pwc-archive/papers-with-abstracts huggingface dataset:
A Novel Method for the Recognition of Isolated Handwritten Arabic Characters
Authors: Ahmed Sahlol, Cheng Suen
Date: 2014-02-26 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR)

There are many difficulties facing a handwritten Arabic recognition system
such as unlimited variation in human handwriting, similarities of distinct
character shapes, interconnections of neighbouring characters and their
position in the word. The typical Optical Character Recognition (OCR) systems
are based mainly on three stages, preprocessing, features extraction and
recognition. This paper proposes new methods for handwritten Arabic character
recognition which is based on novel preprocessing operations including
different kinds of noise removal also different kind of features like
structural, Statistical and Morphological features from the main body of the
character and also from the secondary components. Evaluation of the accuracy of
the selected features is made. The system was trained and tested by back
propagation neural network with CENPRMI dataset. The proposed algorithm
obtained promising results as it is able to recognize 88% of our test set
accurately. In Comparable with other related works we find that our result is
the highest among other published works.
Even more generic solution construction in Valuation-Based Systems
Authors: Jordi Roca-Lacostena, JesUs Cerquides
Date: 2014-02-26 00:00:00

Valuation algebras abstract a large number of formalisms for automated
reasoning and enable the definition of generic inference procedures. Many of
these formalisms provide some notions of solutions. Typical examples are
satisfying assignments in constraint systems, models in logics or solutions to
linear equation systems.
  Recently, formal requirements for the presence of solutions and a generic
algorithm for solution construction based on the results of a previously
executed inference scheme have been proposed in the literature. Unfortunately,
the formalization of Pouly and Kohlas relies on a theorem for which we provide
a counter example. In spite of that, the mainline of the theory described is
correct, although some of the necessary conditions to apply some of the
algorithms have to be revised. To fix the theory, we generalize some of their
definitions and provide correct sufficient conditions for the algorithms. As a
result, we get a more general and corrected version of the already existing
theory.
Evolutionary solving of the debts' clearing problem
Authors: Csaba Patcas, Attila Bartha
Date: 2014-02-26 00:00:00

The debts' clearing problem is about clearing all the debts in a group of n
entities (persons, companies etc.) using a minimal number of money transaction
operations. The problem is known to be NP-hard in the strong sense. As for many
intractable problems, techniques from the field of artificial intelligence are
useful in finding solutions close to optimum for large inputs. An evolutionary
algorithm for solving the debts' clearing problem is proposed.
Renewable Energy Prediction using Weather Forecasts for Optimal Scheduling in HPC Systems
Authors: Ankur Sahai
Date: 2014-02-26 00:00:00
Tasks: BIG-bench Machine Learning, Scheduling

The objective of the GreenPAD project is to use green energy (wind, solar and
biomass) for powering data-centers that are used to run HPC jobs. As a part of
this it is important to predict the Renewable (Wind) energy for efficient
scheduling (executing jobs that require higher energy when there is more green
energy available and vice-versa). For predicting the wind energy we first
analyze the historical data to find a statistical model that gives relation
between wind energy and weather attributes. Then we use this model based on the
weather forecast data to predict the green energy availability in the future.
Using the green energy prediction obtained from the statistical model we are
able to precompute job schedules for maximizing the green energy utilization in
the future. We propose a model which uses live weather data in addition to
machine learning techniques (which can predict future deviations in weather
conditions based on current deviations from the forecast) to make on-the-fly
changes to the precomputed schedule (based on green energy prediction).
  For this we first analyze the data using histograms and simple statistical
tools such as correlation. In addition we build (correlation) regression model
for finding the relation between wind energy availability and weather
attributes (temperature, cloud cover, air pressure, wind speed / direction,
precipitation and sunshine). We also analyze different algorithms and machine
learning techniques for optimizing the job schedules for maximizing the green
energy utilization.
Modelling the Lexicon in Unsupervised Part of Speech Induction
Authors: Greg Dubbin, Phil Blunsom
Date: 2014-02-26 00:00:00
Proceeding: EACL 2014 4
Tasks: TAG, Vocal Bursts Type Prediction

