pwc-archive/papers-with-abstracts huggingface dataset:
Revisiting Bayesian Blind Deconvolution
Authors: David Wipf, Haichao Zhang
Date: 2013-05-10 00:00:00

Blind deconvolution involves the estimation of a sharp signal or image given
only a blurry observation. Because this problem is fundamentally ill-posed,
strong priors on both the sharp image and blur kernel are required to
regularize the solution space. While this naturally leads to a standard MAP
estimation framework, performance is compromised by unknown trade-off parameter
settings, optimization heuristics, and convergence issues stemming from
non-convexity and/or poor prior selections. To mitigate some of these problems,
a number of authors have recently proposed substituting a variational Bayesian
(VB) strategy that marginalizes over the high-dimensional image space leading
to better estimates of the blur kernel. However, the underlying cost function
now involves both integrals with no closed-form solution and complex,
function-valued arguments, thus losing the transparency of MAP. Beyond standard
Bayesian-inspired intuitions, it thus remains unclear by exactly what mechanism
these methods are able to operate, rendering understanding, improvements and
extensions more difficult. To elucidate these issues, we demonstrate that the
VB methodology can be recast as an unconventional MAP problem with a very
particular penalty/prior that couples the image, blur kernel, and noise level
in a principled way. This unique penalty has a number of useful characteristics
pertaining to relative concavity, local minima avoidance, and scale-invariance
that allow us to rigorously explain the success of VB including its existing
implementational heuristics and approximations. It also provides strict
criteria for choosing the optimal image prior that, perhaps
counter-intuitively, need not reflect the statistics of natural scenes. In so
doing we challenge the prevailing notion of why VB is successful for blind
deconvolution while providing a transparent platform for introducing
enhancements.
Exponentiated Gradient LINUCB for Contextual Multi-Armed Bandits
Authors: Djallel Bouneffouf
Date: 2013-05-10 00:00:00
Tasks: Multi-Armed Bandits

We present Exponentiated Gradient LINUCB, an algorithm for con-textual
multi-armed bandits. This algorithm uses Exponentiated Gradient to find the
optimal exploration of the LINUCB. Within a deliberately designed offline
simulation framework we conduct evaluations with real online event log data.
The experimental results demonstrate that our algorithm outperforms surveyed
algorithms.
Novel Analysis of Population Scalability in Evolutionary Algorithms
Authors: Jun He, Tianshi Chen, Boris Mitavskiy
Date: 2011-08-23 00:00:00
Tasks: Evolutionary Algorithms

Population-based evolutionary algorithms (EAs) have been widely applied to
solve various optimization problems. The question of how the performance of a
population-based EA depends on the population size arises naturally. The
performance of an EA may be evaluated by different measures, such as the
average convergence rate to the optimal set per generation or the expected
number of generations to encounter an optimal solution for the first time.
Population scalability is the performance ratio between a benchmark EA and
another EA using identical genetic operators but a larger population size.
Although intuitively the performance of an EA may improve if its population
size increases, currently there exist only a few case studies for simple
fitness functions. This paper aims at providing a general study for discrete
optimisation. A novel approach is introduced to analyse population scalability
using the fundamental matrix. The following two contributions summarize the
major results of the current article. (1) We demonstrate rigorously that for
elitist EAs with identical global mutation, using a lager population size
always increases the average rate of convergence to the optimal set; and yet,
sometimes, the expected number of generations needed to find an optimal
solution (measured by either the maximal value or the average value) may
increase, rather than decrease. (2) We establish sufficient and/or necessary
conditions for the superlinear scalability, that is, when the average
convergence rate of a $(\mu+\mu)$ EA (where $\mu\ge2$) is bigger than $\mu$
times that of a $(1+1)$ EA.
Fast Collision Checking: From Single Robots to Multi-Robot Teams
Authors: Joshua Bialkowski, Michael Otte, Emilio Frazzoli
Date: 2013-05-10 00:00:00

We examine three different algorithms that enable the collision certificate
method from [Bialkowski, et al.] to handle the case of a centralized
multi-robot team. By taking advantage of symmetries in the configuration space
of multi-robot teams, our methods can significantly reduce the number of
collision checks vs. both [Bialkowski, et al.] and standard collision checking
implementations.
Human Mood Detection For Human Computer Interaction
Authors: Preeti Badar, Urmila Shrawankar
Date: 2013-05-10 00:00:00
Tasks: Face Detection, Facial Expression Recognition, Facial Expression Recognition (FER), General Classification

