pwc-archive/papers-with-abstracts huggingface dataset:
OntoRich - A Support Tool for Semi-Automatic Ontology Enrichment and Evaluation
Authors: Adrian Groza, Gabriel Barbur, Bogdan Blaga
Date: 2013-04-19 00:00:00

This paper presents the OntoRich framework, a support tool for semi-automatic
ontology enrichment and evaluation. The WordNet is used to extract candidates
for dynamic ontology enrichment from RSS streams. With the integration of
OpenNLP the system gains access to syntactic analysis of the RSS news. The
enriched ontologies are evaluated against several qualitative metrics.
Hands-free Evolution of 3D-printable Objects via Eye Tracking
Authors: Nick Cheney, Jeff Clune, Jason Yosinski, Hod Lipson
Date: 2013-04-17 00:00:00

Interactive evolution has shown the potential to create amazing and complex
forms in both 2-D and 3-D settings. However, the algorithm is slow and users
quickly become fatigued. We propose that the use of eye tracking for
interactive evolution systems will both reduce user fatigue and improve
evolutionary success. We describe a systematic method for testing the
hypothesis that eye tracking driven interactive evolution will be a more
successful and easier-to-use design method than traditional interactive
evolution methods driven by mouse clicks. We provide preliminary results that
support the possibility of this proposal, and lay out future work to
investigate these advantages in extensive clinical trials.
Personalized Academic Research Paper Recommendation System
Authors: Joonseok Lee, Kisung Lee, Jennifer G. Kim
Date: 2013-04-19 00:00:00
Tasks: Articles, Collaborative Filtering, Recommendation Systems, text similarity

A huge number of academic papers are coming out from a lot of conferences and
journals these days. In these circumstances, most researchers rely on key-based
search or browsing through proceedings of top conferences and journals to find
their related work. To ease this difficulty, we propose a Personalized Academic
Research Paper Recommendation System, which recommends related articles, for
each researcher, that may be interesting to her/him. In this paper, we first
introduce our web crawler to retrieve research papers from the web. Then, we
define similarity between two research papers based on the text similarity
between them. Finally, we propose our recommender system developed using
collaborative filtering methods. Our evaluation results demonstrate that our
system recommends good quality research papers.
Solving WCSP by Extraction of Minimal Unsatisfiable Cores
Authors: Christophe Lecoutre, Nicolas Paris, Olivier Roussel, Sébastien Tabary
Date: 2013-04-19 00:00:00

Usual techniques to solve WCSP are based on cost transfer operations coupled
with a branch and bound algorithm. In this paper, we focus on an approach
integrating extraction and relaxation of Minimal Unsatisfiable Cores in order
to solve this problem. We decline our approach in two ways: an incomplete,
greedy, algorithm and a complete one.
Analytic Expressions for Stochastic Distances Between Relaxed Complex Wishart Distributions
Authors: Alejandro C. Frery, Abraão D. C. Nascimento, Renato J. Cintra
Date: 2013-04-19 00:00:00

The scaled complex Wishart distribution is a widely used model for multilook
full polarimetric SAR data whose adequacy has been attested in the literature.
Classification, segmentation, and image analysis techniques which depend on
this model have been devised, and many of them employ some type of
dissimilarity measure. In this paper we derive analytic expressions for four
stochastic distances between relaxed scaled complex Wishart distributions in
their most general form and in important particular cases. Using these
distances, inequalities are obtained which lead to new ways of deriving the
Bartlett and revised Wishart distances. The expressiveness of the four analytic
distances is assessed with respect to the variation of parameters. Such
distances are then used for deriving new tests statistics, which are proved to
have asymptotic chi-square distribution. Adopting the test size as a comparison
criterion, a sensitivity study is performed by means of Monte Carlo experiments
suggesting that the Bhattacharyya statistic outperforms all the others. The
power of the tests is also assessed. Applications to actual data illustrate the
discrimination and homogeneity identification capabilities of these distances.
A Joint Intensity and Depth Co-Sparse Analysis Model for Depth Map Super-Resolution
Authors: Martin Kiechle, Simon Hawe, Martin Kleinsteuber
Date: 2013-04-19 00:00:00
Tasks: Depth Map Super-Resolution, Super-Resolution

