pwc-archive/papers-with-abstracts huggingface dataset:
Longitudinal Study of Child Face Recognition
Authors: Debayan Deb, Neeta Nain, Anil K. Jain
Date: 2017-11-10 00:00:00
Tasks: Face Recognition

We present a longitudinal study of face recognition performance on Children
Longitudinal Face (CLF) dataset containing 3,682 face images of 919 subjects,
in the age group [2, 18] years. Each subject has at least four face images
acquired over a time span of up to six years. Face comparison scores are
obtained from (i) a state-of-the-art COTS matcher (COTS-A), (ii) an open-source
matcher (FaceNet), and (iii) a simple sum fusion of scores obtained from COTS-A
and FaceNet matchers. To improve the performance of the open-source FaceNet
matcher for child face recognition, we were able to fine-tune it on an
independent training set of 3,294 face images of 1,119 children in the age
group [3, 18] years. Multilevel statistical models are fit to genuine
comparison scores from the CLF dataset to determine the decrease in face
recognition accuracy over time. Additionally, we analyze both the verification
and open-set identification accuracies in order to evaluate state-of-the-art
face recognition technology for tracing and identifying children lost at a
young age as victims of child trafficking or abduction.
EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies
Authors: Redouane Lguensat, Miao Sun, Ronan Fablet, Evan Mason, Pierre Tandeo, Ge Chen
Date: 2017-11-10 00:00:00
Tasks: Decoder, General Classification, Oceanic Eddy Classification

This work presents EddyNet, a deep learning based architecture for automated
eddy detection and classification from Sea Surface Height (SSH) maps provided
by the Copernicus Marine and Environment Monitoring Service (CMEMS). EddyNet is
a U-Net like network that consists of a convolutional encoder-decoder followed
by a pixel-wise classification layer. The output is a map with the same size of
the input where pixels have the following labels \{'0': Non eddy, '1':
anticyclonic eddy, '2': cyclonic eddy\}. We investigate the use of SELU
activation function instead of the classical ReLU+BN and we use an overlap
based loss function instead of the cross entropy loss. Keras Python code, the
training datasets and EddyNet weights files are open-source and freely
available on https://github.com/redouanelg/EddyNet.
Dynamic Analysis of Executables to Detect and Characterize Malware
Authors: Michael R. Smith, Joe B. Ingram, Christopher C. Lamb, Timothy J. Draelos, Justin E. Doak, James B. Aimone, Conrad D. James
Date: 2017-11-10 00:00:00
Tasks: BIG-bench Machine Learning

It is needed to ensure the integrity of systems that process sensitive
information and control many aspects of everyday life. We examine the use of
machine learning algorithms to detect malware using the system calls generated
by executables-alleviating attempts at obfuscation as the behavior is monitored
rather than the bytes of an executable. We examine several machine learning
techniques for detecting malware including random forests, deep learning
techniques, and liquid state machines. The experiments examine the effects of
concept drift on each algorithm to understand how well the algorithms
generalize to novel malware samples by testing them on data that was collected
after the training data. The results suggest that each of the examined machine
learning algorithms is a viable solution to detect malware-achieving between
90% and 95% class-averaged accuracy (CAA). In real-world scenarios, the
performance evaluation on an operational network may not match the performance
achieved in training. Namely, the CAA may be about the same, but the values for
precision and recall over the malware can change significantly. We structure
experiments to highlight these caveats and offer insights into expected
performance in operational environments. In addition, we use the induced models
to gain a better understanding about what differentiates the malware samples
from the goodware, which can further be used as a forensics tool to understand
what the malware (or goodware) was doing to provide directions for
investigation and remediation.
CARLA: An Open Urban Driving Simulator
Authors: Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, Vladlen Koltun
Date: 2017-11-10 00:00:00
Tasks: Autonomous Driving, Imitation Learning, Reinforcement Learning