In this paper we propose an easiest approach for facial expression
recognition. Here we are using concept of SVM for Expression Classification.
Main problem is sub divided in three main modules. First one is Face detection
in which we are using skin filter and Face segmentation. We are given more
stress on feature Extraction. This method is effective enough for application
where fast execution is required. Second, Facial Feature Extraction which is
essential part for expression recognition. In this module we used Edge
Projection Analysis. Finally extracted features vector is passed towards SVM
classifier for Expression Recognition. We are considering six basic Expressions
(Anger, Fear, Disgust, Joy, Sadness, and Surprise)
Image Optimization and Prediction
Authors: Shweta Jain, Urmila Shrawankar
Date: 2013-05-10 00:00:00
Tasks: Object Recognition, Prediction

Image Processing, Optimization and Prediction of an Image play a key role in
Computer Science. Image processing provides a way to analyze and identify an
image .Many areas like medical image processing, Satellite images, natural
images and artificial images requires lots of analysis and research on
optimization. In Image Optimization and Prediction we are combining the
features of Query Optimization, Image Processing and Prediction . Image
optimization is used in Pattern analysis, object recognition, in medical Image
processing to predict the type of diseases, in satellite images for predicting
weather forecast, availability of water or mineral etc. Image Processing,
Optimization and analysis is a wide open area for research .Lots of research
has been conducted in the area of Image analysis and many techniques are
available for image analysis but, a single technique is not yet identified for
image analysis and prediction .our research is focused on identifying a global
technique for image analysis and Prediction.
Performance Enhancement of Distributed Quasi Steady-State Genetic Algorithm
Authors: Rahila Patel, Urmila Shrawankar, MM. Raghuwanshi, Anil N. Jaiswal
Date: 2013-05-10 00:00:00
Tasks: Clustering, Position

This paper proposes a new scheme for performance enhancement of distributed
genetic algorithm (DGA). Initial population is divided in two classes i.e.
female and male. Simple distance based clustering is used for cluster formation
around females. For reclustering self-adaptive K-means is used, which produces
well distributed and well separated clusters. The self-adaptive K-means used
for reclustering automatically locates initial position of centroids and number
of clusters. Four plans of co-evolution are applied on these clusters
independently. Clusters evolve separately. Merging of clusters takes place
depending on their performance. For experimentation unimodal and multimodal
test functions have been used. Test result show that the new scheme of
distribution of population has given better performance.
Beyond Physical Connections: Tree Models in Human Pose Estimation
Authors: Fang Wang, Yi Li
Date: 2013-05-10 00:00:00
Proceeding: CVPR 2013 6
Tasks: Pose Estimation

Simple tree models for articulated objects prevails in the last decade.
However, it is also believed that these simple tree models are not capable of
capturing large variations in many scenarios, such as human pose estimation.
This paper attempts to address three questions: 1) are simple tree models
sufficient? more specifically, 2) how to use tree models effectively in human
pose estimation? and 3) how shall we use combined parts together with single
parts efficiently?
  Assuming we have a set of single parts and combined parts, and the goal is to
estimate a joint distribution of their locations. We surprisingly find that no
latent variables are introduced in the Leeds Sport Dataset (LSP) during
learning latent trees for deformable model, which aims at approximating the
joint distributions of body part locations using minimal tree structure. This
suggests one can straightforwardly use a mixed representation of single and
combined parts to approximate their joint distribution in a simple tree model.
As such, one only needs to build Visual Categories of the combined parts, and
then perform inference on the learned latent tree. Our method outperformed the
state of the art on the LSP, both in the scenarios when the training images are
from the same dataset and from the PARSE dataset. Experiments on animal images
from the VOC challenge further support our findings.
Quality Measures of Parameter Tuning for Aggregated Multi-Objective Temporal Planning
Authors: Mostepha Redouane Khouadjia, Marc Schoenauer, Vincent Vidal, Johann Dréo, Pierre Savéant
Date: 2013-05-10 00:00:00

