pwc-archive/papers-with-abstracts huggingface dataset:
Illumination-invariant face recognition from a single image across extreme pose using a dual dimension AAM ensemble in the thermal infrared spectrum
Authors: Reza Shoja Ghiass, Ognjen Arandjelovic, Hakim Bendada, Xavier Maldague
Date: 2013-06-07 00:00:00
Tasks: Face Recognition

Over the course of the last decade, infrared (IR) and particularly thermal IR
imaging based face recognition has emerged as a promising complement to
conventional, visible spectrum based approaches which continue to struggle when
applied in practice. While inherently insensitive to visible spectrum
illumination changes, IR data introduces specific challenges of its own, most
notably sensitivity to factors which affect facial heat emission patterns, e.g.
emotional state, ambient temperature, and alcohol intake. In addition, facial
expression and pose changes are more difficult to correct in IR images because
they are less rich in high frequency detail which is an important cue for
fitting any deformable model. In this paper we describe a novel method which
addresses these major challenges. Specifically, when comparing two thermal IR
images of faces, we mutually normalize their poses and facial expressions by
using an active appearance model (AAM) to generate synthetic images of the two
faces with a neutral facial expression and in the same view (the average of the
two input views). This is achieved by piecewise affine warping which follows
AAM fitting. A major contribution of our work is the use of an AAM ensemble in
which each AAM is specialized to a particular range of poses and a particular
region of the thermal IR face space. Combined with the contributions from our
previous work which addressed the problem of reliable AAM fitting in the
thermal IR spectrum, and the development of a person-specific representation
robust to transient changes in the pattern of facial temperature emissions, the
proposed ensemble framework accurately matches faces across the full range of
yaw from frontal to profile, even in the presence of scale variation (e.g. due
to the varying distance of a subject from the camera).
Infrared face recognition: a literature review
Authors: Reza Shoja Ghiass, Ognjen Arandjelovic, Hakim Bendada, Xavier Maldague
Date: 2013-06-07 00:00:00
Tasks: Face Recognition

Automatic face recognition (AFR) is an area with immense practical potential
which includes a wide range of commercial and law enforcement applications, and
it continues to be one of the most active research areas of computer vision.
Even after over three decades of intense research, the state-of-the-art in AFR
continues to improve, benefiting from advances in a range of different fields
including image processing, pattern recognition, computer graphics and
physiology. However, systems based on visible spectrum images continue to face
challenges in the presence of illumination, pose and expression changes, as
well as facial disguises, all of which can significantly decrease their
accuracy. Amongst various approaches which have been proposed in an attempt to
overcome these limitations, the use of infrared (IR) imaging has emerged as a
particularly promising research direction. This paper presents a comprehensive
and timely review of the literature on this subject.
Autonomous search for a diffusive source in an unknown environment
Authors: Branko Ristic, Alex Skvortsov, Andrew Walker
Date: 2013-06-07 00:00:00

The paper presents an approach to olfactory search for a diffusive emitting
source of tracer (e.g. aerosol, gas) in an environment with unknown map of
randomly placed and shaped obstacles.
  The measurements of tracer concentration are sporadic, noisy and without
directional information. The search domain is discretised and modelled by a
finite two-dimensional lattice. The links is the lattice represent the
traversable paths for emitted particles and for the searcher. A missing link in
the lattice indicates a blocked paths, due to the walls or obstacles. The
searcher must simultaneously estimate the source parameters, the map of the
search domain and its own location within the map. The solution is formulated
in the sequential Bayesian framework and implemented as a Rao-Blackwellised
particle filter with information-driven motion control. The numerical results
demonstrate the concept and its performance.
Deep Learning of Representations: Looking Forward
Authors: Yoshua Bengio
Date: 2013-05-02 00:00:00
Tasks: Deep Learning

