pwc-archive/papers-with-abstracts huggingface dataset:
Automatic Extrinsic Calibration for Lidar-Stereo Vehicle Sensor Setups
Authors: Carlos Guindel, Jorge Beltrán, David Martín, Fernando García
Date: 2017-05-11 00:00:00

Sensor setups consisting of a combination of 3D range scanner lasers and
stereo vision systems are becoming a popular choice for on-board perception
systems in vehicles; however, the combined use of both sources of information
implies a tedious calibration process. We present a method for extrinsic
calibration of lidar-stereo camera pairs without user intervention. Our
calibration approach is aimed to cope with the constraints commonly found in
automotive setups, such as low-resolution and specific sensor poses. To
demonstrate the performance of our method, we also introduce a novel approach
for the quantitative assessment of the calibration results, based on a
simulation environment. Tests using real devices have been conducted as well,
proving the usability of the system and the improvement over the existing
approaches. Code is available at http://wiki.ros.org/velo2cam_calibration
Fast Face-swap Using Convolutional Neural Networks
Authors: Iryna Korshunova, Wenzhe Shi, Joni Dambre, Lucas Theis
Date: 2016-11-29 00:00:00
Proceeding: ICCV 2017 10
Tasks: Face Swapping, Style Transfer

We consider the problem of face swapping in images, where an input identity
is transformed into a target identity while preserving pose, facial expression,
and lighting. To perform this mapping, we use convolutional neural networks
trained to capture the appearance of the target identity from an unstructured
collection of his/her photographs.This approach is enabled by framing the face
swapping problem in terms of style transfer, where the goal is to render an
image in the style of another one. Building on recent advances in this area, we
devise a new loss function that enables the network to produce highly
photorealistic results. By combining neural networks with simple pre- and
post-processing steps, we aim at making face swap work in real-time with no
input from the user.
Detecting and Explaining Causes From Text For a Time Series Event
Authors: Dongyeop Kang, Varun Gangal, Ang Lu, Zheng Chen, Eduard Hovy
Date: 2017-07-27 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Time Series, Time Series Analysis

Explaining underlying causes or effects about events is a challenging but
valuable task. We define a novel problem of generating explanations of a time
series event by (1) searching cause and effect relationships of the time series
with textual data and (2) constructing a connecting chain between them to
generate an explanation. To detect causal features from text, we propose a
novel method based on the Granger causality of time series between features
extracted from text such as N-grams, topics, sentiments, and their composition.
The generation of the sequence of causal entities requires a commonsense
causative knowledge base with efficient reasoning. To ensure good
interpretability and appropriate lexical usage we combine symbolic and neural
representations, using a neural reasoning algorithm trained on commonsense
causal tuples to predict the next cause step. Our quantitative and human
analysis show empirical evidence that our method successfully extracts
meaningful causality relationships between time series with textual features
and generates appropriate explanation between them.
DSRIM: A Deep Neural Information Retrieval Model Enhanced by a Knowledge Resource Driven Representation of Documents
Authors: Gia-Hung Nguyen, Laure Soulier, Lynda Tamine, Nathalie Bricon-Souf
Date: 2017-06-15 00:00:00
Tasks: Information Retrieval, Retrieval

The state-of-the-art solutions to the vocabulary mismatch in information
retrieval (IR) mainly aim at leveraging either the relational semantics
provided by external resources or the distributional semantics, recently
investigated by deep neural approaches. Guided by the intuition that the
relational semantics might improve the effectiveness of deep neural approaches,
we propose the Deep Semantic Resource Inference Model (DSRIM) that relies on:
1) a representation of raw-data that models the relational semantics of text by
jointly considering objects and relations expressed in a knowledge resource,
and 2) an end-to-end neural architecture that learns the query-document
relevance by leveraging the distributional and relational semantics of
documents and queries. The experimental evaluation carried out on two TREC
datasets from TREC Terabyte and TREC CDS tracks relying respectively on WordNet
and MeSH resources, indicates that our model outperforms state-of-the-art
semantic and deep neural IR models.
STN-OCR: A single Neural Network for Text Detection and Text Recognition
Authors: Christian Bartz, Haojin Yang, Christoph Meinel
Date: 2017-07-27 00:00:00
Tasks: Optical Character Recognition (OCR), Scene Text Detection, Scene Text Recognition, Text Detection