We introduce CARLA, an open-source simulator for autonomous driving research.
CARLA has been developed from the ground up to support development, training,
and validation of autonomous urban driving systems. In addition to open-source
code and protocols, CARLA provides open digital assets (urban layouts,
buildings, vehicles) that were created for this purpose and can be used freely.
The simulation platform supports flexible specification of sensor suites and
environmental conditions. We use CARLA to study the performance of three
approaches to autonomous driving: a classic modular pipeline, an end-to-end
model trained via imitation learning, and an end-to-end model trained via
reinforcement learning. The approaches are evaluated in controlled scenarios of
increasing difficulty, and their performance is examined via metrics provided
by CARLA, illustrating the platform's utility for autonomous driving research.
The supplementary video can be viewed at https://youtu.be/Hp8Dz-Zek2E
Disentangling group and link persistence in Dynamic Stochastic Block models
Authors: Paolo Barucca, Fabrizio Lillo, Piero Mazzarisi, Daniele Tantari
Date: 2017-01-20 00:00:00
Tasks: Community Detection

We study the inference of a model of dynamic networks in which both
communities and links keep memory of previous network states. By considering
maximum likelihood inference from single snapshot observations of the network,
we show that link persistence makes the inference of communities harder,
decreasing the detectability threshold, while community persistence tends to
make it easier. We analytically show that communities inferred from single
network snapshot can share a maximum overlap with the underlying communities of
a specific previous instant in time. This leads to time-lagged inference: the
identification of past communities rather than present ones. Finally we compute
the time lag and propose a corrected algorithm, the Lagged Snapshot Dynamic
(LSD) algorithm, for community detection in dynamic networks. We analytically
and numerically characterize the detectability transitions of such algorithm as
a function of the memory parameters of the model and we make a comparison with
a full dynamic inference.
Prototype Matching Networks for Large-Scale Multi-label Genomic Sequence Classification
Authors: Jack Lanchantin, Arshdeep Sekhon, Ritambhara Singh, Yanjun Qi
Date: 2017-10-30 00:00:00
Proceeding: ICLR 2018 1
Tasks: General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

One of the fundamental tasks in understanding genomics is the problem of
predicting Transcription Factor Binding Sites (TFBSs). With more than hundreds
of Transcription Factors (TFs) as labels, genomic-sequence based TFBS
prediction is a challenging multi-label classification task. There are two
major biological mechanisms for TF binding: (1) sequence-specific binding
patterns on genomes known as "motifs" and (2) interactions among TFs known as
co-binding effects. In this paper, we propose a novel deep architecture, the
Prototype Matching Network (PMN) to mimic the TF binding mechanisms. Our PMN
model automatically extracts prototypes ("motif"-like features) for each TF
through a novel prototype-matching loss. Borrowing ideas from few-shot matching
models, we use the notion of support set of prototypes and an LSTM to learn how
TFs interact and bind to genomic sequences. On a reference TFBS dataset with
$2.1$ $million$ genomic sequences, PMN significantly outperforms baselines and
validates our design choices empirically. To our knowledge, this is the first
deep learning architecture that introduces prototype learning and considers
TF-TF interactions for large-scale TFBS prediction. Not only is the proposed
architecture accurate, but it also models the underlying biology.
D-SLATS: Distributed Simultaneous Localization and Time Synchronization
Authors: Amr Alanwar, Henrique Ferraz, Kevin Hsieh, Rohit Thazhath, Paul Martin, Joao Hespanha, Mani Srivastava
Date: 2017-11-10 00:00:00

Through the last decade, we have witnessed a surge of Internet of Things
(IoT) devices, and with that a greater need to choreograph their actions across
both time and space. Although these two problems, namely time synchronization
and localization, share many aspects in common, they are traditionally treated
separately or combined on centralized approaches that results in an ineffcient
use of resources, or in solutions that are not scalable in terms of the number
of IoT devices. Therefore, we propose D-SLATS, a framework comprised of three
different and independent algorithms to jointly solve time synchronization and
localization problems in a distributed fashion. The First two algorithms are
based mainly on the distributed Extended Kalman Filter (EKF) whereas the third
one uses optimization techniques. No fusion center is required, and the devices
only communicate with their neighbors. The proposed methods are evaluated on
custom Ultra-Wideband communication Testbed and a quadrotor, representing a
network of both static and mobile nodes. Our algorithms achieve up to three
microseconds time synchronization accuracy and 30 cm localization error.
Neural-Symbolic Learning and Reasoning: A Survey and Interpretation
Authors: Tarek R. Besold, Artur d'Avila Garcez, Sebastian Bader, Howard Bowman, Pedro Domingos, Pascal Hitzler, Kai-Uwe Kuehnberger, Luis C. Lamb, Daniel Lowd, Priscila Machado Vieira Lima, Leo de Penning, Gadi Pinkas, Hoifung Poon, Gerson Zaverucha
Date: 2017-11-10 00:00:00
Tasks: Fault Diagnosis, Philosophy, Survey