Automatically inducing the syntactic part-of-speech categories for words in
text is a fundamental task in Computational Linguistics. While the performance
of unsupervised tagging models has been slowly improving, current
state-of-the-art systems make the obviously incorrect assumption that all
tokens of a given word type must share a single part-of-speech tag. This
one-tag-per-type heuristic counters the tendency of Hidden Markov Model based
taggers to over generate tags for a given word type. However, it is clearly
incompatible with basic syntactic theory. In this paper we extend a
state-of-the-art Pitman-Yor Hidden Markov Model tagger with an explicit model
of the lexicon. In doing so we are able to incorporate a soft bias towards
inducing few tags per type. We develop a particle filter for drawing samples
from the posterior of our model and present empirical results that show that
our model is competitive with and faster than the state-of-the-art without
making any unrealistic restrictions.
Solving MaxSAT and #SAT on structured CNF formulas
Authors: Sigve Hortemo Sæther, Jan Arne Telle, Martin Vatshelle
Date: 2014-02-26 00:00:00

In this paper we propose a structural parameter of CNF formulas and use it to
identify instances of weighted MaxSAT and #SAT that can be solved in polynomial
time. Given a CNF formula we say that a set of clauses is precisely satisfiable
if there is some complete assignment satisfying these clauses only. Let the
ps-value of the formula be the number of precisely satisfiable sets of clauses.
Applying the notion of branch decompositions to CNF formulas and using ps-value
as cut function, we define the ps-width of a formula. For a formula given with
a decomposition of polynomial ps-width we show dynamic programming algorithms
solving weighted MaxSAT and #SAT in polynomial time. Combining with results of
'Belmonte and Vatshelle, Graph classes with structured neighborhoods and
algorithmic applications, Theor. Comput. Sci. 511: 54-65 (2013)' we get
polynomial-time algorithms solving weighted MaxSAT and #SAT for some classes of
structured CNF formulas. For example, we get $O(m^2(m + n)s)$ algorithms for
formulas $F$ of $m$ clauses and $n$ variables and size $s$, if $F$ has a linear
ordering of the variables and clauses such that for any variable $x$ occurring
in clause $C$, if $x$ appears before $C$ then any variable between them also
occurs in $C$, and if $C$ appears before $x$ then $x$ occurs also in any clause
between them. Note that the class of incidence graphs of such formulas do not
have bounded clique-width.
Clustering Multidimensional Data with PSO based Algorithm
Authors: Jayshree Ghorpade-Aher, Vishakha A. Metre
Date: 2014-02-26 00:00:00
Tasks: Clustering

Data clustering is a recognized data analysis method in data mining whereas
K-Means is the well known partitional clustering method, possessing pleasant
features. We observed that, K-Means and other partitional clustering techniques
suffer from several limitations such as initial cluster centre selection,
preknowledge of number of clusters, dead unit problem, multiple cluster
membership and premature convergence to local optima. Several optimization
methods are proposed in the literature in order to solve clustering
limitations, but Swarm Intelligence (SI) has achieved its remarkable position
in the concerned area. Particle Swarm Optimization (PSO) is the most popular SI
technique and one of the favorite areas of researchers. In this paper, we
present a brief overview of PSO and applicability of its variants to solve
clustering challenges. Also, we propose an advanced PSO algorithm named as
Subtractive Clustering based Boundary Restricted Adaptive Particle Swarm
Optimization (SC-BR-APSO) algorithm for clustering multidimensional data. For
comparison purpose, we have studied and analyzed various algorithms such as
K-Means, PSO, K-Means-PSO, Hybrid Subtractive + PSO, BRAPSO, and proposed
algorithm on nine different datasets. The motivation behind proposing
SC-BR-APSO algorithm is to deal with multidimensional data clustering, with
minimum error rate and maximum convergence rate.
Deconstruction of compound objects from image sets
Authors: Anton van den Hengel, John Bastian, Anthony Dick, Lachlan Fleming
Date: 2014-02-26 00:00:00