Detecting and recognizing text in natural scene images is a challenging, yet
not completely solved task. In re- cent years several new systems that try to
solve at least one of the two sub-tasks (text detection and text recognition)
have been proposed. In this paper we present STN-OCR, a step towards
semi-supervised neural networks for scene text recognition, that can be
optimized end-to-end. In contrast to most existing works that consist of
multiple deep neural networks and several pre-processing steps we propose to
use a single deep neural network that learns to detect and recognize text from
natural images in a semi-supervised way. STN-OCR is a network that integrates
and jointly learns a spatial transformer network, that can learn to detect text
regions in an image, and a text recognition network that takes the identified
text regions and recognizes their textual content. We investigate how our model
behaves on a range of different tasks (detection and recognition of characters,
and lines of text). Experimental results on public benchmark datasets show the
ability of our model to handle a variety of different tasks, without
substantial changes in its overall network structure.
Serious Games Application for Memory Training Using Egocentric Images
Authors: Gabriel Oliveira-Barra, Marc Bolaños, Estefania Talavera, Adrián Dueñas, Olga Gelonch, Maite Garolera
Date: 2017-07-27 00:00:00

Mild cognitive impairment is the early stage of several neurodegenerative
diseases, such as Alzheimer's. In this work, we address the use of lifelogging
as a tool to obtain pictures from a patient's daily life from an egocentric
point of view. We propose to use them in combination with serious games as a
way to provide a non-pharmacological treatment to improve their quality of
life. To do so, we introduce a novel computer vision technique that classifies
rich and non rich egocentric images and uses them in serious games. We present
results over a dataset composed by 10,997 images, recorded by 7 different
users, achieving 79% of F1-score. Our model presents the first method used for
automatic egocentric images selection applicable to serious games.
Max K-armed bandit: On the ExtremeHunter algorithm and beyond
Authors: Mastane Achab, Stephan Clémençon, Aurélien Garivier, Anne Sabourin, Claire Vernade
Date: 2017-07-27 00:00:00

This paper is devoted to the study of the max K-armed bandit problem, which
consists in sequentially allocating resources in order to detect extreme
values. Our contribution is twofold. We first significantly refine the analysis
of the ExtremeHunter algorithm carried out in Carpentier and Valko (2014), and
next propose an alternative approach, showing that, remarkably, Extreme Bandits
can be reduced to a classical version of the bandit problem to a certain
extent. Beyond the formal analysis, these two approaches are compared through
numerical experiments.
Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards
Authors: Mel Vecerik, Todd Hester, Jonathan Scholz, Fumin Wang, Olivier Pietquin, Bilal Piot, Nicolas Heess, Thomas Rothörl, Thomas Lampe, Martin Riedmiller
Date: 2017-07-27 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a general and model-free approach for Reinforcement Learning (RL)
on real robotics with sparse rewards. We build upon the Deep Deterministic
Policy Gradient (DDPG) algorithm to use demonstrations. Both demonstrations and
actual interactions are used to fill a replay buffer and the sampling ratio
between demonstrations and transitions is automatically tuned via a prioritized
replay mechanism. Typically, carefully engineered shaping rewards are required
to enable the agents to efficiently explore on high dimensional control
problems such as robotics. They are also required for model-based acceleration
methods relying on local solvers such as iLQG (e.g. Guided Policy Search and
Normalized Advantage Function). The demonstrations replace the need for
carefully engineered rewards, and reduce the exploration problem encountered by
classical RL approaches in these domains. Demonstrations are collected by a
robot kinesthetically force-controlled by a human demonstrator. Results on four
simulated insertion tasks show that DDPG from demonstrations out-performs DDPG,
and does not require engineered rewards. Finally, we demonstrate the method on
a real robotics task consisting of inserting a clip (flexible object) into a
rigid object.
Food Ingredients Recognition through Multi-label Learning
Authors: Marc Bolaños, Aina Ferrà, Petia Radeva
Date: 2017-07-27 00:00:00
Tasks: Multi-Label Classification, Multi-Label Learning

