel. There are two possible reasons
for the failure: (i) most existing MKL methods assume that the optimal kernel
is a linear combination of base kernels, which may not hold true; and (ii) some
kernel weights are inappropriately assigned due to noises and carelessly
designed algorithms. In this paper, we propose a novel MKL framework by
following two intuitive assumptions: (i) each kernel is a perturbation of the
consensus kernel; and (ii) the kernel that is close to the consensus kernel
should be assigned a large weight. Impressively, the proposed method can
automatically assign an appropriate weight to each kernel without introducing
additional parameters, as existing methods do. The proposed framework is
integrated into a unified framework for graph-based clustering and
semi-supervised classification. We have conducted experiments on multiple
benchmark datasets and our empirical results verify the superiority of the
proposed framework.
Reinforcement Learning using Augmented Neural Networks
Authors: Jack Shannon, Marek Grzes
Date: 2018-06-20 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Neural networks allow Q-learning reinforcement learning agents such as deep
Q-networks (DQN) to approximate complex mappings from state spaces to value
functions. However, this also brings drawbacks when compared to other function
approximators such as tile coding or their generalisations, radial basis
functions (RBF) because they introduce instability due to the side effect of
globalised updates present in neural networks. This instability does not even
vanish in neural networks that do not have any hidden layers. In this paper, we
show that simple modifications to the structure of the neural network can
improve stability of DQN learning when a multi-layer perceptron is used for
function approximation.
Non-Parametric Calibration of Probabilistic Regression
Authors: Hao Song, Meelis Kull, Peter Flach
Date: 2018-06-20 00:00:00
Tasks: General Classification, regression

The task of calibration is to retrospectively adjust the outputs from a
machine learning model to provide better probability estimates on the target
variable. While calibration has been investigated thoroughly in classification,
it has not yet been well-established for regression tasks. This paper considers
the problem of calibrating a probabilistic regression model to improve the
estimated probability densities over the real-valued targets. We propose to
calibrate a regression model through the cumulative probability density, which
can be derived from calibrating a multi-class classifier. We provide three
non-parametric approaches to solve the problem, two of which provide empirical
estimates and the third providing smooth density estimates. The proposed
approaches are experimentally evaluated to show their ability to improve the
performance of regression models on the predictive likelihood.
Automated Fact Checking: Task formulations, methods and future directions
Authors: James Thorne, Andreas Vlachos
Date: 2018-06-20 00:00:00
Proceeding: COLING 2018 8
Tasks: Articles, Fact Checking, Misinformation

The recently increased focus on misinformation has stimulated research in
fact checking, the task of assessing the truthfulness of a claim. Research in
automating this task has been conducted in a variety of disciplines including
natural language processing, machine learning, knowledge representation,
databases, and journalism. While there has been substantial progress, relevant
papers and articles have been published in research communities that are often
unaware of each other and use inconsistent terminology, thus impeding
understanding and further progress. In this paper we survey automated fact
checking research stemming from natural language processing and related
disciplines, unifying the task formulations and methodologies across papers and
authors. Furthermore, we highlight the use of evidence as an important
distinguishing factor among them cutting across task formulations and methods.
We conclude with proposing avenues for future NLP research on automated fact
checking.
DEFRAG: Deep Euclidean Feature Representations through Adaptation on the Grassmann Manifold
Authors: Breton Minnehan, Andreas Savakis
Date: 2018-06-20 00:00:00
Tasks: Clustering, General Classification

We propose a novel technique for training deep networks with the objective of
obtaining feature representations that exist in a Euclidean space and exhibit
strong clustering behavior. Our desired features representations have three
traits: they can be compared using a standard Euclidian distance metric,
samples from the same class are tightly clustered, and samples from different
classes are well separated. However, most deep networks do not enforce such
feature representations. The DEFRAG training technique consists of two steps:
first good feature clustering behavior is encouraged though an auxiliary loss
function based on the Silhouette clustering metric. Then the feature space is
retracted onto a Grassmann manifold to ensure that the L_2 Norm forms a
similarity metric. The DEFRAG technique achieves state of the art results on
standard classification datasets using a relatively small network architecture
with significantly fewer parameters than many standard networks.
Dynamic voting in multi-view learning for radiomics applications
Authors: Hongliu Cao, Simon Bernard, Laurent Heutte, Robert Sabourin
Date: 2018-06-20 00:00:00
Tasks: MULTI-VIEW LEARNING

