pwc-archive/papers-with-abstracts huggingface dataset:
A Reliable Effective Terascale Linear Learning System
Authors: Alekh Agarwal, Olivier Chapelle, Miroslav Dudik, John Langford
Date: 2011-10-19 00:00:00

We present a system and a set of techniques for learning linear predictors
with convex losses on terascale datasets, with trillions of features, {The
number of features here refers to the number of non-zero entries in the data
matrix.} billions of training examples and millions of parameters in an hour
using a cluster of 1000 machines. Individually none of the component techniques
are new, but the careful synthesis required to obtain an efficient
implementation is. The result is, up to our knowledge, the most scalable and
efficient linear learning system reported in the literature (as of 2011 when
our experiments were conducted). We describe and thoroughly evaluate the
components of the system, showing the importance of the various design choices.
Improving the quality of Gujarati-Hindi Machine Translation through part-of-speech tagging and stemmer-assisted transliteration
Authors: Juhi Ameta, Nisheeth Joshi, Iti Mathur
Date: 2013-07-12 00:00:00
Tasks: Machine Translation, Part-Of-Speech Tagging, Translation, Transliteration

Machine Translation for Indian languages is an emerging research area.
Transliteration is one such module that we design while designing a translation
system. Transliteration means mapping of source language text into the target
language. Simple mapping decreases the efficiency of overall translation
system. We propose the use of stemming and part-of-speech tagging for
transliteration. The effectiveness of translation can be improved if we use
part-of-speech tagging and stemming assisted transliteration.We have shown that
much of the content in Gujarati gets transliterated while being processed for
translation to Hindi language.
Fuzzy Fibers: Uncertainty in dMRI Tractography
Authors: Thomas Schultz, Anna Vilanova, Ralph Brecheisen, Gordon Kindlmann
Date: 2013-07-11 00:00:00
Tasks: Model Selection

Fiber tracking based on diffusion weighted Magnetic Resonance Imaging (dMRI)
allows for noninvasive reconstruction of fiber bundles in the human brain. In
this chapter, we discuss sources of error and uncertainty in this technique,
and review strategies that afford a more reliable interpretation of the
results. This includes methods for computing and rendering probabilistic
tractograms, which estimate precision in the face of measurement noise and
artifacts. However, we also address aspects that have received less attention
so far, such as model selection, partial voluming, and the impact of
parameters, both in preprocessing and in fiber tracking itself. We conclude by
giving impulses for future research.
Minimum Distance Estimation for Robust High-Dimensional Regression
Authors: Aurélie C. Lozano, Nicolai Meinshausen
Date: 2013-07-11 00:00:00
Tasks: regression, Vocal Bursts Intensity Prediction

We propose a minimum distance estimation method for robust regression in
sparse high-dimensional settings. The traditional likelihood-based estimators
lack resilience against outliers, a critical issue when dealing with
high-dimensional noisy data. Our method, Minimum Distance Lasso (MD-Lasso),
combines minimum distance functionals, customarily used in nonparametric
estimation for their robustness, with l1-regularization for high-dimensional
regression. The geometry of MD-Lasso is key to its consistency and robustness.
The estimator is governed by a scaling parameter that caps the influence of
outliers: the loss per observation is locally convex and close to quadratic for
small squared residuals, and flattens for squared residuals larger than the
scaling parameter. As the parameter approaches infinity, the estimator becomes
equivalent to least-squares Lasso. MD-Lasso enjoys fast convergence rates under
mild conditions on the model error distribution, which hold for any of the
solutions in a convexity region around the true parameter and in certain cases
for every solution. Remarkably, a first-order optimization method is able to
produce iterates very close to the consistent solutions, with geometric
convergence and regardless of the initialization. A connection is established
with re-weighted least-squares that intuitively explains MD-Lasso robustness.
The merits of our method are demonstrated through simulation and eQTL data
analysis.
Action-based Character AI in Video-games with CogBots Architecture: A Preliminary Report
Authors: Davide Aversa, Stavros Vassos
Date: 2013-07-11 00:00:00