High-resolution depth maps can be inferred from low-resolution depth
measurements and an additional high-resolution intensity image of the same
scene. To that end, we introduce a bimodal co-sparse analysis model, which is
able to capture the interdependency of registered intensity and depth
information. This model is based on the assumption that the co-supports of
corresponding bimodal image structures are aligned when computed by a suitable
pair of analysis operators. No analytic form of such operators exist and we
propose a method for learning them from a set of registered training signals.
This learning process is done offline and returns a bimodal analysis operator
that is universally applicable to natural scenes. We use this to exploit the
bimodal co-sparse analysis model as a prior for solving inverse problems, which
leads to an efficient algorithm for depth map super-resolution.
Object Tracking in Videos: Approaches and Issues
Authors: Duc Phu Chau, François Bremond, Monique Thonnat
Date: 2013-04-18 00:00:00
Tasks: Object, Object Tracking

Mobile object tracking has an important role in the computer vision
applications. In this paper, we use a tracked target-based taxonomy to present
the object tracking algorithms. The tracked targets are divided into three
categories: points of interest, appearance and silhouette of mobile objects.
Advantages and limitations of the tracking approaches are also analyzed to find
the future directions in the object tracking domain.
Image Retrieval based on Bag-of-Words model
Authors: Jialu Liu
Date: 2013-04-18 00:00:00
Tasks: General Classification, image-classification, Image Classification, Image Retrieval, model, Object Recognition, Retrieval, Survey

This article gives a survey for bag-of-words (BoW) or bag-of-features model
in image retrieval system. In recent years, large-scale image retrieval shows
significant potential in both industry applications and research problems. As
local descriptors like SIFT demonstrate great discriminative power in solving
vision problems like object recognition, image classification and annotation,
more and more state-of-the-art large scale image retrieval systems are trying
to rely on them. A common way to achieve this is first quantizing local
descriptors into visual words, and then applying scalable textual indexing and
retrieval schemes. We call this model as bag-of-words or bag-of-features model.
The goal of this survey is to give an overview of this model and introduce
different strategies when building the system based on this model.
Interactive POMDP Lite: Towards Practical Planning to Predict and Exploit Intentions for Interacting with Self-Interested Agents
Authors: Trong Nghia Hoang, Kian Hsiang Low
Date: 2013-04-18 00:00:00

A key challenge in non-cooperative multi-agent systems is that of developing
efficient planning algorithms for intelligent agents to interact and perform
effectively among boundedly rational, self-interested agents (e.g., humans).
The practicality of existing works addressing this challenge is being
undermined due to either the restrictive assumptions of the other agents'
behavior, the failure in accounting for their rationality, or the prohibitively
expensive cost of modeling and predicting their intentions. To boost the
practicality of research in this field, we investigate how intention prediction
can be efficiently exploited and made practical in planning, thereby leading to
efficient intention-aware planning frameworks capable of predicting the
intentions of other agents and acting optimally with respect to their predicted
intentions. We show that the performance losses incurred by the resulting
planning policies are linearly bounded by the error of intention prediction.
Empirical evaluations through a series of stochastic games demonstrate that our
policies can achieve better and more robust performance than the
state-of-the-art algorithms.
Multiobjective optimization in Gene Expression Programming for Dew Point
Authors: Siddharth Shroff, Vipul Dabhi
Date: 2013-04-15 00:00:00
Tasks: Multiobjective Optimization