Cancer diagnosis and treatment often require a personalized analysis for each
patient nowadays, due to the heterogeneity among the different types of tumor
and among patients. Radiomics is a recent medical imaging field that has shown
during the past few years to be promising for achieving this personalization.
However, a recent study shows that most of the state-of-the-art works in
Radiomics fail to identify this problem as a multi-view learning task and that
multi-view learning techniques are generally more efficient. In this work, we
propose to further investigate the potential of one family of multi-view
learning methods based on Multiple Classifiers Systems where one classifier is
learnt on each view and all classifiers are combined afterwards. In particular,
we propose a random forest based dynamic weighted voting scheme, which
personalizes the combination of views for each new patient for classification
tasks. The proposed method is validated on several real-world Radiomics
problems.
Approximation by filter functions
Authors: Ivo Düntsch, Günther Gediga, Hui Wang
Date: 2018-06-20 00:00:00

In this exploratory article, we draw attention to the common formal ground
among various estimators such as the belief functions of evidence theory and
their relatives, approximation quality of rough set theory, and contextual
probability. The unifying concept will be a general filter function composed of
a basic probability and a weighting which varies according to the problem at
hand. To compare the various filter functions we conclude with a simulation
study with an example from the area of item response theory.
Cross-Domain Deep Face Matching for Real Banking Security Systems
Authors: Johnatan S. Oliveira, Gustavo B. Souza, Anderson R. Rocha, Flávio E. Deus, Aparecido N. Marana
Date: 2018-06-20 00:00:00

Ensuring the security of transactions is currently one of the major challenges that banking systems deal with. The usage of face for biometric authentication of users is attracting large investments from banks worldwide due to its convenience and acceptability by people, especially in cross-domain scenarios, in which facial images from ID documents are compared with digital self-portraits (selfies) for the automated opening of new checking accounts, e.g, or financial transactions authorization. Actually, the comparison of selfies and IDs has also been applied in another wide variety of tasks nowadays, such as automated immigration control. The major difficulty in such process consists in attenuating the differences between the facial images compared given their different domains. In this work, in addition to collecting a large cross-domain face dataset, with 27,002 real facial images of selfies and ID documents (13,501 subjects) captured from the databases of the major public Brazilian bank, we propose a novel architecture for such cross-domain matching problem based on deep features extracted by two well-referenced Convolutional Neural Networks (CNN). Results obtained on the dataset collected, called FaceBank, with accuracy rates higher than 93%, demonstrate the robustness of the proposed approach to the cross-domain face matching problem and its feasible application in real banking security systems.
Skilled Experience Catalogue: A Skill-Balancing Mechanism for Non-Player Characters using Reinforcement Learning
Authors: Frank G. Glavin, Michael G. Madden
Date: 2018-06-20 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we introduce a skill-balancing mechanism for adversarial
non-player characters (NPCs), called Skilled Experience Catalogue (SEC). The
objective of this mechanism is to approximately match the skill level of an NPC
to an opponent in real-time. We test the technique in the context of a
First-Person Shooter (FPS) game. Specifically, the technique adjusts a
reinforcement learning NPC's proficiency with a weapon based on its current
performance against an opponent. Firstly, a catalogue of experience, in the
form of stored learning policies, is built up by playing a series of training
games. Once the NPC has been sufficiently trained, the catalogue acts as a
timeline of experience with incremental knowledge milestones in the form of
stored learning policies. If the NPC is performing poorly, it can jump to a
later stage in the learning timeline to be equipped with more informed
decision-making. Likewise, if it is performing significantly better than the
opponent, it will jump to an earlier stage. The NPC continues to learn in
real-time using reinforcement learning but its policy is adjusted, as required,
by loading the most suitable milestones for the current circumstances.
Dynamic Risk Assessment for Vehicles of Higher Automation Levels by Deep Learning
Authors: Patrik Feth, Mohammed Naveed Akram, René Schuster, Oliver Wasenmüller
Date: 2018-06-20 00:00:00