We propose a method to recover the structure of a compound object from
multiple silhouettes. Structure is expressed as a collection of 3D primitives
chosen from a pre-defined library, each with an associated pose. This has
several advantages over a volume or mesh representation both for estimation and
the utility of the recovered model. The main challenge in recovering such a
model is the combinatorial number of possible arrangements of parts. We address
this issue by exploiting the sparse nature of the problem, and show that our
method scales to objects constructed from large libraries of parts.
Active spline model: A shape based model-interactive segmentation
Authors: Jen Hong Tan, U. Rajendra Acharya
Date: 2014-02-26 00:00:00
Tasks: Interactive Segmentation, model, Segmentation

Rarely in literature a method of segmentation cares for the edit after the
algorithm delivers. They provide no solution when segmentation goes wrong. We
propose to formulate point distribution model in terms of
centripetal-parameterized Catmull-Rom spline. Such fusion brings interactivity
to model-based segmentation, so that edit is better handled. When the delivered
segment is unsatisfactory, user simply shifts points to vary the curve. We ran
the method on three disparate imaging modalities and achieved an average
overlap of 0.879 for automated lung segmentation on chest radiographs. The edit
afterward improved the average overlap to 0.945, with a minimum of 0.925. The
source code and the demo video are available at http://wp.me/p3vCKy-2S
Large-margin Learning of Compact Binary Image Encodings
Authors: Sakrapee Paisitkriangkrai, Chunhua Shen, Anton Van Den Hengel
Date: 2014-02-26 00:00:00
Tasks: Content-Based Image Retrieval, General Classification, image-classification, Image Classification, Image Retrieval, Retrieval

The use of high-dimensional features has become a normal practice in many
computer vision applications. The large dimension of these features is a
limiting factor upon the number of data points which may be effectively stored
and processed, however. We address this problem by developing a novel approach
to learning a compact binary encoding, which exploits both pair-wise proximity
and class-label information on training data set. Exploiting this extra
information allows the development of encodings which, although compact,
outperform the original high-dimensional features in terms of final
classification or retrieval performance. The method is general, in that it is
applicable to both non-parametric and parametric learning methods. This
generality means that the embedded features are suitable for a wide variety of
computer vision tasks, such as image classification and content-based image
retrieval. Experimental results demonstrate that the new compact descriptor
achieves an accuracy comparable to, and in some cases better than, the visual
descriptor in the original space despite being significantly more compact.
Moreover, any convex loss function and convex regularization penalty (e.g., $
\ell_p $ norm with $ p \ge 1 $) can be incorporated into the framework, which
provides future flexibility.
LSSVM-ABC Algorithm for Stock Price prediction
Authors: Osman Hegazy, Omar S. Soliman, Mustafa Abdul Salam
Date: 2014-02-25 00:00:00
Tasks: Prediction, Stock Price Prediction

In this paper, Artificial Bee Colony (ABC) algorithm which inspired from the
behavior of honey bees swarm is presented. ABC is a stochastic population-based
evolutionary algorithm for problem solving. ABC algorithm, which is considered
one of the most recently swarm intelligent techniques, is proposed to optimize
least square support vector machine (LSSVM) to predict the daily stock prices.
The proposed model is based on the study of stocks historical data, technical
indicators and optimizing LSSVM with ABC algorithm. ABC selects best free
parameters combination for LSSVM to avoid over-fitting and local minima
problems and improve prediction accuracy. LSSVM optimized by Particle swarm
optimization (PSO) algorithm, LSSVM, and ANN techniques are used for comparison
with proposed model. Proposed model tested with twenty datasets representing
different sectors in S&P 500 stock market. Results presented in this paper show
that the proposed model has fast convergence speed, and it also achieves better
accuracy than compared techniques in most cases.
Oracle-Based Robust Optimization via Online Learning
Authors: Aharon Ben-Tal, Elad Hazan, Tomer Koren, Shie Mannor
Date: 2014-02-25 00:00:00