Automatically constructing a food diary that tracks the ingredients consumed
can help people follow a healthy diet. We tackle the problem of food
ingredients recognition as a multi-label learning problem. We propose a method
for adapting a highly performing state of the art CNN in order to act as a
multi-label predictor for learning recipes in terms of their list of
ingredients. We prove that our model is able to, given a picture, predict its
list of ingredients, even if the recipe corresponding to the picture has never
been seen by the model. We make public two new datasets suitable for this
purpose. Furthermore, we prove that a model trained with a high variability of
recipes and ingredients is able to generalize better on new data, and visualize
how it specializes each of its neurons to different ingredients.
Representation-Aggregation Networks for Segmentation of Multi-Gigapixel Histology Images
Authors: Abhinav Agarwalla, Muhammad Shaban, Nasir M. Rajpoot
Date: 2017-07-27 00:00:00
Tasks: Representation Learning, Segmentation, whole slide images

Convolutional Neural Network (CNN) models have become the state-of-the-art
for most computer vision tasks with natural images. However, these are not best
suited for multi-gigapixel resolution Whole Slide Images (WSIs) of histology
slides due to large size of these images. Current approaches construct smaller
patches from WSIs which results in the loss of contextual information. We
propose to capture the spatial context using novel Representation-Aggregation
Network (RAN) for segmentation purposes, wherein the first network learns
patch-level representation and the second network aggregates context from a
grid of neighbouring patches. We can use any CNN for representation learning,
and can utilize CNN or 2D-Long Short Term Memory (2D-LSTM) for
context-aggregation. Our method significantly outperformed conventional
patch-based CNN approaches on segmentation of tumour in WSIs of breast cancer
tissue sections.
Pycobra: A Python Toolbox for Ensemble Learning and Visualisation
Authors: Benjamin Guedj, Bhargav Srinivasa Desikan
Date: 2017-04-25 00:00:00
Tasks: BIG-bench Machine Learning, Ensemble Learning, General Classification

We introduce \texttt{pycobra}, a Python library devoted to ensemble learning (regression and classification) and visualisation. Its main assets are the implementation of several ensemble learning algorithms, a flexible and generic interface to compare and blend any existing machine learning algorithm available in Python libraries (as long as a \texttt{predict} method is given), and visualisation tools such as Voronoi tessellations. \texttt{pycobra} is fully \texttt{scikit-learn} compatible and is released under the MIT open-source license. \texttt{pycobra} can be downloaded from the Python Package Index (PyPi) and Machine Learning Open Source Software (MLOSS). The current version (along with Jupyter notebooks, extensive documentation, and continuous integration tests) is available at \href{https://github.com/bhargavvader/pycobra}{https://github.com/bhargavvader/pycobra} and official documentation website is \href{https://modal.lille.inria.fr/pycobra}{https://modal.lille.inria.fr/pycobra}.
Relative Depth Order Estimation Using Multi-scale Densely Connected Convolutional Networks
Authors: Ruoxi Deng, Tianqi Zhao, Chunhua Shen, Shengjun Liu
Date: 2017-07-25 00:00:00

We study the problem of estimating the relative depth order of point pairs in
a monocular image. Recent advances mainly focus on using deep convolutional
neural networks (DCNNs) to learn and infer the ordinal information from
multiple contextual information of the points pair such as global scene
context, local contextual information, and the locations. However, it remains
unclear how much each context contributes to the task. To address this, we
first examine the contribution of each context cue [1], [2] to the performance
in the context of depth order estimation. We find out the local context
surrounding the points pair contributes the most and the global scene context
helps little. Based on the findings, we propose a simple method, using a
multi-scale densely-connected network to tackle the task. Instead of learning
the global structure, we dedicate to explore the local structure by learning to
regress from regions of multiple sizes around the point pairs. Moreover, we use
the recent densely connected network [3] to encourage substantial feature reuse
as well as deepen our network to boost the performance. We show in experiments
that the results of our approach is on par with or better than the
state-of-the-art methods with the benefit of using only a small number of
training data.
You Are How You Walk: Uncooperative MoCap Gait Identification for Video Surveillance with Incomplete and Noisy Data
Authors: Michal Balazia, Petr Sojka
Date: 2017-06-28 00:00:00
Tasks: Gait Identification

