pwc-archive/papers-with-abstracts huggingface dataset:
Random Forests for Industrial Device Functioning Diagnostics Using Wireless Sensor Networks
Authors: Wiem Elghazel, Kamal Medjaher, Nourredine Zerhouni, Jacques Bahi, Ahamd Farhat, Christophe Guyeux, Mourad Hakem
Date: 2017-06-25 00:00:00
Tasks: Scheduling

In this paper, random forests are proposed for operating devices diagnostics
in the presence of a variable number of features. In various contexts, like
large or difficult-to-access monitored areas, wired sensor networks providing
features to achieve diagnostics are either very costly to use or totally
impossible to spread out. Using a wireless sensor network can solve this
problem, but this latter is more subjected to flaws. Furthermore, the networks'
topology often changes, leading to a variability in quality of coverage in the
targeted area. Diagnostics at the sink level must take into consideration that
both the number and the quality of the provided features are not constant, and
that some politics like scheduling or data aggregation may be developed across
the network. The aim of this article is ($1$) to show that random forests are
relevant in this context, due to their flexibility and robustness, and ($2$) to
provide first examples of use of this method for diagnostics based on data
provided by a wireless sensor network.
Specifying Non-Markovian Rewards in MDPs Using LDL on Finite Traces (Preliminary Version)
Authors: Ronen Brafman, Giuseppe De Giacomo, Fabio Patrizi
Date: 2017-06-25 00:00:00

In Markov Decision Processes (MDPs), the reward obtained in a state depends
on the properties of the last state and action. This state dependency makes it
difficult to reward more interesting long-term behaviors, such as always
closing a door after it has been opened, or providing coffee only following a
request. Extending MDPs to handle such non-Markovian reward function was the
subject of two previous lines of work, both using variants of LTL to specify
the reward function and then compiling the new model back into a Markovian
model. Building upon recent progress in the theories of temporal logics over
finite traces, we adopt LDLf for specifying non-Markovian rewards and provide
an elegant automata construction for building a Markovian model, which extends
that of previous work and offers strong minimality and compositionality
guarantees.
Merging real and virtual worlds: An analysis of the state of the art and practical evaluation of Microsoft Hololens
Authors: Adrien Coppens
Date: 2017-06-25 00:00:00

Achieving a symbiotic blending between reality and virtuality is a dream that
has been lying in the minds of many people for a long time. Advances in various
domains constantly bring us closer to making that dream come true. Augmented
reality as well as virtual reality are in fact trending terms and are expected
to further progress in the years to come.
  This master's thesis aims to explore these areas and starts by defining
necessary terms such as augmented reality (AR) or virtual reality (VR). Usual
taxonomies to classify and compare the corresponding experiences are then
discussed.
  In order to enable those applications, many technical challenges need to be
tackled, such as accurate motion tracking with 6 degrees of freedom (positional
and rotational), that is necessary for compelling experiences and to prevent
user sickness. Additionally, augmented reality experiences typically rely on
image processing to position the superimposed content. To do so, "paper"
markers or features extracted from the environment are often employed. Both
sets of techniques are explored and common solutions and algorithms are
presented.
  After investigating those technical aspects, I carry out an objective
comparison of the existing state-of-the-art and state-of-the-practice in those
domains, and I discuss present and potential applications in these areas. As a
practical validation, I present the results of an application that I have
developed using Microsoft HoloLens, one of the more advanced affordable
technologies for augmented reality that is available today. Based on the
experience and lessons learned during this development, I discuss the
limitations of current technologies and present some avenues of future
research.
Object Boundary Detection and Classification with Image-level Labels
Authors: Jing Yu Koh, Wojciech Samek, Klaus-Robert Müller, Alexander Binder
Date: 2016-06-29 00:00:00
Tasks: Boundary Detection, Classification, Edge Detection, General Classification, Object

