pwc-archive/papers-with-abstracts huggingface dataset:
Designing a Belief Function-Based Accessibility Indicator to Improve Web Browsing for Disabled People
Authors: Jean-Christophe Dubois, Yolande Le Gall, Arnaud Martin
Date: 2015-01-20 00:00:00

The purpose of this study is to provide an accessibility measure of
web-pages, in order to draw disabled users to the pages that have been designed
to be ac-cessible to them. Our approach is based on the theory of belief
functions, using data which are supplied by reports produced by automatic web
content assessors that test the validity of criteria defined by the WCAG 2.0
guidelines proposed by the World Wide Web Consortium (W3C) organization. These
tools detect errors with gradual degrees of certainty and their results do not
always converge. For these reasons, to fuse information coming from the
reports, we choose to use an information fusion framework which can take into
account the uncertainty and imprecision of infor-mation as well as divergences
between sources. Our accessibility indicator covers four categories of
deficiencies. To validate the theoretical approach in this context, we propose
an evaluation completed on a corpus of 100 most visited French news websites,
and 2 evaluation tools. The results obtained illustrate the interest of our
accessibility indicator.
Consid{é}rant la d{é}pendance dans la th{é}orie des fonctions de croyance
Authors: Mouna Chebbah, Mouloud Kharoune, Arnaud Martin, Boutheina Ben Yaghlane
Date: 2015-01-20 00:00:00

In this paper, we propose to learn sources independence in order to choose
the appropriate type of combination rules when aggregating their beliefs. Some
combination rules are used with the assumption of their sources independence
whereas others combine beliefs of dependent sources. Therefore, the choice of
the combination rule depends on the independence of sources involved in the
combination. In this paper, we propose also a measure of independence, positive
and negative dependence to integrate in mass functions before the combinaision
with the independence assumption.
Fast Localization of Facial Landmark Points
Authors: Nenad Markuš, Miroslav Frljak, Igor S. Pandžić, Jörgen Ahlberg, Robert Forchheimer
Date: 2014-03-26 00:00:00
Tasks: Facial Landmark Detection

Localization of salient facial landmark points, such as eye corners or the
tip of the nose, is still considered a challenging computer vision problem
despite recent efforts. This is especially evident in unconstrained
environments, i.e., in the presence of background clutter and large head pose
variations. Most methods that achieve state-of-the-art accuracy are slow, and,
thus, have limited applications. We describe a method that can accurately
estimate the positions of relevant facial landmarks in real-time even on
hardware with limited processing power, such as mobile devices. This is
achieved with a sequence of estimators based on ensembles of regression trees.
The trees use simple pixel intensity comparisons in their internal nodes and
this makes them able to process image regions very fast. We test the developed
system on several publicly available datasets and analyse its processing speed
on various devices. Experimental results show that our method has practical
value.
Distributed Data Association in Smart Camera Networks via Dual Decomposition
Authors: Jiuqing Wan, Yuting Nie, Li Liu
Date: 2015-01-20 00:00:00

One of the fundamental requirements for visual surveillance using smart
camera networks is the correct association of each persons observations
generated on different cameras. Recently, distributed data association that
involves only local information processing on each camera node and mutual
information exchanging between neighboring cameras has attracted many research
interests due to its superiority in large scale applications. In this paper, we
formulate the problem of data association in smart camera networks as an
Integer Programming problem by introducing a set of linking variables, and
propose two distributed algorithms, namely L-DD and Q-DD, to solve the Integer
Programming problem using dual decomposition technique. In our algorithms, the
original IP problem is decomposed into several sub-problems, which can be
solved locally and efficiently on each smart camera, and then different
sub-problems reach consensus on their solutions in a rigorous way by adjusting
their parameters based on projected sub-gradient optimization. The proposed
methods are simple and flexible, in that (i) we can incorporate any feature
extraction and matching technique into our framework to measure the similarity
between two observations, which is used to define the cost of each link, and
(ii) we can decompose the original problem in any way as long as the resulting
sub-problem can be solved independently on individual camera. We show the
competitiveness of our methods in both accuracy and speed by theoretical
analysis and experimental comparison with state of the art algorithms on two
real data sets collected by camera networks in our campus garden and office
building.
Building DNN Acoustic Models for Large Vocabulary Speech Recognition
Authors: Andrew L. Maas, Peng Qi, Ziang Xie, Awni Y. Hannun, Christopher T. Lengerich, Daniel Jurafsky, Andrew Y. Ng
Date: 2014-06-30 00:00:00
Tasks: speech-recognition, Speech Recognition