Parameter tuning is recognized today as a crucial ingredient when tackling an
optimization problem. Several meta-optimization methods have been proposed to
find the best parameter set for a given optimization algorithm and (set of)
problem instances. When the objective of the optimization is some scalar
quality of the solution given by the target algorithm, this quality is also
used as the basis for the quality of parameter sets. But in the case of
multi-objective optimization by aggregation, the set of solutions is given by
several single-objective runs with different weights on the objectives, and it
turns out that the hypervolume of the final population of each single-objective
run might be a better indicator of the global performance of the aggregation
method than the best fitness in its population. This paper discusses this issue
on a case study in multi-objective temporal planning using the evolutionary
planner DaE-YAHSP and the meta-optimizer ParamILS. The results clearly show how
ParamILS makes a difference between both approaches, and demonstrate that
indeed, in this context, using the hypervolume indicator as ParamILS target is
the best choice. Other issues pertaining to parameter tuning in the proposed
context are also discussed.
Multi-q Pattern Classification of Polarization Curves
Authors: Ricardo Fabbri, Ivan N. Bastos, Francisco D. Moura Neto, Francisco J. P. Lopes, Wesley N. Goncalves, Odemir M. Bruno
Date: 2013-05-10 00:00:00
Tasks: Classification, General Classification

Several experimental measurements are expressed in the form of
one-dimensional profiles, for which there is a scarcity of methodologies able
to classify the pertinence of a given result to a specific group. The
polarization curves that evaluate the corrosion kinetics of electrodes in
corrosive media are an application where the behavior is chiefly analyzed from
profiles. Polarization curves are indeed a classic method to determine the
global kinetics of metallic electrodes, but the strong nonlinearity from
different metals and alloys can overlap and the discrimination becomes a
challenging problem. Moreover, even finding a typical curve from replicated
tests requires subjective judgement. In this paper we used the so-called
multi-q approach based on the Tsallis statistics in a classification engine to
separate multiple polarization curve profiles of two stainless steels. We
collected 48 experimental polarization curves in aqueous chloride medium of two
stainless steel types, with different resistance against localized corrosion.
Multi-q pattern analysis was then carried out on a wide potential range, from
cathodic up to anodic regions. An excellent classification rate was obtained,
at a success rate of 90%, 80%, and 83% for low (cathodic), high (anodic), and
both potential ranges, respectively, using only 2% of the original profile
data. These results show the potential of the proposed approach towards
efficient, robust, systematic and automatic classification of highly non-linear
profile curves.
Programming with Personalized PageRank: A Locally Groundable First-Order Probabilistic Logic
Authors: William Yang Wang, Kathryn Mazaitis, William W. Cohen
Date: 2013-05-10 00:00:00
Tasks: Entity Resolution

In many probabilistic first-order representation systems, inference is
performed by "grounding"---i.e., mapping it to a propositional representation,
and then performing propositional inference. With a large database of facts,
groundings can be very large, making inference and learning computationally
expensive. Here we present a first-order probabilistic language which is
well-suited to approximate "local" grounding: every query $Q$ can be
approximately grounded with a small graph. The language is an extension of
stochastic logic programs where inference is performed by a variant of
personalized PageRank. Experimentally, we show that the approach performs well
without weight learning on an entity resolution task; that supervised
weight-learning improves accuracy; and that grounding time is independent of DB
size. We also show that order-of-magnitude speedups are possible by
parallelizing learning.
Joint Topic Modeling and Factor Analysis of Textual Information and Graded Response Data
Authors: Andrew S. Lan, Christoph Studer, Andrew E. Waters, Richard G. Baraniuk
Date: 2013-05-08 00:00:00
Tasks: BIG-bench Machine Learning

Modern machine learning methods are critical to the development of
large-scale personalized learning systems that cater directly to the needs of
individual learners. The recently developed SPARse Factor Analysis (SPARFA)
framework provides a new statistical model and algorithms for machine
learning-based learning analytics, which estimate a learner's knowledge of the
latent concepts underlying a domain, and content analytics, which estimate the
relationships among a collection of questions and the latent concepts. SPARFA
estimates these quantities given only the binary-valued graded responses to a
collection of questions. In order to better interpret the estimated latent
concepts, SPARFA relies on a post-processing step that utilizes user-defined
tags (e.g., topics or keywords) available for each question. In this paper, we
relax the need for user-defined tags by extending SPARFA to jointly process
both graded learner responses and the text of each question and its associated
answer(s) or other feedback. Our purely data-driven approach (i) enhances the
interpretability of the estimated latent concepts without the need of
explicitly generating a set of tags or performing a post-processing step, (ii)
improves the prediction performance of SPARFA, and (iii) scales to large
test/assessments where human annotation would prove burdensome. We demonstrate
the efficacy of the proposed approach on two real educational datasets.
Repairing and Inpainting Damaged Images using Diffusion Tensor
Authors: Faouzi Benzarti, Hamid Amiri
Date: 2013-05-09 00:00:00
Tasks: Image Inpainting