Vehicles of higher automation levels require the creation of situation
awareness. One important aspect of this situation awareness is an understanding
of the current risk of a driving situation. In this work, we present a novel
approach for the dynamic risk assessment of driving situations based on images
of a front stereo camera using deep learning. To this end, we trained a deep
neural network with recorded monocular images, disparity maps and a risk metric
for diverse traffic scenes. Our approach can be used to create the
aforementioned situation awareness of vehicles of higher automation levels and
can serve as a heterogeneous channel to systems based on radar or lidar sensors
that are used traditionally for the calculation of risk metrics.
Deep Similarity Metric Learning for Real-Time Pedestrian Tracking
Authors: Michael Thoreau, Navinda Kottege
Date: 2018-06-20 00:00:00
Tasks: Metric Learning, Multiple Object Tracking, Object Tracking, Person Re-Identification

Tracking by detection is a common approach to solving the Multiple Object Tracking problem. In this paper we show how learning a deep similarity metric can improve three key aspects of pedestrian tracking on a multiple object tracking benchmark. We train a convolutional neural network to learn an embedding function in a Siamese configuration on a large person re-identification dataset. The offline-trained embedding network is integrated in to the tracking formulation to improve performance while retaining real-time performance. The proposed tracker stores appearance metrics while detections are strong, using this appearance information to: prevent ID switches, associate tracklets through occlusion, and propose new detections where detector confidence is low. This method achieves competitive results in evaluation, especially among online, real-time approaches. We present an ablative study showing the impact of each of the three uses of our deep appearance metric.
A CADe System for Gliomas in Brain MRI using Convolutional Neural Networks
Authors: Subhasis Banerjee, Sushmita Mitra, Anmol Sharma, B. Uma Shankar
Date: 2018-06-20 00:00:00
Tasks: Segmentation, Tumor Segmentation

Inspired by the success of Convolutional Neural Networks (CNN), we develop a
novel Computer Aided Detection (CADe) system using CNN for Glioblastoma
Multiforme (GBM) detection and segmentation from multi channel MRI data. A
two-stage approach first identifies the presence of GBM. This is followed by a
GBM localization in each "abnormal" MR slice. As part of the CADe system, two
CNN architectures viz. Classification CNN (C-CNN) and Detection CNN (D-CNN) are
employed. The CADe system considers MRI data consisting of four sequences
($T_1$, $T_{1c},$ $T_2$, and $T_{2FLAIR}$) as input, and automatically
generates the bounding boxes encompassing the tumor regions in each slice which
is deemed abnormal. Experimental results demonstrate that the proposed CADe
system, when used as a preliminary step before segmentation, can allow improved
delineation of tumor region while reducing false positives arising in normal
areas of the brain. The GrowCut method, employed for tumor segmentation,
typically requires a foreground and background seed region for initialization.
Here the algorithm is initialized with seeds automatically generated from the
output of the proposed CADe system, thereby resulting in improved performance
as compared to that using random seeds.
Classifying Object Manipulation Actions based on Grasp-types and Motion-Constraints
Authors: Kartik Gupta, Darius Burschka, Arnav Bhavsar
Date: 2018-06-20 00:00:00
Tasks: Action Recognition, Object, Temporal Action Localization

In this work, we address a challenging problem of fine-grained and
coarse-grained recognition of object manipulation actions. Due to the
variations in geometrical and motion constraints, there are different
manipulations actions possible to perform different sets of actions with an
object. Also, there are subtle movements involved to complete most of object
manipulation actions. This makes the task of object manipulation action
recognition difficult with only just the motion information. We propose to use
grasp and motion-constraints information to recognise and understand action
intention with different objects. We also provide an extensive experimental
evaluation on the recent Yale Human Grasping dataset consisting of large set of
455 manipulation actions. The evaluation involves a) Different contemporary
multi-class classifiers, and binary classifiers with one-vs-one multi- class
voting scheme, b) Differential comparisons results based on subsets of
attributes involving information of grasp and motion-constraints, c)
Fine-grained and Coarse-grained object manipulation action recognition based on
fine-grained as well as coarse-grained grasp type information, and d)
Comparison between Instance level and Sequence level modeling of object
manipulation actions. Our results justifies the efficacy of grasp attributes
for the task of fine-grained and coarse-grained object manipulation action
recognition.