Deep learning research aims at discovering learning algorithms that discover
multiple levels of distributed representations, with higher levels representing
more abstract concepts. Although the study of deep learning has already led to
impressive theoretical results, learning algorithms and breakthrough
experiments, several challenges lie ahead. This paper proposes to examine some
of these challenges, centering on the questions of scaling deep learning
algorithms to much larger models and datasets, reducing optimization
difficulties due to ill-conditioning or local minima, designing more efficient
and powerful inference and sampling procedures, and learning to disentangle the
factors of variation underlying the observed data. It also proposes a few
forward-looking research directions aimed at overcoming these challenges.
North Atlantic Right Whale Contact Call Detection
Authors: Rami Abousleiman, Guangzhi Qu, Osamah Rawashdeh
Date: 2013-04-30 00:00:00

The North Atlantic right whale (Eubalaena glacialis) is an endangered
species. These whales continuously suffer from deadly vessel impacts alongside
the eastern coast of North America. There have been countless efforts to save
the remaining 350 - 400 of them. One of the most prominent works is done by
Marinexplore and Cornell University. A system of hydrophones linked to
satellite connected-buoys has been deployed in the whales habitat. These
hydrophones record and transmit live sounds to a base station. These recording
might contain the right whale contact call as well as many other noises. The
noise rate increases rapidly in vessel-busy areas such as by the Boston harbor.
This paper presents and studies the problem of detecting the North Atlantic
right whale contact call with the presence of noise and other marine life
sounds. A novel algorithm was developed to preprocess the sound waves before a
tree based hierarchical classifier is used to classify the data and provide a
score. The developed model was trained with 30,000 data points made available
through the Cornell University Whale Detection Challenge program. Results
showed that the developed algorithm had close to 85% success rate in detecting
the presence of the North Atlantic right whale.
Extending Universal Intelligence Models with Formal Notion of Representation
Authors: Alexey Potapov, Sergey Rodionov
Date: 2013-06-06 00:00:00
Tasks: Model Optimization

Solomonoff induction is known to be universal, but incomputable. Its
approximations, namely, the Minimum Description (or Message) Length (MDL)
principles, are adopted in practice in the efficient, but non-universal form.
Recent attempts to bridge this gap leaded to development of the
Representational MDL principle that originates from formal decomposition of the
task of induction. In this paper, possible extension of the RMDL principle in
the context of universal intelligence agents is considered, for which
introduction of representations is shown to be an unavoidable meta-heuristic
and a step toward efficient general intelligence. Hierarchical representations
and model optimization with the use of information-theoretic interpretation of
the adaptive resonance are also discussed.
Policy Search: Any Local Optimum Enjoys a Global Performance Guarantee
Authors: Bruno Scherrer, Matthieu Geist
Date: 2013-06-06 00:00:00
Tasks: Reinforcement Learning

Local Policy Search is a popular reinforcement learning approach for handling
large state spaces. Formally, it searches locally in a paramet erized policy
space in order to maximize the associated value function averaged over some
predefined distribution. It is probably commonly b elieved that the best one
can hope in general from such an approach is to get a local optimum of this
criterion. In this article, we show th e following surprising result:
\emph{any} (approximate) \emph{local optimum} enjoys a \emph{global performance
guarantee}. We compare this g uarantee with the one that is satisfied by Direct
Policy Iteration, an approximate dynamic programming algorithm that does some
form of Poli cy Search: if the approximation error of Local Policy Search may
generally be bigger (because local search requires to consider a space of s
tochastic policies), we argue that the concentrability coefficient that appears
in the performance bound is much nicer. Finally, we discuss several practical
and theoretical consequences of our analysis.
Kernel Mean Estimation and Stein's Effect
Authors: Krikamol Muandet, Kenji Fukumizu, Bharath Sriperumbudur, Arthur Gretton, Bernhard Schölkopf
Date: 2013-06-04 00:00:00