Robust optimization is a common framework in optimization under uncertainty
when the problem parameters are not known, but it is rather known that the
parameters belong to some given uncertainty set. In the robust optimization
framework the problem solved is a min-max problem where a solution is judged
according to its performance on the worst possible realization of the
parameters. In many cases, a straightforward solution of the robust
optimization problem of a certain type requires solving an optimization problem
of a more complicated type, and in some cases even NP-hard. For example,
solving a robust conic quadratic program, such as those arising in robust SVM,
ellipsoidal uncertainty leads in general to a semidefinite program. In this
paper we develop a method for approximately solving a robust optimization
problem using tools from online convex optimization, where in every stage a
standard (non-robust) optimization program is solved. Our algorithms find an
approximate robust solution using a number of calls to an oracle that solves
the original (non-robust) problem that is inversely proportional to the square
of the target accuracy.
Unit Tests for Stochastic Optimization
Authors: Tom Schaul, Ioannis Antonoglou, David Silver
Date: 2013-12-20 00:00:00
Tasks: Stochastic Optimization

Optimization by stochastic gradient descent is an important component of many
large-scale machine learning algorithms. A wide variety of such optimization
algorithms have been devised; however, it is unclear whether these algorithms
are robust and widely applicable across many different optimization landscapes.
In this paper we develop a collection of unit tests for stochastic
optimization. Each unit test rapidly evaluates an optimization algorithm on a
small-scale, isolated, and well-understood difficulty, rather than in
real-world scenarios where many such issues are entangled. Passing these unit
tests is not sufficient, but absolutely necessary for any algorithms with
claims to generality or robustness. We give initial quantitative and
qualitative results on numerous established algorithms. The testing framework
is open-source, extensible, and easy to apply to new algorithms.
The IBMAP approach for Markov networks structure learning
Authors: Federico Schlüter, Facundo Bromberg, Alejandro Edera
Date: 2013-01-16 00:00:00
Tasks: Evolutionary Algorithms

In this work we consider the problem of learning the structure of Markov
networks from data. We present an approach for tackling this problem called
IBMAP, together with an efficient instantiation of the approach: the IBMAP-HC
algorithm, designed for avoiding important limitations of existing
independence-based algorithms. These algorithms proceed by performing
statistical independence tests on data, trusting completely the outcome of each
test. In practice tests may be incorrect, resulting in potential cascading
errors and the consequent reduction in the quality of the structures learned.
IBMAP contemplates this uncertainty in the outcome of the tests through a
probabilistic maximum-a-posteriori approach. The approach is instantiated in
the IBMAP-HC algorithm, a structure selection strategy that performs a
polynomial heuristic local search in the space of possible structures. We
present an extensive empirical evaluation on synthetic and real data, showing
that our algorithm outperforms significantly the current independence-based
algorithms, in terms of data efficiency and quality of learned structures, with
equivalent computational complexities. We also show the performance of IBMAP-HC
in a real-world application of knowledge discovery: EDAs, which are
evolutionary algorithms that use structure learning on each generation for
modeling the distribution of populations. The experiments show that when
IBMAP-HC is used to learn the structure, EDAs improve the convergence to the
optimum.
Improving Collaborative Filtering based Recommenders using Topic Modelling
Authors: Jobin Wilson, Santanu Chaudhury, Brejesh lall, Prateek Kapadia
Date: 2014-02-25 00:00:00
Tasks: Collaborative Filtering