Semantic boundary and edge detection aims at simultaneously detecting object
edge pixels in images and assigning class labels to them. Systematic training
of predictors for this task requires the labeling of edges in images which is a
particularly tedious task. We propose a novel strategy for solving this task,
when pixel-level annotations are not available, performing it in an almost
zero-shot manner by relying on conventional whole image neural net classifiers
that were trained using large bounding boxes. Our method performs the following
two steps at test time. Firstly it predicts the class labels by applying the
trained whole image network to the test images. Secondly, it computes
pixel-wise scores from the obtained predictions by applying backprop gradients
as well as recent visualization algorithms such as deconvolution and layer-wise
relevance propagation. We show that high pixel-wise scores are indicative for
the location of semantic boundaries, which suggests that the semantic boundary
problem can be approached without using edge labels during the training phase.
Count-Based Exploration in Feature Space for Reinforcement Learning
Authors: Jarryd Martin, Suraj Narayanan Sasikumar, Tom Everitt, Marcus Hutter
Date: 2017-06-25 00:00:00
Tasks: Atari Games, Efficient Exploration, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We introduce a new count-based optimistic exploration algorithm for
Reinforcement Learning (RL) that is feasible in environments with
high-dimensional state-action spaces. The success of RL algorithms in these
domains depends crucially on generalisation from limited training experience.
Function approximation techniques enable RL agents to generalise in order to
estimate the value of unvisited states, but at present few methods enable
generalisation regarding uncertainty. This has prevented the combination of
scalable RL algorithms with efficient exploration strategies that drive the
agent to reduce its uncertainty. We present a new method for computing a
generalised state visit-count, which allows the agent to estimate the
uncertainty associated with any state. Our \phi-pseudocount achieves
generalisation by exploiting same feature representation of the state space
that is used for value function approximation. States that have less frequently
observed features are deemed more uncertain. The \phi-Exploration-Bonus
algorithm rewards the agent for exploring in feature space rather than in the
untransformed state space. The method is simpler and less computationally
expensive than some previous proposals, and achieves near state-of-the-art
results on high-dimensional RL benchmarks.
Finding optimal finite biological sequences over finite alphabets: the OptiFin toolbox
Authors: Régis Garnier, Christophe Guyeux, Stéphane Chrétien
Date: 2017-06-25 00:00:00
Tasks: Protein Structure Prediction

In this paper, we present a toolbox for a specific optimization problem that
frequently arises in bioinformatics or genomics. In this specific optimisation
problem, the state space is a set of words of specified length over a finite
alphabet. To each word is associated a score. The overall objective is to find
the words which have the lowest possible score. This type of general
optimization problem is encountered in e.g 3D conformation optimisation for
protein structure prediction, or largest core genes subset discovery based on
best supported phylogenetic tree for a set of species. In order to solve this
problem, we propose a toolbox that can be easily launched using MPI and embeds
3 well-known metaheuristics. The toolbox is fully parametrized and well
documented. It has been specifically designed to be easy modified and possibly
improved by the user depending on the application, and does not require to be a
computer scientist. We show that the toolbox performs very well on two
difficult practical problems.
Efficient and accurate monitoring of the depth information in a Wireless Multimedia Sensor Network based surveillance
Authors: Anthony Tannoury, Rony Darazi, Christophe Guyeux, Abdallah Makhoul
Date: 2017-06-25 00:00:00
Tasks: 3D Scene Reconstruction, Object, Stereo Matching, Stereo Matching Hand

Wireless Multimedia Sensor Network (WMSN) is a promising technology capturing
rich multimedia data like audio and video, which can be useful to monitor an
environment under surveillance. However, many scenarios in real time monitoring
requires 3D depth information. In this research work, we propose to use the
disparity map that is computed from two or multiple images, in order to monitor
the depth information in an object or event under surveillance using WMSN. Our
system is based on distributed wireless sensors allowing us to notably reduce
the computational time needed for 3D depth reconstruction, thus permitting the
success of real time solutions. Each pair of sensors will capture images for a
targeted place/object and will operate a Stereo Matching in order to create a
Disparity Map. Disparity maps will give us the ability to decrease traffic on
the bandwidth, because they are of low size. This will increase WMSN lifetime.
Any event can be detected after computing the depth value for the target object
in the scene, and also 3D scene reconstruction can be achieved with a disparity
map and some reference(s) image(s) taken by the node(s).
Target contrastive pessimistic risk for robust domain adaptation
Authors: Wouter M. Kouw, Marco Loog
Date: 2017-06-25 00:00:00
Tasks: Domain Adaptation, Selection bias