Deep neural networks (DNNs) are now a central component of nearly all
state-of-the-art speech recognition systems. Building neural network acoustic
models requires several design decisions including network architecture, size,
and training loss function. This paper offers an empirical investigation on
which aspects of DNN acoustic model design are most important for speech
recognition system performance. We report DNN classifier performance and final
speech recognizer word error rates, and compare DNNs using several metrics to
quantify factors influencing differences in task performance. Our first set of
experiments use the standard Switchboard benchmark corpus, which contains
approximately 300 hours of conversational telephone speech. We compare standard
DNNs to convolutional networks, and present the first experiments using
locally-connected, untied neural networks for acoustic modeling. We
additionally build systems on a corpus of 2,100 hours of training data by
combining the Switchboard and Fisher corpora. This larger corpus allows us to
more thoroughly examine performance of large DNN models -- with up to ten times
more parameters than those typically used in speech recognition systems. Our
results suggest that a relatively simple DNN architecture and optimization
technique produces strong results. These findings, along with previous work,
help establish a set of best practices for building DNN hybrid speech
recognition systems with maximum likelihood training. Our experiments in DNN
optimization additionally serve as a case study for training DNNs with
discriminative loss functions for speech tasks, as well as DNN classifiers more
generally.
Learning Invariants using Decision Trees
Authors: Siddharth Krishna, Christian Puhrsch, Thomas Wies
Date: 2015-01-20 00:00:00
Tasks: Binary Classification

The problem of inferring an inductive invariant for verifying program safety
can be formulated in terms of binary classification. This is a standard problem
in machine learning: given a sample of good and bad points, one is asked to
find a classifier that generalizes from the sample and separates the two sets.
Here, the good points are the reachable states of the program, and the bad
points are those that reach a safety property violation. Thus, a learned
classifier is a candidate invariant. In this paper, we propose a new algorithm
that uses decision trees to learn candidate invariants in the form of arbitrary
Boolean combinations of numerical inequalities. We have used our algorithm to
verify C programs taken from the literature. The algorithm is able to infer
safe invariants for a range of challenging benchmarks and compares favorably to
other ML-based invariant inference techniques. In particular, it scales well to
large sample sets.
Robust Face Recognition by Constrained Part-based Alignment
Authors: Yuting Zhang, Kui Jia, Yueming Wang, Gang Pan, Tsung-Han Chan, Yi Ma
Date: 2015-01-20 00:00:00
Tasks: Face Alignment, Face Recognition, Robust Face Recognition

Developing a reliable and practical face recognition system is a
long-standing goal in computer vision research. Existing literature suggests
that pixel-wise face alignment is the key to achieve high-accuracy face
recognition. By assuming a human face as piece-wise planar surfaces, where each
surface corresponds to a facial part, we develop in this paper a Constrained
Part-based Alignment (CPA) algorithm for face recognition across pose and/or
expression. Our proposed algorithm is based on a trainable CPA model, which
learns appearance evidence of individual parts and a tree-structured shape
configuration among different parts. Given a probe face, CPA simultaneously
aligns all its parts by fitting them to the appearance evidence with
consideration of the constraint from the tree-structured shape configuration.
This objective is formulated as a norm minimization problem regularized by
graph likelihoods. CPA can be easily integrated with many existing classifiers
to perform part-based face recognition. Extensive experiments on benchmark face
datasets show that CPA outperforms or is on par with existing methods for
robust face recognition across pose, expression, and/or illumination changes.
DeepHash: Getting Regularization, Depth and Fine-Tuning Right
Authors: Jie Lin, Olivier Morere, Vijay Chandrasekhar, Antoine Veillard, Hanlin Goh
Date: 2015-01-20 00:00:00
Tasks: Retrieval