This work offers a design of a video surveillance system based on a soft biometric -- gait identification from MoCap data. The main focus is on two substantial issues of the video surveillance scenario: (1) the walkers do not cooperate in providing learning data to establish their identities and (2) the data are often noisy or incomplete. We show that only a few examples of human gait cycles are required to learn a projection of raw MoCap data onto a low-dimensional sub-space where the identities are well separable. Latent features learned by Maximum Margin Criterion (MMC) method discriminate better than any collection of geometric features. The MMC method is also highly robust to noisy data and works properly even with only a fraction of joints tracked. The overall workflow of the design is directly applicable for a day-to-day operation based on the available MoCap technology and algorithms for gait analysis. In the concept we introduce, a walker's identity is represented by a cluster of gait data collected at their incidents within the surveillance system: They are how they walk.
An Evolutionary Stochastic-Local-Search Framework for One-Dimensional Cutting-Stock Problems
Authors: Georgios C. Chasparis, Michael Rossbory, Verena Haunschmid
Date: 2017-07-27 00:00:00

We introduce an evolutionary stochastic-local-search (SLS) algorithm for
addressing a generalized version of the so-called 1/V/D/R cutting-stock
problem. Cutting-stock problems are encountered often in industrial
environments and the ability to address them efficiently usually results in
large economic benefits. Traditionally linear-programming-based techniques have
been utilized to address such problems, however their flexibility might be
limited when nonlinear constraints and objective functions are introduced. To
this end, this paper proposes an evolutionary SLS algorithm for addressing
one-dimensional cutting-stock problems. The contribution lies in the
introduction of a flexible structural framework of the optimization that may
accommodate a large family of diversification strategies including a novel
parallel pattern appropriate for SLS algorithms (not necessarily restricted to
cutting-stock problems). We finally demonstrate through experiments in a
real-world manufacturing problem the benefit in cost reduction of the
considered diversification strategies.
A supermartingale approach to Gaussian process based sequential design of experiments
Authors: Julien Bect, François Bachoc, David Ginsbourger
Date: 2016-08-03 00:00:00
Tasks: global-optimization

Gaussian process (GP) models have become a well-established frameworkfor the
adaptive design of costly experiments, and notably of computerexperiments.
GP-based sequential designs have been found practicallyefficient for various
objectives, such as global optimization(estimating the global maximum or
maximizer(s) of a function),reliability analysis (estimating a probability of
failure) or theestimation of level sets and excursion sets. In this paper, we
studythe consistency of an important class of sequential designs, known
asstepwise uncertainty reduction (SUR) strategies. Our approach relieson the
key observation that the sequence of residual uncertaintymeasures, in SUR
strategies, is generally a supermartingale withrespect to the filtration
generated by the observations. Thisobservation enables us to establish generic
consistency results for abroad class of SUR strategies. The consistency of
several popularsequential design strategies is then obtained by means of this
generalresult. Notably, we establish the consistency of two SUR
strategiesproposed by Bect, Ginsbourger, Li, Picheny and Vazquez (Stat.
Comp.,2012)---to the best of our knowledge, these are the first proofs
ofconsistency for GP-based sequential design algorithms dedicated to
theestimation of excursion sets and their measure. We also establish anew, more
general proof of consistency for the expected improvementalgorithm for global
optimization which, unlike previous results inthe literature, applies to any GP
with continuous sample paths.
An Improved Epsilon Constraint-handling Method in MOEA/D for CMOPs with Large Infeasible Regions
Authors: Zhun Fan, Wenji Li, Xinye Cai, Han Huang, Yi Fang, Yugen You, Jiajie Mo, Caimin Wei, Erik Goodman
Date: 2017-07-27 00:00:00