In this paper we propose an architecture for specifying the interaction of
non-player characters (NPCs) in the game-world in a way that abstracts common
tasks in four main conceptual components, namely perception, deliberation,
control, action. We argue that this architecture, inspired by AI research on
autonomous agents and robots, can offer a number of benefits in the form of
abstraction, modularity, re-usability and higher degrees of personalization for
the behavior of each NPC. We also show how this architecture can be used to
tackle a simple scenario related to the navigation of NPCs under incomplete
information about the obstacles that may obstruct the various way-points in the
game, in a simple and effective way.
Artificial Intelligence MArkup Language: A Brief Tutorial
Authors: Maria das Graças Bruno Marietto, Rafael Varago de Aguiar, Gislene de Oliveira Barbosa, Wagner Tanaka Botelho, Edson Pimentel, Robson dos Santos França, Vera Lúcia da Silva
Date: 2013-07-11 00:00:00
Tasks: TAG

The purpose of this paper is to serve as a reference guide for the
development of chatterbots implemented with the AIML language. In order to
achieve this, the main concepts in Pattern Recognition area are described
because the AIML uses such theoretical framework in their syntactic and
semantic structures. After that, AIML language is described and each AIML
command/tag is followed by an application example. Also, the usage of AIML
embedded tags for the handling of sequence dialogue limitations between humans
and machines is shown. Finally, computer systems that assist in the design of
chatterbots with the AIML language are classified and described.
Contrast Enhancement And Brightness Preservation Using Multi- Decomposition Histogram Equalization
Authors: Sayali Nimkar, Sanal Varghese, Sucheta Shrivastava
Date: 2013-07-11 00:00:00
Tasks: Image Enhancement

Histogram Equalization (HE) has been an essential addition to the Image
Enhancement world. Enhancement techniques like Classical Histogram Equalization
(CHE), Adaptive Histogram Equalization (ADHE), Bi-Histogram Equalization (BHE)
and Recursive Mean Separate Histogram Equalization (RMSHE) methods enhance
contrast, however, brightness is not well preserved with these methods, which
gives an unpleasant look to the final image obtained. Thus, we introduce a
novel technique Multi-Decomposition Histogram Equalization (MDHE) to eliminate
the drawbacks of the earlier methods. In MDHE, we have decomposed the input
sixty-four parts, applied CHE in each of the sub-images and then finally
interpolated them in correct order. The final image after MDHE results in
contrast enhanced and brightness preserved image compared to all other
techniques mentioned above. We have calculated the various parameters like
PSNR, SNR, RMSE, MSE, etc. for every technique. Our results are well supported
by bar graphs, histograms and the parameter calculations at the end.
Application of three graph Laplacian based semi-supervised learning methods to protein function prediction problem
Authors: Loc Tran
Date: 2012-11-19 00:00:00
Tasks: Protein Function Prediction

Protein function prediction is the important problem in modern biology. In
this paper, the un-normalized, symmetric normalized, and random walk graph
Laplacian based semi-supervised learning methods will be applied to the
integrated network combined from multiple networks to predict the functions of
all yeast proteins in these multiple networks. These multiple networks are
network created from Pfam domain structure, co-participation in a protein
complex, protein-protein interaction network, genetic interaction network, and
network created from cell cycle gene expression measurements. Multiple networks
are combined with fixed weights instead of using convex optimization to
determine the combination weights due to high time complexity of convex
optimization method. This simple combination method will not affect the
accuracy performance measures of the three semi-supervised learning methods.
Experiment results show that the un-normalized and symmetric normalized graph
Laplacian based methods perform slightly better than random walk graph
Laplacian based method for integrated network. Moreover, the accuracy
performance measures of these three semi-supervised learning methods for
integrated network are much better than the best accuracy performance measures
of these three methods for the individual network.
Genetic approach for arabic part of speech tagging
Authors: Bilel Ben Ali, Fethi Jarray
Date: 2013-07-11 00:00:00
Tasks: Part-Of-Speech Tagging, Sentence