The processes occurring in climatic change evolution and their variations
play a major role in environmental engineering. Different techniques are used
to model the relationship between temperatures, dew point and relative
humidity. Gene expression programming is capable of modelling complex realities
with great accuracy, allowing, at the same time, the extraction of knowledge
from the evolved models compared to other learning algorithms. This research
aims to use Gene Expression Programming for modelling of dew point. Generally,
accuracy of the model is the only objective used by selection mechanism of GEP.
This will evolve large size models with low training error. To avoid this
situation, use of multiple objectives, like accuracy and size of the model are
preferred by Genetic Programming practitioners. Multi-objective problem finds a
set of solutions satisfying the objectives given by decision maker.
Multiobjective based GEP will be used to evolve simple models. Various
algorithms widely used for multi objective optimization like NSGA II and SPEA 2
are tested for different test cases. The results obtained thereafter gives idea
that SPEA 2 is better algorithm compared to NSGA II based on the features like
execution time, number of solutions obtained and convergence rate. Thus
compared to models obtained by GEP, multi-objective algorithms fetch better
solutions considering the dual objectives of fitness and size of the equation.
These simple models can be used to predict dew point.
Constraint Satisfaction over Generalized Staircase Constraints
Authors: Shubhadip Mitra, Partha Dutta, Arnab Bhattacharya
Date: 2013-04-18 00:00:00
Tasks: ARC

One of the key research interests in the area of Constraint Satisfaction
Problem (CSP) is to identify tractable classes of constraints and develop
efficient solutions for them. In this paper, we introduce generalized staircase
(GS) constraints which is an important generalization of one such tractable
class found in the literature, namely, staircase constraints. GS constraints
are of two kinds, down staircase (DS) and up staircase (US). We first examine
several properties of GS constraints, and then show that arc consistency is
sufficient to determine a solution to a CSP over DS constraints. Further, we
propose an optimal O(cd) time and space algorithm to compute arc consistency
for GS constraints where c is the number of constraints and d is the size of
the largest domain. Next, observing that arc consistency is not necessary for
solving a DSCSP, we propose a more efficient algorithm for solving it. With
regard to US constraints, arc consistency is not known to be sufficient to
determine a solution, and therefore, methods such as path consistency or
variable elimination are required. Since arc consistency acts as a subroutine
for these existing methods, replacing it by our optimal O(cd) arc consistency
algorithm produces a more efficient method for solving a USCSP.
Efficient network-guided multi-locus association mapping with graph cuts
Authors: Chloé-Agathe Azencott, Dominik Grimm, Mahito Sugiyama, Yoshinobu Kawahara, Karsten M. Borgwardt
Date: 2012-11-10 00:00:00

As an increasing number of genome-wide association studies reveal the
limitations of attempting to explain phenotypic heritability by single genetic
loci, there is growing interest for associating complex phenotypes with sets of
genetic loci. While several methods for multi-locus mapping have been proposed,
it is often unclear how to relate the detected loci to the growing knowledge
about gene pathways and networks. The few methods that take biological pathways
or networks into account are either restricted to investigating a limited
number of predetermined sets of loci, or do not scale to genome-wide settings.
  We present SConES, a new efficient method to discover sets of genetic loci
that are maximally associated with a phenotype, while being connected in an
underlying network. Our approach is based on a minimum cut reformulation of the
problem of selecting features under sparsity and connectivity constraints that
can be solved exactly and rapidly.
  SConES outperforms state-of-the-art competitors in terms of runtime, scales
to hundreds of thousands of genetic loci, and exhibits higher power in
detecting causal SNPs in simulation studies than existing methods. On flowering
time phenotypes and genotypes from Arabidopsis thaliana, SConES detects loci
that enable accurate phenotype prediction and that are supported by the
literature.
  Matlab code for SConES is available at
http://webdav.tuebingen.mpg.de/u/karsten/Forschung/scones/
On the Complexity of Trial and Error
Authors: Xiaohui Bei, Ning Chen, Shengyu Zhang
Date: 2012-05-06 00:00:00