The study and understanding of human behaviour is relevant to computer
science, artificial intelligence, neural computation, cognitive science,
philosophy, psychology, and several other areas. Presupposing cognition as
basis of behaviour, among the most prominent tools in the modelling of
behaviour are computational-logic systems, connectionist models of cognition,
and models of uncertainty. Recent studies in cognitive science, artificial
intelligence, and psychology have produced a number of cognitive models of
reasoning, learning, and language that are underpinned by computation. In
addition, efforts in computer science research have led to the development of
cognitive computational systems integrating machine learning and automated
reasoning. Such systems have shown promise in a range of applications,
including computational biology, fault diagnosis, training and assessment in
simulators, and software verification. This joint survey reviews the personal
ideas and views of several researchers on neural-symbolic learning and
reasoning. The article is organised in three parts: Firstly, we frame the scope
and goals of neural-symbolic computation and have a look at the theoretical
foundations. We then proceed to describe the realisations of neural-symbolic
computation, systems, and applications. Finally we present the challenges
facing the area and avenues for further research.
Arrhythmia Classification from the Abductive Interpretation of Short Single-Lead ECG Records
Authors: Tomás Teijeiro, Constantino A. García, Daniel Castro, Paulo Félix
Date: 2017-11-10 00:00:00
Tasks: General Classification, Rhythm

In this work we propose a new method for the rhythm classification of short
single-lead ECG records, using a set of high-level and clinically meaningful
features provided by the abductive interpretation of the records. These
features include morphological and rhythm-related features that are used to
build two classifiers: one that evaluates the record globally, using aggregated
values for each feature; and another one that evaluates the record as a
sequence, using a Recurrent Neural Network fed with the individual features for
each detected heartbeat. The two classifiers are finally combined using the
stacking technique, providing an answer by means of four target classes: Normal
sinus rhythm, Atrial fibrillation, Other anomaly, and Noisy. The approach has
been validated against the 2017 Physionet/CinC Challenge dataset, obtaining a
final score of 0.83 and ranking first in the competition.
Variational Policy for Guiding Point Processes
Authors: Yichen Wang, Grady Williams, Evangelos Theodorou, Le Song
Date: 2017-01-30 00:00:00
Proceeding: ICML 2017 8
Tasks: Point Processes, Variational Inference

Temporal point processes have been widely applied to model event sequence
data generated by online users. In this paper, we consider the problem of how
to design the optimal control policy for point processes, such that the
stochastic system driven by the point process is steered to a target state. In
particular, we exploit the key insight to view the stochastic optimal control
problem from the perspective of optimal measure and variational inference. We
further propose a convex optimization framework and an efficient algorithm to
update the policy adaptively to the current system state. Experiments on
synthetic and real-world data show that our algorithm can steer the user
activities much more accurately and efficiently than other stochastic control
methods.
GPflowOpt: A Bayesian Optimization Library using TensorFlow
Authors: Nicolas Knudde, Joachim van der Herten, Tom Dhaene, Ivo Couckuyt
Date: 2017-11-10 00:00:00
Tasks: Bayesian Optimisation, Bayesian Optimization, Gaussian Processes, GPU