With the growing number of textual resources available, the ability to
understand them becomes critical. An essential first step in understanding
these sources is the ability to identify the part of speech in each sentence.
Arabic is a morphologically rich language, wich presents a challenge for part
of speech tagging. In this paper, our goal is to propose, improve and implement
a part of speech tagger based on a genetic alorithm. The accuracy obtained with
this method is comparable to that of other probabilistic approaches.
A New Approach to the Solution of Economic Dispatch Using Particle Swarm Optimization with Simulated Annealing
Authors: V. Karthikeyan, S. Senthilkumar, V. J. Vijayalakshmi
Date: 2013-07-11 00:00:00

A new approach to the solution of Economic Dispatch using Particle Swarm
Optimization is presented. It is the progression of allocating production
amongst the dedicated units such that the restriction forced are fulfilled and
the power needs are reduced. More just, the soft computing method has received
supplementary concentration and was used in a quantity of successful and
sensible applications. Here, an attempt has been made to find out the minimum
cost by using Particle Swarm Optimization Algorithm using the data of three
generating units. In this work, data has been taken such as the loss
coefficients with the max-min power limit and cost function. PSO and Simulated
Annealing are functional to put out the least amount for dissimilar energy
requirements. When the outputs are compared with the conventional method, PSO
seems to give an improved result with enhanced convergence feature. All the
methods are executed in MATLAB environment. The effectiveness and feasibility
of the proposed method were demonstrated by three generating units case study.
Output gives hopeful results, signifying that the projected method of
calculation is competent of economically formative advanced eminence solutions
addressing economic dispatch problems.
Soft Computing Framework for Routing in Wireless Mesh Networks: An Integrated Cost Function Approach
Authors: Shakti Kumar, Brahmjit Singh, Sharad Sharma
Date: 2013-07-11 00:00:00

Dynamic behaviour of a WMN imposes stringent constraints on the routing
policy of the network. In the shortest path based routing the shortest paths
needs to be evaluated within a given time frame allowed by the WMN dynamics.
The exact reasoning based shortest path evaluation methods usually fail to meet
this rigid requirement. Thus, requiring some soft computing based approaches
which can replace "best for sure" solutions with "good enough" solutions. This
paper proposes a framework for optimal routing in the WMNs; where we
investigate the suitability of Big Bang-Big Crunch (BB-BC), a soft computing
based approach to evaluate shortest/near-shortest path. In order to make
routing optimal we first propose to replace distance between the adjacent nodes
with an integrated cost measure that takes into account throughput, delay,
jitter and residual energy of a node. A fuzzy logic based inference mechanism
evaluates this cost measure at each node. Using this distance measure we apply
BB-BC optimization algorithm to evaluate shortest/near shortest path to update
the routing tables periodically as dictated by network requirements. A large
number of simulations were conducted and it has been observed that BB-BC
algorithm appears to be a high potential candidate suitable for routing in
WMNs.
Routing in Wireless Mesh Networks: Two Soft Computing Based Approaches
Authors: Sharad Sharma, Shakti Kumar, Brahmjit Singh
Date: 2013-07-11 00:00:00
Tasks: Vocal Bursts Valence Prediction

Due to dynamic network conditions, routing is the most critical part in WMNs
and needs to be optimised. The routing strategies developed for WMNs must be
efficient to make it an operationally self configurable network. Thus we need
to resort to near shortest path evaluation. This lays down the requirement of
some soft computing approaches such that a near shortest path is available in
an affordable computing time. This paper proposes a Fuzzy Logic based
integrated cost measure in terms of delay, throughput and jitter. Based upon
this distance (cost) between two adjacent nodes we evaluate minimal shortest
path that updates routing tables. We apply two recent soft computing approaches
namely Big Bang Big Crunch (BB-BC) and Biogeography Based Optimization (BBO)
approaches to enumerate shortest or near short paths. BB-BC theory is related
with the evolution of the universe whereas BBO is inspired by dynamical
equilibrium in the number of species on an island. Both the algorithms have low
computational time and high convergence speed. Simulation results show that the
proposed routing algorithms find the optimal shortest path taking into account
three most important parameters of network dynamics. It has been further
observed that for the shortest path problem BB-BC outperforms BBO in terms of
speed and percent error between the evaluated minimal path and the actual
shortest path.
Conversion of Braille to Text in English, Hindi and Tamil Languages
Authors: S. Padmavathi, Manojna K. S. S, S. Sphoorthy Reddy, D. Meenakshy
Date: 2013-07-11 00:00:00