Motivated by certain applications from physics, biochemistry, economics, and
computer science, in which the objects under investigation are not accessible
because of various limitations, we propose a trial-and-error model to examine
algorithmic issues in such situations. Given a search problem with a hidden
input, we are asked to find a valid solution, to find which we can propose
candidate solutions (trials), and use observed violations (errors), to prepare
future proposals. In accordance with our motivating applications, we consider
the fairly broad class of constraint satisfaction problems, and assume that
errors are signaled by a verification oracle in the format of the index of a
violated constraint (with the content of the constraint still hidden).
  Our discoveries are summarized as follows. On one hand, despite the seemingly
very little information provided by the verification oracle, efficient
algorithms do exist for a number of important problems. For the Nash, Core,
Stable Matching, and SAT problems, the unknown-input versions are as hard as
the corresponding known-input versions, up to a factor of polynomial. We
further give almost tight bounds on the latter two problems' trial
complexities. On the other hand, there are problems whose complexities are
substantially increased in the unknown-input model. In particular, no
time-efficient algorithms exist (under standard hardness assumptions) for Graph
Isomorphism and Group Isomorphism problems. The tools used to achieve these
results include order theory, strong ellipsoid method, and some non-standard
reductions.
  Our model investigates the value of information, and our results demonstrate
that the lack of input information can introduce various levels of extra
difficulty. The model exhibits intimate connections with (and we hope can also
serve as a useful supplement to) certain existing learning and complexity
theories.
Polygon Matching and Indexing Under Affine Transformations
Authors: Edgar Chávez, Ana C. Chávez-Cáliz, Jorge L. López-López
Date: 2013-04-18 00:00:00
Tasks: All

Given a collection $\{Z_1,Z_2,\ldots,Z_m\}$ of $n$-sided polygons in the
plane and a query polygon $W$ we give algorithms to find all $Z_\ell$ such that
$W=f(Z_\ell)$ with $f$ an unknown similarity transformation in time independent
of the size of the collection. If $f$ is a known affine transformation, we show
how to find all $Z_\ell$ such that $W=f(Z_\ell)$ in $O(n+\log(m))$ time.
  For a pair $W,W^\prime$ of polygons we can find all the pairs
$Z_\ell,Z_{\ell^\prime}$ such that $W=f(Z_\ell)$ and
$W^\prime=f(Z_{\ell^\prime})$ for an unknown affine transformation $f$ in
$O(m+n)$ time.
  For the case of triangles we also give bounds for the problem of matching
triangles with variable vertices, which is equivalent to affine matching
triangles in noisy conditions.
Learning using Local Membership Queries
Authors: Pranjal Awasthi, Vitaly Feldman, Varun Kanade
Date: 2012-11-05 00:00:00

We introduce a new model of membership query (MQ) learning, where the
learning algorithm is restricted to query points that are \emph{close} to
random examples drawn from the underlying distribution. The learning model is
intermediate between the PAC model (Valiant, 1984) and the PAC+MQ model (where
the queries are allowed to be arbitrary points).
  Membership query algorithms are not popular among machine learning
practitioners. Apart from the obvious difficulty of adaptively querying
labelers, it has also been observed that querying \emph{unnatural} points leads
to increased noise from human labelers (Lang and Baum, 1992). This motivates
our study of learning algorithms that make queries that are close to examples
generated from the data distribution.
  We restrict our attention to functions defined on the $n$-dimensional Boolean
hypercube and say that a membership query is local if its Hamming distance from
some example in the (random) training data is at most $O(\log(n))$. We show the
following results in this model:
  (i) The class of sparse polynomials (with coefficients in R) over $\{0,1\}^n$
is polynomial time learnable under a large class of \emph{locally smooth}
distributions using $O(\log(n))$-local queries. This class also includes the
class of $O(\log(n))$-depth decision trees.
  (ii) The class of polynomial-sized decision trees is polynomial time
learnable under product distributions using $O(\log(n))$-local queries.
  (iii) The class of polynomial size DNF formulas is learnable under the
uniform distribution using $O(\log(n))$-local queries in time
$n^{O(\log(\log(n)))}$.
  (iv) In addition we prove a number of results relating the proposed model to
the traditional PAC model and the PAC+MQ model.
Improvement/Extension of Modular Systems as Combinatorial Reengineering (Survey)
Authors: Mark Sh. Levin
Date: 2013-04-17 00:00:00
Tasks: Combinatorial Optimization, Multiple-choice, Survey