A mean function in reproducing kernel Hilbert space, or a kernel mean, is an
important part of many applications ranging from kernel principal component
analysis to Hilbert-space embedding of distributions. Given finite samples, an
empirical average is the standard estimate for the true kernel mean. We show
that this estimator can be improved via a well-known phenomenon in statistics
called Stein's phenomenon. After consideration, our theoretical analysis
reveals the existence of a wide class of estimators that are better than the
standard. Focusing on a subset of this class, we propose efficient shrinkage
estimators for the kernel mean. Empirical evaluations on several benchmark
applications clearly demonstrate that the proposed estimators outperform the
standard kernel mean estimator.
Highly Scalable, Parallel and Distributed AdaBoost Algorithm using Light Weight Threads and Web Services on a Network of Multi-Core Machines
Authors: Munther Abualkibash, Ahmed Elsayed, Ausif Mahmood
Date: 2013-06-06 00:00:00
Tasks: CPU, Face Detection, object-detection, Object Detection, Real-Time Object Detection

AdaBoost is an important algorithm in machine learning and is being widely
used in object detection. AdaBoost works by iteratively selecting the best
amongst weak classifiers, and then combines several weak classifiers to obtain
a strong classifier. Even though AdaBoost has proven to be very effective, its
learning execution time can be quite large depending upon the application e.g.,
in face detection, the learning time can be several days. Due to its increasing
use in computer vision applications, the learning time needs to be drastically
reduced so that an adaptive near real time object detection system can be
incorporated. In this paper, we develop a hybrid parallel and distributed
AdaBoost algorithm that exploits the multiple cores in a CPU via light weight
threads, and also uses multiple machines via a web service software
architecture to achieve high scalability. We present a novel hierarchical web
services based distributed architecture and achieve nearly linear speedup up to
the number of processors available to us. In comparison with the previously
published work, which used a single level master-slave parallel and distributed
implementation [1] and only achieved a speedup of 2.66 on four nodes, we
achieve a speedup of 95.1 on 31 workstations each having a quad-core processor,
resulting in a learning time of only 4.8 seconds per feature.
K-Algorithm A Modified Technique for Noise Removal in Handwritten Documents
Authors: Kanika Bansal, Rajiv Kumar
Date: 2013-06-06 00:00:00
Tasks: Binarization, Optical Character Recognition (OCR)

OCR has been an active research area since last few decades. OCR performs the
recognition of the text in the scanned document image and converts it into
editable form. The OCR process can have several stages like pre-processing,
segmentation, recognition and post processing. The pre-processing stage is a
crucial stage for the success of OCR, which mainly deals with noise removal. In
the present paper, a modified technique for noise removal named as K-Algorithm
has been proposed, which has two stages as filtering and binarization. The
proposed technique shows improvised results in comparison to median filtering
technique.
Bayesian Inference of Natural Rankings in Incomplete Competition Networks
Authors: Juyong Park, Soon-Hyung Yook
Date: 2013-06-06 00:00:00
Tasks: Bayesian Inference

Competition between a complex system's constituents and a corresponding
reward mechanism based on it have profound influence on the functioning,
stability, and evolution of the system. But determining the dominance hierarchy
or ranking among the constituent parts from the strongest to the weakest --
essential in determining reward or penalty -- is almost always an ambiguous
task due to the incomplete nature of competition networks. Here we introduce
``Natural Ranking," a desirably unambiguous ranking method applicable to a
complete (full) competition network, and formulate an analytical model based on
the Bayesian formula inferring the expected mean and error of the natural
ranking of nodes from an incomplete network. We investigate its potential and
uses in solving issues in ranking by applying to a real-world competition
network of economic and social importance.
Ontology-based Data Access: A Study through Disjunctive Datalog, CSP, and MMSNP
Authors: Meghyn Bienvenu, Balder ten Cate, Carsten Lutz, Frank Wolter
Date: 2013-01-28 00:00:00
Tasks: Negation