The Braille system has been used by the visually impaired for reading and
writing. Due to limited availability of the Braille text books an efficient
usage of the books becomes a necessity. This paper proposes a method to convert
a scanned Braille document to text which can be read out to many through the
computer. The Braille documents are pre processed to enhance the dots and
reduce the noise. The Braille cells are segmented and the dots from each cell
is extracted and converted in to a number sequence. These are mapped to the
appropriate alphabets of the language. The converted text is spoken out through
a speech synthesizer. The paper also provides a mechanism to type the Braille
characters through the number pad of the keyboard. The typed Braille character
is mapped to the alphabet and spoken out. The Braille cell has a standard
representation but the mapping differs for each language. In this paper mapping
of English, Hindi and Tamil are considered.
Accuracy of MAP segmentation with hidden Potts and Markov mesh prior models via Path Constrained Viterbi Training, Iterated Conditional Modes and Graph Cut based algorithms
Authors: Ana Georgina Flesia, Josef Baumgartner, Javier Gimenez, Jorge Martinez
Date: 2013-07-11 00:00:00
Tasks: Diagnostic, General Classification, Image Segmentation, parameter estimation, Segmentation, Semantic Segmentation

In this paper, we study statistical classification accuracy of two different
Markov field environments for pixelwise image segmentation, considering the
labels of the image as hidden states and solving the estimation of such labels
as a solution of the MAP equation. The emission distribution is assumed the
same in all models, and the difference lays in the Markovian prior hypothesis
made over the labeling random field. The a priori labeling knowledge will be
modeled with a) a second order anisotropic Markov Mesh and b) a classical
isotropic Potts model. Under such models, we will consider three different
segmentation procedures, 2D Path Constrained Viterbi training for the Hidden
Markov Mesh, a Graph Cut based segmentation for the first order isotropic Potts
model, and ICM (Iterated Conditional Modes) for the second order isotropic
Potts model.
  We provide a unified view of all three methods, and investigate goodness of
fit for classification, studying the influence of parameter estimation,
computational gain, and extent of automation in the statistical measures
Overall Accuracy, Relative Improvement and Kappa coefficient, allowing robust
and accurate statistical analysis on synthetic and real-life experimental data
coming from the field of Dental Diagnostic Radiography. All algorithms, using
the learned parameters, generate good segmentations with little interaction
when the images have a clear multimodal histogram. Suboptimal learning proves
to be frail in the case of non-distinctive modes, which limits the complexity
of usable models, and hence the achievable error rate as well.
  All Matlab code written is provided in a toolbox available for download from
our website, following the Reproducible Research Paradigm.
Online Alternating Direction Method (longer version)
Authors: Huahua Wang, Arindam Banerjee
Date: 2013-06-17 00:00:00

Online optimization has emerged as powerful tool in large scale optimization.
In this pa- per, we introduce efficient online optimization algorithms based on
the alternating direction method (ADM), which can solve online convex
optimization under linear constraints where the objective could be non-smooth.
We introduce new proof techniques for ADM in the batch setting, which yields a
O(1/T) convergence rate for ADM and forms the basis for regret anal- ysis in
the online setting. We consider two scenarios in the online setting, based on
whether an additional Bregman divergence is needed or not. In both settings, we
establish regret bounds for both the objective function as well as constraints
violation for general and strongly convex functions. We also consider inexact
ADM updates where certain terms are linearized to yield efficient updates and
show the stochastic convergence rates. In addition, we briefly discuss that
online ADM can be used as projection- free online learning algorithm in some
scenarios. Preliminary results are presented to illustrate the performance of
the proposed algorithms.
Tractable Combinations of Global Constraints
Authors: David A. Cohen, Peter G. Jeavons, Evgenij Thorstensen, Stanislav Živný
Date: 2013-07-10 00:00:00