This paper proposes an improved epsilon constraint-handling mechanism, and
combines it with a decomposition-based multi-objective evolutionary algorithm
(MOEA/D) to solve constrained multi-objective optimization problems (CMOPs).
The proposed constrained multi-objective evolutionary algorithm (CMOEA) is
named MOEA/D-IEpsilon. It adjusts the epsilon level dynamically according to
the ratio of feasible to total solutions (RFS) in the current population. In
order to evaluate the performance of MOEA/D-IEpsilon, a new set of CMOPs with
two and three objectives is designed, having large infeasible regions (relative
to the feasible regions), and they are called LIR-CMOPs. Then the fourteen
benchmarks, including LIR-CMOP1-14, are used to test MOEA/D-IEpsilon and four
other decomposition-based CMOEAs, including MOEA/D-Epsilon, MOEA/D-SR,
MOEA/D-CDP and C-MOEA/D. The experimental results indicate that MOEA/D-IEpsilon
is significantly better than the other four CMOEAs on all of the test
instances, which shows that MOEA/D-IEpsilon is more suitable for solving CMOPs
with large infeasible regions. Furthermore, a real-world problem, namely the
robot gripper optimization problem, is used to test the five CMOEAs. The
experimental results demonstrate that MOEA/D-IEpsilon also outperforms the
other four CMOEAs on this problem.
A New Modal Framework for Epistemic Logic
Authors: Yanjing Wang
Date: 2017-07-27 00:00:00

Recent years witnessed a growing interest in non-standard epistemic logics of
knowing whether, knowing how, knowing what, knowing why and so on. The new
epistemic modalities introduced in those logics all share, in their semantics,
the general schema of $\exists x \Box \phi$, e.g., knowing how to achieve
$\phi$ roughly means that there exists a way such that you know that it is a
way to ensure that $\phi$. Moreover, the resulting logics are decidable.
Inspired by those particular logics, in this work, we propose a very general
and powerful framework based on quantifier-free predicate language extended by
a new modality $\Box^x$, which packs exactly $\exists x \Box$ together. We show
that the resulting language, though much more expressive, shares many good
properties of the basic propositional modal logic over arbitrary models, such
as finite-tree-model property and van Benthem-like characterization w.r.t.\
first-order modal logic. We axiomatize the logic over S5 frames with intuitive
axioms to capture the interaction between $\Box^x$ and know-that operator in an
epistemic setting.
Reconciling Bayesian Epistemology and Narration-based Approaches to Judiciary Fact-finding
Authors: Rafal Urbaniak
Date: 2017-07-27 00:00:00

Legal probabilism (LP) claims the degrees of conviction in juridical
fact-finding are to be modeled exactly the way degrees of beliefs are modeled
in standard bayesian epistemology. Classical legal probabilism (CLP) adds that
the conviction is justified if the credence in guilt given the evidence is
above an appropriate guilt probability threshold. The views are challenged on
various counts, especially by the proponents of the so-called narrative
approach, on which the fact-finders' decision is the result of a dynamic
interplay between competing narratives of what happened. I develop a way a
bayesian epistemologist can make sense of the narrative approach. I do so by
formulating a probabilistic framework for evaluating competing narrations in
terms of formal explications of the informal evaluation criteria used in the
narrative approach.
Argument-based Belief in Topological Structures
Authors: Chenwei Shi, Sonja Smets, Fernando R. Velázquez-Quesada
Date: 2017-07-27 00:00:00
Tasks: Abstract Argumentation

This paper combines two studies: a topological semantics for epistemic
notions and abstract argumentation theory. In our combined setting, we use a
topological semantics to represent the structure of an agent's collection of
evidence, and we use argumentation theory to single out the relevant sets of
evidence through which a notion of beliefs grounded on arguments is defined. We
discuss the formal properties of this newly defined notion, providing also a
formal language with a matching modality together with a sound and complete
axiom system for it. Despite the fact that our agent can combine her evidence
in a 'rational' way (captured via the topological structure), argument-based
beliefs are not closed under conjunction. This illustrates the difference
between an agent's reasoning abilities (i.e. the way she is able to combine her
available evidence) and the closure properties of her beliefs. We use this
point to argue for why the failure of closure under conjunction of belief
should not bear the burden of the failure of rationality.
Together We Know How to Achieve: An Epistemic Logic of Know-How (Extended Abstract)
Authors: Pavel Naumov, Jia Tao
Date: 2017-07-27 00:00:00

The existence of a coalition strategy to achieve a goal does not necessarily
mean that the coalition has enough information to know how to follow the
strategy. Neither does it mean that the coalition knows that such a strategy
exists. The paper studies an interplay between the distributed knowledge,
coalition strategies, and coalition "know-how" strategies. The main technical
result is a sound and complete trimodal logical system that describes the
properties of this interplay.