This work focuses on representing very high-dimensional global image
descriptors using very compact 64-1024 bit binary hashes for instance
retrieval. We propose DeepHash: a hashing scheme based on deep networks. Key to
making DeepHash work at extremely low bitrates are three important
considerations -- regularization, depth and fine-tuning -- each requiring
solutions specific to the hashing problem. In-depth evaluation shows that our
scheme consistently outperforms state-of-the-art methods across all data sets
for both Fisher Vectors and Deep Convolutional Neural Network features, by up
to 20 percent over other schemes. The retrieval performance with 256-bit hashes
is close to that of the uncompressed floating point features -- a remarkable
512 times compression.
Comment on "Clustering by fast search and find of density peaks"
Authors: Shuliang Wang, Dakui Wang, Caoyuan Li, Yan Li
Date: 2015-01-18 00:00:00
Tasks: Clustering

In [1], a clustering algorithm was given to find the centers of clusters
quickly. However, the accuracy of this algorithm heavily depend on the
threshold value of d-c. Furthermore, [1] has not provided any efficient way to
select the threshold value of d-c, that is, one can have to estimate the value
of d_c depend on one's subjective experience. In this paper, based on the data
field [2], we propose a new way to automatically extract the threshold value of
d_c from the original data set by using the potential entropy of data field.
For any data set to be clustered, the most reasonable value of d_c can be
objectively calculated from the data set by using our proposed method. The same
experiments in [1] are redone with our proposed method on the same experimental
data set used in [1], the results of which shows that the problem to calculate
the threshold value of d_c in [1] has been solved by using our method.
Learning Mixtures of Bernoulli Templates by Two-Round EM with Performance Guarantee
Authors: Adrian Barbu, Tianfu Wu, Ying Nian Wu
Date: 2013-05-02 00:00:00

Dasgupta and Shulman showed that a two-round variant of the EM algorithm can
learn mixture of Gaussian distributions with near optimal precision with high
probability if the Gaussian distributions are well separated and if the
dimension is sufficiently high. In this paper, we generalize their theory to
learning mixture of high-dimensional Bernoulli templates. Each template is a
binary vector, and a template generates examples by randomly switching its
binary components independently with a certain probability. In computer vision
applications, a binary vector is a feature map of an image, where each binary
component indicates whether a local feature or structure is present or absent
within a certain cell of the image domain. A Bernoulli template can be
considered as a statistical model for images of objects (or parts of objects)
from the same category. We show that the two-round EM algorithm can learn
mixture of Bernoulli templates with near optimal precision with high
probability, if the Bernoulli templates are sufficiently different and if the
number of features is sufficiently high. We illustrate the theoretical results
by synthetic and real examples.
Naive-Deep Face Recognition: Touching the Limit of LFW Benchmark or Not?
Authors: Erjin Zhou, Zhimin Cao, Qi Yin
Date: 2015-01-20 00:00:00
Tasks: Face Recognition

Face recognition performance improves rapidly with the recent deep learning
technique developing and underlying large training dataset accumulating. In
this paper, we report our observations on how big data impacts the recognition
performance. According to these observations, we build our Megvii Face
Recognition System, which achieves 99.50% accuracy on the LFW benchmark,
outperforming the previous state-of-the-art. Furthermore, we report the
performance in a real-world security certification scenario. There still exists
a clear gap between machine recognition and human performance. We summarize our
experiments and present three challenges lying ahead in recent face
recognition. And we indicate several possible solutions towards these
challenges. We hope our work will stimulate the community's discussion of the
difference between research benchmark and real-world applications.
Deep Convolutional Neural Networks for Action Recognition Using Depth Map Sequences
Authors: Pichao Wang, Wanqing Li, Zhimin Gao, Jing Zhang, Chang Tang, Philip Ogunbona
Date: 2015-01-20 00:00:00
Tasks: Action Recognition, Temporal Action Localization