We study the complexity of constraint satisfaction problems involving global
constraints, i.e., special-purpose constraints provided by a solver and
represented implicitly by a parametrised algorithm. Such constraints are widely
used; indeed, they are one of the key reasons for the success of constraint
programming in solving real-world problems.
  Previous work has focused on the development of efficient propagators for
individual constraints. In this paper, we identify a new tractable class of
constraint problems involving global constraints of unbounded arity. To do so,
we combine structural restrictions with the observation that some important
types of global constraint do not distinguish between large classes of
equivalent solutions.
A Polynomial Time Algorithm for Lossy Population Recovery
Authors: Ankur Moitra, Michael Saks
Date: 2013-02-06 00:00:00

We give a polynomial time algorithm for the lossy population recovery
problem. In this problem, the goal is to approximately learn an unknown
distribution on binary strings of length $n$ from lossy samples: for some
parameter $\mu$ each coordinate of the sample is preserved with probability
$\mu$ and otherwise is replaced by a `?'. The running time and number of
samples needed for our algorithm is polynomial in $n$ and $1/\varepsilon$ for
each fixed $\mu>0$. This improves on algorithm of Wigderson and Yehudayoff that
runs in quasi-polynomial time for any $\mu > 0$ and the polynomial time
algorithm of Dvir et al which was shown to work for $\mu \gtrapprox 0.30$ by
Batman et al. In fact, our algorithm also works in the more general framework
of Batman et al. in which there is no a priori bound on the size of the support
of the distribution. The algorithm we analyze is implicit in previous work; our
main contribution is to analyze the algorithm by showing (via linear
programming duality and connections to complex analysis) that a certain matrix
associated with the problem has a robust local inverse even though its
condition number is exponentially small. A corollary of our result is the first
polynomial time algorithm for learning DNFs in the restriction access model of
Dvir et al.
Anisotropic Diffusion for Details Enhancement in Multi-Exposure Image Fusion
Authors: Harbinder Singh, Vinay Kumar, Sunil Bhooshan
Date: 2013-07-10 00:00:00
Tasks: Multi-Exposure Image Fusion, Tone Mapping

We develop a multiexposure image fusion method based on texture features,
which exploits the edge preserving and intraregion smoothing property of
nonlinear diffusion filters based on partial differential equations (PDE). With
the captured multiexposure image series, we first decompose images into base
layers and detail layers to extract sharp details and fine details,
respectively. The magnitude of the gradient of the image intensity is utilized
to encourage smoothness at homogeneous regions in preference to inhomogeneous
regions. Then, we have considered texture features of the base layer to
generate a mask (i.e., decision mask) that guides the fusion of base layers in
multiresolution fashion. Finally, well-exposed fused image is obtained that
combines fused base layer and the detail layers at each scale across all the
input exposures. Proposed algorithm skipping complex High Dynamic Range Image
(HDRI) generation and tone mapping steps to produce detail preserving image for
display on standard dynamic range display devices. Moreover, our technique is
effective for blending flash/no-flash image pair and multifocus images, that
is, images focused on different targets.
Optimisation dans la détection de communautés recouvrantes et équilibre de Nash
Authors: Michel Crampes, Michel Plantié, Marie Lopez
Date: 2013-07-10 00:00:00
Tasks: Community Detection

Community detection in graphs has been the subject of many algorithms. Recent
methods want to optimize a modularity function which shows a maximum of
relationships within communities and found a minimum of inter-community
relations. these algorithms are applied to unipartite, multipartite and
directed graphs. However, given the NP-completeness of the problem, these
algorithms are heuristics that do not guarantee an optimum. In this paper we
introduce an algorithm which, based on an approximate solution obtained through
a efficient detection algorithm, modifie it to achieve a local optimum based on
a function. this reassignment function is a potential function and therefore
the computed optimum is a Nash equilibrium. We supplement our method with an
overlap function that allows to have simultaneously the two detection modes.
Several experiments show the interest of our approach.