Ontology-based data access is concerned with querying incomplete data sources
in the presence of domain-specific knowledge provided by an ontology. A central
notion in this setting is that of an ontology-mediated query, which is a
database query coupled with an ontology. In this paper, we study several
classes of ontology-mediated queries, where the database queries are given as
some form of conjunctive query and the ontologies are formulated in description
logics or other relevant fragments of first-order logic, such as the guarded
fragment and the unary-negation fragment. The contributions of the paper are
three-fold. First, we characterize the expressive power of ontology-mediated
queries in terms of fragments of disjunctive datalog. Second, we establish
intimate connections between ontology-mediated queries and constraint
satisfaction problems (CSPs) and their logical generalization, MMSNP formulas.
Third, we exploit these connections to obtain new results regarding (i)
first-order rewritability and datalog-rewritability of ontology-mediated
queries, (ii) P/NP dichotomies for ontology-mediated queries, and (iii) the
query containment problem for ontology-mediated queries.
PyHST2: an hybrid distributed code for high speed tomographic reconstruction with iterative reconstruction and a priori knowledge capabilities
Authors: Alessandro Mirone, Emmanuelle Gouillart, Emmanuel Brun, Paul Tafforeau, Jerome Kieffer
Date: 2013-06-06 00:00:00

We present the PyHST2 code which is in service at ESRF for phase-contrast and
absorption tomography. This code has been engineered to sustain the high data
flow typical of the third generation synchrotron facilities (10 terabytes per
experiment) by adopting a distributed and pipelined architecture. The code
implements, beside a default filtered backprojection reconstruction, iterative
reconstruction techniques with a-priori knowledge. These latter are used to
improve the reconstruction quality or in order to reduce the required data
volume and reach a given quality goal. The implemented a-priori knowledge
techniques are based on the total variation penalisation and a new recently
found convex functional which is based on overlapping patches.
  We give details of the different methods and their implementations while the
code is distributed under free license.
  We provide methods for estimating, in the absence of ground-truth data, the
optimal parameters values for a-priori techniques.
Geometric operations implemented by conformal geometric algebra neural nodes
Authors: Eckhard Hitzer
Date: 2013-06-06 00:00:00

Geometric algebra is an optimal frame work for calculating with vectors. The
geometric algebra of a space includes elements that represent all the its
subspaces (lines, planes, volumes, ...). Conformal geometric algebra expands
this approach to elementary representations of arbitrary points, point pairs,
lines, circles, planes and spheres. Apart from including curved objects,
conformal geometric algebra has an elegant unified quaternion like
representation for all proper and improper Euclidean transformations, including
reflections at spheres, general screw transformations and scaling. Expanding
the concepts of real and complex neurons we arrive at the new powerful concept
of conformal geometric algebra neurons. These neurons can easily take the above
mentioned geometric objects or sets of these objects as inputs and apply a wide
range of geometric transformations via the geometric algebra valued weights.
The User Feedback on SentiWordNet
Authors: Andrea Esuli
Date: 2013-06-06 00:00:00

With the release of SentiWordNet 3.0 the related Web interface has been
restyled and improved in order to allow users to submit feedback on the
SentiWordNet entries, in the form of the suggestion of alternative triplets of
values for an entry. This paper reports on the release of the user feedback
collected so far and on the plans for the future.
Table of Content detection using Machine Learning
Authors: Rachana Parikh, Avani R. Vasant
Date: 2013-06-06 00:00:00
Tasks: BIG-bench Machine Learning, Information Retrieval, Retrieval

Table of content (TOC) detection has drawn attention now a day because it
plays an important role in digitization of multipage document. Generally book
document is multipage document. So it becomes necessary to detect Table of
Content page for easy navigation of multipage document and also to make
information retrieval faster for desirable data from the multipage document.
All the Table of content pages follow the different layout, different way of
presenting the contents of the document like chapter, section, subsection etc.
This paper introduces a new method to detect Table of content using machine
learning technique with different features. With the main aim to detect Table
of Content pages is to structure the document according to their contents.
Performance analysis of unsupervised feature selection methods
Authors: A. Nisthana Parveen, H. Hannah Inbarani, E. N. Sathishkumar
Date: 2013-06-06 00:00:00
Tasks: Classification, feature selection, General Classification