Recently, deep learning approach has achieved promising results in various
fields of computer vision. In this paper, a new framework called Hierarchical
Depth Motion Maps (HDMM) + 3 Channel Deep Convolutional Neural Networks
(3ConvNets) is proposed for human action recognition using depth map sequences.
Firstly, we rotate the original depth data in 3D pointclouds to mimic the
rotation of cameras, so that our algorithms can handle view variant cases.
Secondly, in order to effectively extract the body shape and motion
information, we generate weighted depth motion maps (DMM) at several temporal
scales, referred to as Hierarchical Depth Motion Maps (HDMM). Then, three
channels of ConvNets are trained on the HDMMs from three projected orthogonal
planes separately. The proposed algorithms are evaluated on MSRAction3D,
MSRAction3DExt, UTKinect-Action and MSRDailyActivity3D datasets respectively.
We also combine the last three datasets into a larger one (called Combined
Dataset) and test the proposed method on it. The results show that our approach
can achieve state-of-the-art results on the individual datasets and without
dramatical performance degradation on the Combined Dataset.
Slice Sampling for Probabilistic Programming
Authors: Razvan Ranca, Zoubin Ghahramani
Date: 2015-01-20 00:00:00
Tasks: Probabilistic Programming, regression

We introduce the first, general purpose, slice sampling inference engine for
probabilistic programs. This engine is released as part of StocPy, a new
Turing-Complete probabilistic programming language, available as a Python
library. We present a transdimensional generalisation of slice sampling which
is necessary for the inference engine to work on traces with different numbers
of random variables. We show that StocPy compares favourably to other PPLs in
terms of flexibility and usability, and that slice sampling can outperform
previously introduced inference methods. Our experiments include a logistic
regression, HMM, and Bayesian Neural Net.
Non-Verbal Communication Analysis in Victim-Offender Mediations
Authors: Víctor Ponce-López, Sergio Escalera, Marc Pérez, Oriol Janés, Xavier Baró
Date: 2014-11-25 00:00:00
Tasks: Binary Classification

In this paper we present a non-invasive ambient intelligence framework for
the semi-automatic analysis of non-verbal communication applied to the
restorative justice field. In particular, we propose the use of computer vision
and social signal processing technologies in real scenarios of Victim-Offender
Mediations, applying feature extraction techniques to multi-modal
audio-RGB-depth data. We compute a set of behavioral indicators that define
communicative cues from the fields of psychology and observational methodology.
We test our methodology on data captured in real world Victim-Offender
Mediation sessions in Catalonia in collaboration with the regional government.
We define the ground truth based on expert opinions when annotating the
observed social responses. Using different state-of-the-art binary
classification approaches, our system achieves recognition accuracies of 86%
when predicting satisfaction, and 79% when predicting both agreement and
receptivity. Applying a regression strategy, we obtain a mean deviation for the
predictions between 0.5 and 0.7 in the range [1-5] for the computed social
signals.
Sparse Bayesian Learning for EEG Source Localization
Authors: Sajib Saha, Frank de Hoog, Ya. I. Nesterets, Rajib Rana, M. Tahtali, T. E. Gureyev
Date: 2015-01-19 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Purpose: Localizing the sources of electrical activity from
electroencephalographic (EEG) data has gained considerable attention over the
last few years. In this paper, we propose an innovative source localization
method for EEG, based on Sparse Bayesian Learning (SBL). Methods: To better
specify the sparsity profile and to ensure efficient source localization, the
proposed approach considers grouping of the electrical current dipoles inside
human brain. SBL is used to solve the localization problem in addition with
imposed constraint that the electric current dipoles associated with the brain
activity are isotropic. Results: Numerical experiments are conducted on a
realistic head model that is obtained by segmentation of MRI images of the head
and includes four major components, namely the scalp, the skull, the
cerebrospinal fluid (CSF) and the brain, with appropriate relative conductivity
values. The results demonstrate that the isotropy constraint significantly
improves the performance of SBL. In a noiseless environment, the proposed
method was 1 found to accurately (with accuracy of >75%) locate up to 6
simultaneously active sources, whereas for SBL without the isotropy constraint,
the accuracy of finding just 3 simultaneously active sources was <75%.
Conclusions: Compared to the state-of-the-art algorithms, the proposed method
is potentially more consistent in specifying the sparsity profile of human
brain activity and is able to produce better source localization for EEG.
Implementable confidence sets in high dimensional regression
Authors: Alexandra Carpentier
Date: 2015-01-19 00:00:00
Tasks: regression, Vocal Bursts Intensity Prediction