In domain adaptation, classifiers with information from a source domain adapt
to generalize to a target domain. However, an adaptive classifier can perform
worse than a non-adaptive classifier due to invalid assumptions, increased
sensitivity to estimation errors or model misspecification. Our goal is to
develop a domain-adaptive classifier that is robust in the sense that it does
not rely on restrictive assumptions on how the source and target domains relate
to each other and that it does not perform worse than the non-adaptive
classifier. We formulate a conservative parameter estimator that only deviates
from the source classifier when a lower risk is guaranteed for all possible
labellings of the given target samples. We derive the classical least-squares
and discriminant analysis cases and show that these perform on par with
state-of-the-art domain adaptive classifiers in sample selection bias settings,
while outperforming them in more general domain adaptation settings.
Large-scale Validation of Counterfactual Learning Methods: A Test-Bed
Authors: Damien Lefortier, Adith Swaminathan, Xiaotao Gu, Thorsten Joachims, Maarten de Rijke
Date: 2016-12-01 00:00:00
Tasks: counterfactual, Off-policy evaluation, Recommendation Systems

The ability to perform effective off-policy learning would revolutionize the
process of building better interactive systems, such as search engines and
recommendation systems for e-commerce, computational advertising and news.
Recent approaches for off-policy evaluation and learning in these settings
appear promising. With this paper, we provide real-world data and a
standardized test-bed to systematically investigate these algorithms using data
from display advertising. In particular, we consider the problem of filling a
banner ad with an aggregate of multiple products the user may want to purchase.
This paper presents our test-bed, the sanity checks we ran to ensure its
validity, and shows results comparing state-of-the-art off-policy learning
methods like doubly robust optimization, POEM, and reductions to supervised
learning using regression baselines. Our results show experimental evidence
that recent off-policy learning methods can improve upon state-of-the-art
supervised learning techniques on a large-scale real-world data set.
Deep Factorization for Speech Signal
Authors: Dong Wang, Lantian Li, Ying Shi, Yixiang Chen, Zhiyuan Tang
Date: 2017-06-05 00:00:00
Tasks: Emotion Recognition

Speech signals are complex intermingling of various informative factors, and
this information blending makes decoding any of the individual factors
extremely difficult. A natural idea is to factorize each speech frame into
independent factors, though it turns out to be even more difficult than
decoding each individual factor. A major encumbrance is that the speaker trait,
a major factor in speech signals, has been suspected to be a long-term
distributional pattern and so not identifiable at the frame level. In this
paper, we demonstrated that the speaker factor is also a short-time spectral
pattern and can be largely identified with just a few frames using a simple
deep neural network (DNN). This discovery motivated a cascade deep
factorization (CDF) framework that infers speech factors in a sequential way,
and factors previously inferred are used as conditional variables when
inferring other factors. Our experiment on an automatic emotion recognition
(AER) task demonstrated that this approach can effectively factorize speech
signals, and using these factors, the original speech spectrum can be recovered
with high accuracy. This factorization and reconstruction approach provides a
novel tool for many speech processing tasks.
AdaScan: Adaptive Scan Pooling in Deep Convolutional Neural Networks for Human Action Recognition in Videos
Authors: Amlan Kar, Nishant Rai, Karan Sikka, Gaurav Sharma
Date: 2016-11-24 00:00:00
Proceeding: CVPR 2017 7
Tasks: Action Recognition, Action Recognition In Videos, Optical Flow Estimation, Temporal Action Localization