A novel Python framework for Bayesian optimization known as GPflowOpt is
introduced. The package is based on the popular GPflow library for Gaussian
processes, leveraging the benefits of TensorFlow including automatic
differentiation, parallelization and GPU computations for Bayesian
optimization. Design goals focus on a framework that is easy to extend with
custom acquisition functions and models. The framework is thoroughly tested and
well documented, and provides scalability. The current released version of
GPflowOpt includes some standard single-objective acquisition functions, the
state-of-the-art max-value entropy search, as well as a Bayesian
multi-objective approach. Finally, it permits easy use of custom modeling
strategies implemented in GPflow.
Deep Reinforcement Learning with Surrogate Agent-Environment Interface
Authors: Song Wang, Yu Jing
Date: 2017-09-12 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we propose surrogate agent-environment interface (SAEI) in
reinforcement learning. We also state that learning based on probability
surrogate agent-environment interface provides optimal policy of task
agent-environment interface. We introduce surrogate probability action and
develop the probability surrogate action deterministic policy gradient (PSADPG)
algorithm based on SAEI. This algorithm enables continuous control of discrete
action. The experiments show PSADPG achieves the performance of DQN in certain
tasks with the stochastic optimal policy nature in the initial training stage.
LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances
Authors: Nicolò Navarin, Beatrice Vincenzi, Mirko Polato, Alessandro Sperduti
Date: 2017-11-10 00:00:00

Predicting the completion time of business process instances would be a very
helpful aid when managing processes under service level agreement constraints.
The ability to know in advance the trend of running process instances would
allow business managers to react in time, in order to prevent delays or
undesirable situations. However, making such accurate forecasts is not easy:
many factors may influence the required time to complete a process instance. In
this paper, we propose an approach based on deep Recurrent Neural Networks
(specifically LSTMs) that is able to exploit arbitrary information associated
to single events, in order to produce an as-accurate-as-possible prediction of
the completion time of running instances. Experiments on real-world datasets
confirm the quality of our proposal.
Robotic Tactile Perception of Object Properties: A Review
Authors: Shan Luo, Joao Bimbo, Ravinder Dahiya, Hong-Bin Liu
Date: 2017-11-10 00:00:00
Tasks: Object

Touch sensing can help robots understand their sur- rounding environment, and
in particular the objects they interact with. To this end, roboticists have, in
the last few decades, developed several tactile sensing solutions, extensively
reported in the literature. Research into interpreting the conveyed tactile
information has also started to attract increasing attention in recent years.
However, a comprehensive study on this topic is yet to be reported. In an
effort to collect and summarize the major scientific achievements in the area,
this survey extensively reviews current trends in robot tactile perception of
object properties. Available tactile sensing technologies are briefly presented
before an extensive review on tactile recognition of object properties. The
object properties that are targeted by this review are shape, surface material
and object pose. The role of touch sensing in combination with other sensing
sources is also discussed. In this review, open issues are identified and
future directions for applying tactile sensing in different tasks are
suggested.
Depression Severity Estimation from Multiple Modalities
Authors: Evgeny Stepanov, Stephane Lathuiliere, Shammur Absar Chowdhury, Arindam Ghosh, Radu-Laurentiu Vieriu, Nicu Sebe, Giuseppe Riccardi
Date: 2017-11-10 00:00:00

Depression is a major debilitating disorder which can affect people from all
ages. With a continuous increase in the number of annual cases of depression,
there is a need to develop automatic techniques for the detection of the
presence and extent of depression. In this AVEC challenge we explore different
modalities (speech, language and visual features extracted from face) to design
and develop automatic methods for the detection of depression. In psychology
literature, the PHQ-8 questionnaire is well established as a tool for measuring
the severity of depression. In this paper we aim to automatically predict the
PHQ-8 scores from features extracted from the different modalities. We show
that visual features extracted from facial landmarks obtain the best
performance in terms of estimating the PHQ-8 results with a mean absolute error
(MAE) of 4.66 on the development set. Behavioral characteristics from speech
provide an MAE of 4.73. Language features yield a slightly higher MAE of 5.17.
When switching to the test set, our Turn Features derived from audio
transcriptions achieve the best performance, scoring an MAE of 4.11
(corresponding to an RMSE of 4.94), which makes our system the winner of the
AVEC 2017 depression sub-challenge.
Depth Estimation using Modified Cost Function for Occlusion Handling
Authors: Krzysztof Wegner, Olgierd Stankiewicz, Marek Domanski
Date: 2017-03-02 00:00:00
Tasks: Depth Estimation, Occlusion Handling