We consider the setting of linear regression in high dimension. We focus on
the problem of constructing adaptive and honest confidence sets for the sparse
parameter \theta, i.e. we want to construct a confidence set for theta that
contains theta with high probability, and that is as small as possible. The l_2
diameter of a such confidence set should depend on the sparsity S of \theta -
the larger S, the wider the confidence set. However, in practice, S is unknown.
This paper focuses on constructing a confidence set for \theta which contains
\theta with high probability, whose diameter is adaptive to the unknown
sparsity S, and which is implementable in practice.
An Evolutionary Optimization Approach to Risk Parity Portfolio Selection
Authors: Ronald Hochreiter
Date: 2014-11-27 00:00:00
Tasks: Portfolio Optimization

In this paper we present an evolutionary optimization approach to solve the
risk parity portfolio selection problem. While there exist convex optimization
approaches to solve this problem when long-only portfolios are considered, the
optimization problem becomes non-trivial in the long-short case. To solve this
problem, we propose a genetic algorithm as well as a local search heuristic.
This algorithmic framework is able to compute solutions successfully. Numerical
results using real-world data substantiate the practicability of the approach
presented in this paper.
Statistical-mechanical analysis of pre-training and fine tuning in deep learning
Authors: Masayuki Ohzeki
Date: 2015-01-19 00:00:00
Tasks: Deep Learning

In this paper, we present a statistical-mechanical analysis of deep learning.
We elucidate some of the essential components of deep learning---pre-training
by unsupervised learning and fine tuning by supervised learning. We formulate
the extraction of features from the training data as a margin criterion in a
high-dimensional feature-vector space. The self-organized classifier is then
supplied with small amounts of labelled data, as in deep learning. Although we
employ a simple single-layer perceptron model, rather than directly analyzing a
multi-layer neural network, we find a nontrivial phase transition that is
dependent on the number of unlabelled data in the generalization error of the
resultant classifier. In this sense, we evaluate the efficacy of the
unsupervised learning component of deep learning. The analysis is performed by
the replica method, which is a sophisticated tool in statistical mechanics. We
validate our result in the manner of deep learning, using a simple iterative
algorithm to learn the weight vector on the basis of belief propagation.
Structure Learning in Bayesian Networks of Moderate Size by Efficient Sampling
Authors: Ru He, Jin Tian, Huaiqing Wu
Date: 2015-01-19 00:00:00

We study the Bayesian model averaging approach to learning Bayesian network
structures (DAGs) from data. We develop new algorithms including the first
algorithm that is able to efficiently sample DAGs according to the exact
structure posterior. The DAG samples can then be used to construct estimators
for the posterior of any feature. We theoretically prove good properties of our
estimators and empirically show that our estimators considerably outperform the
estimators from the previous state-of-the-art methods.
Reconstruction-free action inference from compressive imagers
Authors: Kuldeep Kulkarni, Pavan Turaga
Date: 2015-01-18 00:00:00
Tasks: Action Recognition, Compressive Sensing, Temporal Action Localization

Persistent surveillance from camera networks, such as at parking lots, UAVs,
etc., often results in large amounts of video data, resulting in significant
challenges for inference in terms of storage, communication and computation.
Compressive cameras have emerged as a potential solution to deal with the data
deluge issues in such applications. However, inference tasks such as action
recognition require high quality features which implies reconstructing the
original video data. Much work in compressive sensing (CS) theory is geared
towards solving the reconstruction problem, where state-of-the-art methods are
computationally intensive and provide low-quality results at high compression
rates. Thus, reconstruction-free methods for inference are much desired. In
this paper, we propose reconstruction-free methods for action recognition from
compressive cameras at high compression ratios of 100 and above. Recognizing
actions directly from CS measurements requires features which are mostly
nonlinear and thus not easily applicable. This leads us to search for such
properties that are preserved in compressive measurements. To this end, we
propose the use of spatio-temporal smashed filters, which are compressive
domain versions of pixel-domain matched filters. We conduct experiments on
publicly available databases and show that one can obtain recognition rates
that are comparable to the oracle method in uncompressed setup, even for high
compression ratios.