We propose a novel method for temporally pooling frames in a video for the
task of human action recognition. The method is motivated by the observation
that there are only a small number of frames which, together, contain
sufficient information to discriminate an action class present in a video, from
the rest. The proposed method learns to pool such discriminative and
informative frames, while discarding a majority of the non-informative frames
in a single temporal scan of the video. Our algorithm does so by continuously
predicting the discriminative importance of each video frame and subsequently
pooling them in a deep learning framework. We show the effectiveness of our
proposed pooling method on standard benchmarks where it consistently improves
on baseline pooling methods, with both RGB and optical flow based Convolutional
networks. Further, in combination with complementary video representations, we
show results that are competitive with respect to the state-of-the-art results
on two challenging and publicly available benchmark datasets.
A Deep Neural Architecture for Sentence-level Sentiment Classification in Twitter Social Networking
Authors: Huy Nguyen, Minh-Le Nguyen
Date: 2017-06-25 00:00:00
Tasks: Classification, General Classification, Sentence, Sentiment Analysis, Sentiment Classification

This paper introduces a novel deep learning framework including a
lexicon-based approach for sentence-level prediction of sentiment label
distribution. We propose to first apply semantic rules and then use a Deep
Convolutional Neural Network (DeepCNN) for character-level embeddings in order
to increase information for word-level embedding. After that, a Bidirectional
Long Short-Term Memory Network (Bi-LSTM) produces a sentence-wide feature
representation from the word-level embedding. We evaluate our approach on three
Twitter sentiment classification datasets. Experimental results show that our
model can improve the classification accuracy of sentence-level sentiment
analysis in Twitter social networking.
Learning Tree-Structured Detection Cascades for Heterogeneous Networks of Embedded Devices
Authors: Hamid Dadkhahi, Benjamin M. Marlin
Date: 2016-07-30 00:00:00
Tasks: Activity Recognition

In this paper, we present a new approach to learning cascaded classifiers for
use in computing environments that involve networks of heterogeneous and
resource-constrained, low-power embedded compute and sensing nodes. We present
a generalization of the classical linear detection cascade to the case of
tree-structured cascades where different branches of the tree execute on
different physical compute nodes in the network. Different nodes have access to
different features, as well as access to potentially different computation and
energy resources. We concentrate on the problem of jointly learning the
parameters for all of the classifiers in the cascade given a fixed cascade
architecture and a known set of costs required to carry out the computation at
each node.To accomplish the objective of joint learning of all detectors, we
propose a novel approach to combining classifier outputs during training that
better matches the hard cascade setting in which the learned system will be
deployed. This work is motivated by research in the area of mobile health where
energy efficient real time detectors integrating information from multiple
wireless on-body sensors and a smart phone are needed for real-time monitoring
and delivering just- in-time adaptive interventions. We apply our framework to
two activity recognition datasets as well as the problem of cigarette smoking
detection from a combination of wrist-worn actigraphy data and respiration
chest band data.
Temporal-related Convolutional-Restricted-Boltzmann-Machine capable of learning relational order via reinforcement learning procedure?
Authors: Zizhuang Wang
Date: 2017-06-24 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this article, we extend the conventional framework of
convolutional-Restricted-Boltzmann-Machine to learn highly abstract features
among abitrary number of time related input maps by constructing a layer of
multiplicative units, which capture the relations among inputs. In many cases,
more than two maps are strongly related, so it is wise to make multiplicative
unit learn relations among more input maps, in other words, to find the optimal
relational-order of each unit. In order to enable our machine to learn
relational order, we developed a reinforcement-learning method whose optimality
is proven to train the network.
Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds
Authors: Shusen Wang, Alex Gittens, Michael W. Mahoney
Date: 2017-06-09 00:00:00
Tasks: Clustering