The paper presents a novel approach to occlusion handling problem in depth
estimation using three views. A solution based on modification of similarity
cost function is proposed. During the depth estimation via optimization
algorithms like Graph Cut similarity metric is constantly updated so that only
non-occluded fragments in side views are considered. At each iteration of the
algorithm non-occluded fragments are detected based on side view virtual depth
maps synthesized from the best currently estimated depth map of the center
view. Then similarity metric is updated for correspondence search only in
non-occluded regions of the side views. The experimental results, conducted on
well-known 3D video test sequences, have proved that the depth maps estimated
with the proposed approach provide about 1.25 dB virtual view quality
improvement in comparison to the virtual view synthesized based on depth maps
generated by the state-of-the-art MPEG Depth Estimation Reference Software.
Neural Skill Transfer from Supervised Language Tasks to Reading Comprehension
Authors: Todor Mihaylov, Zornitsa Kozareva, Anette Frank
Date: 2017-11-10 00:00:00
Tasks: General Classification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Natural Language Inference, Reading Comprehension, text-classification, Text Classification, Transfer Learning

Reading comprehension is a challenging task in natural language processing
and requires a set of skills to be solved. While current approaches focus on
solving the task as a whole, in this paper, we propose to use a neural network
`skill' transfer approach. We transfer knowledge from several lower-level
language tasks (skills) including textual entailment, named entity recognition,
paraphrase detection and question type classification into the reading
comprehension model.
  We conduct an empirical evaluation and show that transferring language skill
knowledge leads to significant improvements for the task with much fewer steps
compared to the baseline model. We also show that the skill transfer approach
is effective even with small amounts of training data. Another finding of this
work is that using token-wise deep label supervision for text classification
improves the performance of transfer learning.
Lattice embeddings between types of fuzzy sets. Closed-valued fuzzy sets
Authors: F. J. Lobillo, Luis Merino, Gabriel Navarro, Evangelina Santos
Date: 2017-11-10 00:00:00

In this paper we deal with the problem of extending Zadeh's operators on
fuzzy sets (FSs) to interval-valued (IVFSs), set-valued (SVFSs) and type-2
(T2FSs) fuzzy sets. Namely, it is known that seeing FSs as SVFSs, or T2FSs,
whose membership degrees are singletons is not order-preserving. We then
describe a family of lattice embeddings from FSs to SVFSs. Alternatively, if
the former singleton viewpoint is required, we reformulate the intersection on
hesitant fuzzy sets and introduce what we have called closed-valued fuzzy sets.
This new type of fuzzy sets extends standard union and intersection on FSs. In
addition, it allows handling together membership degrees of different nature
as, for instance, closed intervals and finite sets. Finally, all these
constructions are viewed as T2FSs forming a chain of lattices.
The Data Complexity of Description Logic Ontologies
Authors: Carsten Lutz, Frank Wolter
Date: 2016-11-08 00:00:00

We analyze the data complexity of ontology-mediated querying where the
ontologies are formulated in a description logic (DL) of the ALC family and
queries are conjunctive queries, positive existential queries, or acyclic
conjunctive queries. Our approach is non-uniform in the sense that we aim to
understand the complexity of each single ontology instead of for all ontologies
formulated in a certain language. While doing so, we quantify over the queries
and are interested, for example, in the question whether all queries can be
evaluated in polynomial time w.r.t. a given ontology. Our results include a
PTime/coNP-dichotomy for ontologies of depth one in the description logic
ALCFI, the same dichotomy for ALC- and ALCI-ontologies of unrestricted depth,
and the non-existence of such a dichotomy for ALCF-ontologies. For the latter
DL, we additionally show that it is undecidable whether a given ontology admits
PTime query evaluation. We also consider the connection between PTime query
evaluation and rewritability into (monadic) Datalog.