Standard Collaborative Filtering (CF) algorithms make use of interactions
between users and items in the form of implicit or explicit ratings alone for
generating recommendations. Similarity among users or items is calculated
purely based on rating overlap in this case,without considering explicit
properties of users or items involved, limiting their applicability in domains
with very sparse rating spaces. In many domains such as movies, news or
electronic commerce recommenders, considerable contextual data in text form
describing item properties is available along with the rating data, which could
be utilized to improve recommendation quality.In this paper, we propose a novel
approach to improve standard CF based recommenders by utilizing latent
Dirichlet allocation (LDA) to learn latent properties of items, expressed in
terms of topic proportions, derived from their textual description. We infer
user's topic preferences or persona in the same latent space,based on her
historical ratings. While computing similarity between users, we make use of a
combined similarity measure involving rating overlap as well as similarity in
the latent topic space. This approach alleviates sparsity problem as it allows
calculation of similarity between users even if they have not rated any items
in common. Our experiments on multiple public datasets indicate that the
proposed hybrid approach significantly outperforms standard user Based and item
Based CF recommenders in terms of classification accuracy metrics such as
precision, recall and f-measure.
Regularization of $\ell_1$ minimization for dealing with outliers and noise in Statistics and Signal Recovery
Authors: Salvador Flores, Luis M. Briceno-Arias
Date: 2013-10-28 00:00:00
Tasks: regression

We study the robustness properties of $\ell_1$ norm minimization for the
classical linear regression problem with a given design matrix and
contamination restricted to the dependent variable. We perform a fine error
analysis of the $\ell_1$ estimator for measurements errors consisting of
outliers coupled with noise. We introduce a new estimation technique resulting
from a regularization of $\ell_1$ minimization by inf-convolution with the
$\ell_2$ norm. Concerning robustness to large outliers, the proposed estimator
keeps the breakdown point of the $\ell_1$ estimator, and reduces to least
squares when there are not outliers. We present a globally convergent
forward-backward algorithm for computing our estimator and some numerical
experiments confirming its theoretical properties.
Asymmetric Pruning for Learning Cascade Detectors
Authors: Sakrapee Paisitkriangkrai, Chunhua Shen, Anton Van Den Hengel
Date: 2013-03-25 00:00:00
Tasks: object-detection, Object Detection, Real-Time Object Detection

Cascade classifiers are one of the most important contributions to real-time
object detection. Nonetheless, there are many challenging problems arising in
training cascade detectors. One common issue is that the node classifier is
trained with a symmetric classifier. Having a low misclassification error rate
does not guarantee an optimal node learning goal in cascade classifiers, i.e.,
an extremely high detection rate with a moderate false positive rate. In this
work, we present a new approach to train an effective node classifier in a
cascade detector. The algorithm is based on two key observations: 1) Redundant
weak classifiers can be safely discarded; 2) The final detector should satisfy
the asymmetric learning objective of the cascade architecture. To achieve this,
we separate the classifier training into two steps: finding a pool of
discriminative weak classifiers/features and training the final classifier by
pruning weak classifiers which contribute little to the asymmetric learning
criterion (asymmetric classifier construction). Our model reduction approach
helps accelerate the learning time while achieving the pre-determined learning
objective. Experimental results on both face and car data sets verify the
effectiveness of the proposed algorithm. On the FDDB face data sets, our
approach achieves the state-of-the-art performance, which demonstrates the
advantage of our approach.
Topology preserving thinning for cell complexes
Authors: Paweł Dłotko, Ruben Specogna
Date: 2013-09-06 00:00:00

A topology preserving skeleton is a synthetic representation of an object
that retains its topology and many of its significant morphological properties.
The process of obtaining the skeleton, referred to as skeletonization or
thinning, is a very active research area. It plays a central role in reducing
the amount of information to be processed during image analysis and
visualization, computer-aided diagnosis or by pattern recognition algorithms.
  This paper introduces a novel topology preserving thinning algorithm which
removes \textit{simple cells}---a generalization of simple points---of a given
cell complex. The test for simple cells is based on \textit{acyclicity tables}
automatically produced in advance with homology computations. Using acyclicity
tables render the implementation of thinning algorithms straightforward.
Moreover, the fact that tables are automatically filled for all possible
configurations allows to rigorously prove the generality of the algorithm and
to obtain fool-proof implementations. The novel approach enables, for the first
time, according to our knowledge, to thin a general unstructured simplicial
complex. Acyclicity tables for cubical and simplicial complexes and an open
source implementation of the thinning algorithm are provided as additional
material to allow their immediate use in the vast number of practical
applications arising in medical imaging and beyond.