Kernel $k$-means clustering can correctly identify and extract a far more
varied collection of cluster structures than the linear $k$-means clustering
algorithm. However, kernel $k$-means clustering is computationally expensive
when the non-linear feature map is high-dimensional and there are many input
points. Kernel approximation, e.g., the Nystr\"om method, has been applied in
previous works to approximately solve kernel learning problems when both of the
above conditions are present. This work analyzes the application of this
paradigm to kernel $k$-means clustering, and shows that applying the linear
$k$-means clustering algorithm to $\frac{k}{\epsilon} (1 + o(1))$ features
constructed using a so-called rank-restricted Nystr\"om approximation results
in cluster assignments that satisfy a $1 + \epsilon$ approximation ratio in
terms of the kernel $k$-means cost function, relative to the guarantee provided
by the same algorithm without the use of the Nystr\"om method. As part of the
analysis, this work establishes a novel $1 + \epsilon$ relative-error trace
norm guarantee for low-rank approximation using the rank-restricted Nystr\"om
approximation. Empirical evaluations on the $8.1$ million instance MNIST8M
dataset demonstrate the scalability and usefulness of kernel $k$-means
clustering with Nystr\"om approximation. This work argues that spectral
clustering using Nystr\"om approximation---a popular and computationally
efficient, but theoretically unsound approach to non-linear clustering---should
be replaced with the efficient and theoretically sound combination of kernel
$k$-means clustering with Nystr\"om approximation. The superior performance of
the latter approach is empirically verified.
Inference of High-dimensional Autoregressive Generalized Linear Models
Authors: Eric C. Hall, Garvesh Raskutti, Rebecca Willett
Date: 2016-05-09 00:00:00
Tasks: Time Series, Time Series Analysis, Vocal Bursts Intensity Prediction

Vector autoregressive models characterize a variety of time series in which
linear combinations of current and past observations can be used to accurately
predict future observations. For instance, each element of an observation
vector could correspond to a different node in a network, and the parameters of
an autoregressive model would correspond to the impact of the network structure
on the time series evolution. Often these models are used successfully in
practice to learn the structure of social, epidemiological, financial, or
biological neural networks. However, little is known about statistical
guarantees on estimates of such models in non-Gaussian settings. This paper
addresses the inference of the autoregressive parameters and associated network
structure within a generalized linear model framework that includes Poisson and
Bernoulli autoregressive processes. At the heart of this analysis is a
sparsity-regularized maximum likelihood estimator. While
sparsity-regularization is well-studied in the statistics and machine learning
communities, those analysis methods cannot be applied to autoregressive
generalized linear models because of the correlations and potential
heteroscedasticity inherent in the observations. Sample complexity bounds are
derived using a combination of martingale concentration inequalities and modern
empirical process techniques for dependent random variables. These bounds,
which are supported by several simulation studies, characterize the impact of
various network parameters on estimator performance.
Revisiting Unsupervised Learning for Defect Prediction
Authors: Wei Fu, Tim Menzies
Date: 2017-03-01 00:00:00
Tasks: Prediction

Collecting quality data from software projects can be time-consuming and
expensive. Hence, some researchers explore "unsupervised" approaches to quality
prediction that does not require labelled data. An alternate technique is to
use "supervised" approaches that learn models from project data labelled with,
say, "defective" or "not-defective". Most researchers use these supervised
models since, it is argued, they can exploit more knowledge of the projects.
  At FSE'16, Yang et al. reported startling results where unsupervised defect
predictors outperformed supervised predictors for effort-aware just-in-time
defect prediction. If confirmed, these results would lead to a dramatic
simplification of a seemingly complex task (data mining) that is widely
explored in the software engineering literature.
  This paper repeats and refutes those results as follows. (1) There is much
variability in the efficacy of the Yang et al. predictors so even with their
approach, some supervised data is required to prune weaker predictors away.
(2)Their findings were grouped across $N$ projects. When we repeat their
analysis on a project-by-project basis, supervised predictors are seen to work
better.
  Even though this paper rejects the specific conclusions of Yang et al., we
still endorse their general goal. In our our experiments, supervised predictors
did not perform outstandingly better than unsupervised ones for effort-aware
just-in-time defect prediction. Hence, they may indeed be some combination of
unsupervised learners to achieve comparable performance to supervised ones. We
therefore encourage others to work in this promising area.