The paper describes development (improvement/extension) approaches for
composite (modular) systems (as combinatorial reengineering). The following
system improvement/extension actions are considered: (a) improvement of systems
component(s) (e.g., improvement of a system component, replacement of a system
component); (b) improvement of system component interconnection
(compatibility); (c) joint improvement improvement of system components(s) and
their interconnection; (d) improvement of system structure (replacement of
system part(s), addition of a system part, deletion of a system part,
modification of system structure). The study of system improvement approaches
involve some crucial issues: (i) scales for evaluation of system components and
component compatibility (quantitative scale, ordinal scale, poset-like scale,
scale based on interval multiset estimate), (ii) evaluation of integrated
system quality, (iii) integration methods to obtain the integrated system
quality. The system improvement/extension strategies can be examined as
seleciton/combination of the improvement action(s) above and as modification of
system structure. The strategies are based on combinatorial optimization
problems (e.g., multicriteria selection, knapsack problem, multiple choice
problem, combinatorial synthesis based on morphological clique problem,
assignment/reassignment problem, graph recoloring problem, spanning problems,
hotlink assignment). Here, heuristics are used. Various system
improvement/extension strategies are presented including illustrative numerical
examples.
The Mahalanobis distance for functional data with applications to classification
Authors: Esdras Joseph, Pedro Galeano, Rosa E. Lillo
Date: 2013-04-17 00:00:00
Tasks: Classification, General Classification

This paper presents a general notion of Mahalanobis distance for functional
data that extends the classical multivariate concept to situations where the
observed data are points belonging to curves generated by a stochastic process.
More precisely, a new semi-distance for functional observations that generalize
the usual Mahalanobis distance for multivariate datasets is introduced. For
that, the development uses a regularized square root inverse operator in
Hilbert spaces. Some of the main characteristics of the functional Mahalanobis
semi-distance are shown. Afterwards, new versions of several well known
functional classification procedures are developed using the Mahalanobis
distance for functional data as a measure of proximity between functional
observations. The performance of several well known functional classification
procedures are compared with those methods used in conjunction with the
Mahalanobis distance for functional data, with positive results, through a
Monte Carlo study and the analysis of two real data examples.
Robust Noise Filtering in Image Sequences
Authors: Soumaya Hichri, Faouzi Benzarti, Hamid Amiri
Date: 2013-04-17 00:00:00

Image sequences filtering have recently become a very important technical
problem especially with the advent of new technology in multimedia and video
systems applications. Often image sequences are corrupted by some amount of
noise introduced by the image sensor and therefore inherently present in the
imaging process. The main problem in the image sequences is how to deal with
spatio-temporal and non stationary signals. In this paper, we propose a robust
method for noise removal of image sequence based on coupled spatial and
temporal anisotropic diffusion. The idea is to achieve an adaptive smoothing in
both spatial and temporal directions, by solving a nonlinear diffusion
equation. This allows removing noise while preserving all spatial and temporal
discontinuities
Automated Switching System for Skin Pixel Segmentation in Varied Lighting
Authors: Ankit Chaudhary, Ankur Gupta
Date: 2013-04-17 00:00:00

In Computer Vision, colour-based spatial techniquesoften assume a static skin
colour model. However, skin colour perceived by a camera can change when
lighting changes. In common real environment multiple light sources impinge on
the skin. Moreover, detection techniques may vary when the image under study is
taken under different lighting condition than the one that was earlier under
consideration. Therefore, for robust skin pixel detection, a dynamic skin
colour model that can cope with the changes must be employed. This paper shows
that skin pixel detection in a digital colour image can be significantly
improved by employing automated colour space switching methods. In the root of
the switching technique which is employed in this study, lies the statistical
mean of value of the skin pixels in the image which in turn has been derived
from the Value, measures as a third component of the HSV. The study is based on
experimentations on a set of images where capture time conditions varying from
highly illuminated to almost dark.
Tracking of Fingertips and Centres of Palm using KINECT
Authors: J. L. Raheja, A. Chaudhary, K Singal
Date: 2013-04-17 00:00:00

Hand Gesture is a popular way to interact or control machines and it has been
implemented in many applications. The geometry of hand is such that it is hard
to construct in virtual environment and control the joints but the
functionality and DOF encourage researchers to make a hand like instrument.
This paper presents a novel method for fingertips detection and centres of
palms detection distinctly for both hands using MS KINECT in 3D from the input
image. KINECT facilitates us by providing the depth information of foreground
objects. The hands were segmented using the depth vector and centres of palms
were detected using distance transformation on inverse image. This result would
be used to feed the inputs to the robotic hands to emulate human hands
operation.