Feature selection (FS) is a process which attempts to select more informative
features. In some cases, too many redundant or irrelevant features may
overpower main features for classification. Feature selection can remedy this
problem and therefore improve the prediction accuracy and reduce the
computational overhead of classification algorithms. The main aim of feature
selection is to determine a minimal feature subset from a problem domain while
retaining a suitably high accuracy in representing the original features. In
this paper, Principal Component Analysis (PCA), Rough PCA, Unsupervised Quick
Reduct (USQR) algorithm and Empirical Distribution Ranking (EDR) approaches are
applied to discover discriminative features that will be the most adequate ones
for classification. Efficiency of the approaches is evaluated using standard
classification metrics.
A Fuzzy Based Approach to Text Mining and Document Clustering
Authors: Sumit Goswami, Mayank Singh Shishodia
Date: 2013-06-06 00:00:00
Tasks: Clustering

Fuzzy logic deals with degrees of truth. In this paper, we have shown how to
apply fuzzy logic in text mining in order to perform document clustering. We
took an example of document clustering where the documents had to be clustered
into two categories. The method involved cleaning up the text and stemming of
words. Then, we chose m number of features which differ significantly in their
word frequencies (WF), normalized by document length, between documents
belonging to these two clusters. The documents to be clustered were represented
as a collection of m normalized WF values. Fuzzy c-means (FCM) algorithm was
used to cluster these documents into two clusters. After the FCM execution
finished, the documents in the two clusters were analysed for the values of
their respective m features. It was known that documents belonging to a
document type, say X, tend to have higher WF values for some particular
features. If the documents belonging to a cluster had higher WF values for
those same features, then that cluster was said to represent X. By fuzzy logic,
we not only get the cluster name, but also the degree to which a document
belongs to a cluster.
Verdict Accuracy of Quick Reduct Algorithm using Clustering and Classification Techniques for Gene Expression Data
Authors: T. Chandrasekhar, K. Thangavel, E. N. Sathishkumar
Date: 2013-06-06 00:00:00
Tasks: Clustering, Diagnostic, feature selection, General Classification

In most gene expression data, the number of training samples is very small
compared to the large number of genes involved in the experiments. However,
among the large amount of genes, only a small fraction is effective for
performing a certain task. Furthermore, a small subset of genes is desirable in
developing gene expression based diagnostic tools for delivering reliable and
understandable results. With the gene selection results, the cost of biological
experiment and decision can be greatly reduced by analyzing only the marker
genes. An important application of gene expression data in functional genomics
is to classify samples according to their gene expression profiles. Feature
selection (FS) is a process which attempts to select more informative features.
It is one of the important steps in knowledge discovery. Conventional
supervised FS methods evaluate various feature subsets using an evaluation
function or metric to select only those features which are related to the
decision classes of the data under consideration. This paper studies a feature
selection method based on rough set theory. Further K-Means, Fuzzy C-Means
(FCM) algorithm have implemented for the reduced feature set without
considering class labels. Then the obtained results are compared with the
original class labels. Back Propagation Network (BPN) has also been used for
classification. Then the performance of K-Means, FCM, and BPN are analyzed
through the confusion matrix. It is found that the BPN is performing well
comparatively.
Recognition of Indian Sign Language in Live Video
Authors: Joyeeta Singha, Karen Das
Date: 2013-06-06 00:00:00
Tasks: Sign Language Recognition

Sign Language Recognition has emerged as one of the important area of
research in Computer Vision. The difficulty faced by the researchers is that
the instances of signs vary with both motion and appearance. Thus, in this
paper a novel approach for recognizing various alphabets of Indian Sign
Language is proposed where continuous video sequences of the signs have been
considered. The proposed system comprises of three stages: Preprocessing stage,
Feature Extraction and Classification. Preprocessing stage includes skin
filtering, histogram matching. Eigen values and Eigen Vectors were considered
for feature extraction stage and finally Eigen value weighted Euclidean
distance is used to recognize the sign. It deals with bare hands, thus allowing
the user to interact with the system in natural way. We have considered 24
different alphabets in the video sequences and attained a success rate of
96.25%.