Removing or repairing the imperfections of a digital images or videos is a
very active and attractive field of research belonging to the image inpainting
technique. This later has a wide range of applications, such as removing
scratches in old photographic image, removing text and logos or creating
cartoon and artistic effects. In this paper, we propose an efficient method to
repair a damaged image based on a non linear diffusion tensor. The idea is to
track perfectly the local geometry of the damaged image and allowing diffusion
only in the isophotes curves direction. To illustrate the effective performance
of our method, we present some experimental results on test and real
photographic color images
Stochastic gradient descent algorithms for strongly convex functions at O(1/T) convergence rates
Authors: Shenghuo Zhu
Date: 2013-05-09 00:00:00

With a weighting scheme proportional to t, a traditional stochastic gradient
descent (SGD) algorithm achieves a high probability convergence rate of
O({\kappa}/T) for strongly convex functions, instead of O({\kappa} ln(T)/T). We
also prove that an accelerated SGD algorithm also achieves a rate of
O({\kappa}/T).
Generalized Bregman Divergence and Gradient of Mutual Information for Vector Poisson Channels
Authors: Liming Wang, Miguel Rodrigues, Lawrence Carin
Date: 2013-01-28 00:00:00
Tasks: Compressive Sensing, Document Classification

We investigate connections between information-theoretic and
estimation-theoretic quantities in vector Poisson channel models. In
particular, we generalize the gradient of mutual information with respect to
key system parameters from the scalar to the vector Poisson channel model. We
also propose, as another contribution, a generalization of the classical
Bregman divergence that offers a means to encapsulate under a unifying
framework the gradient of mutual information results for scalar and vector
Poisson and Gaussian channel models. The so-called generalized Bregman
divergence is also shown to exhibit various properties akin to the properties
of the classical version. The vector Poisson channel model is drawing
considerable attention in view of its application in various domains: as an
example, the availability of the gradient of mutual information can be used in
conjunction with gradient descent methods to effect compressive-sensing
projection designs in emerging X-ray and document classification applications.
Unveiling the link between logical fallacies and web persuasion
Authors: Antonio Lieto, Fabiana Vernero
Date: 2013-04-14 00:00:00
Tasks: Logical Fallacies, Persuasion Strategies

In the last decade Human-Computer Interaction (HCI) has started to focus
attention on forms of persuasive interaction where computer technologies have
the goal of changing users behavior and attitudes according to a predefined
direction. In this work, we hypothesize a strong connection between logical
fallacies (forms of reasoning which are logically invalid but cognitively
effective) and some common persuasion strategies adopted within web
technologies. With the aim of empirically evaluating our hypothesis, we carried
out a pilot study on a sample of 150 e-commerce websites.
A Rank Minrelation - Majrelation Coefficient
Authors: Patrick E. Meyer
Date: 2013-05-09 00:00:00
Tasks: Variable Selection

Improving the detection of relevant variables using a new bivariate measure
could importantly impact variable selection and large network inference
methods. In this paper, we propose a new statistical coefficient that we call
the rank minrelation coefficient. We define a minrelation of X to Y (or
equivalently a majrelation of Y to X) as a measure that estimate p(Y > X) when
X and Y are continuous random variables. The approach is similar to Lin's
concordance coefficient that rather focuses on estimating p(X = Y). In other
words, if a variable X exhibits a minrelation to Y then, as X increases, Y is
likely to increases too. However, on the contrary to concordance or
correlation, the minrelation is not symmetric. More explicitly, if X decreases,
little can be said on Y values (except that the uncertainty on Y actually
increases). In this paper, we formally define this new kind of bivariate
dependencies and propose a new statistical coefficient in order to detect those
dependencies. We show through several key examples that this new coefficient
has many interesting properties in order to select relevant variables, in
particular when compared to correlation.
Automatic Speech Recognition Using Template Model for Man-Machine Interface
Authors: Neema Mishra, Urmila Shrawankar, V. M. Thakare
Date: 2013-05-09 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Dynamic Time Warping, speech-recognition, Speech Recognition

Speech is a natural form of communication for human beings, and computers
with the ability to understand speech and speak with a human voice are expected
to contribute to the development of more natural man-machine interfaces.
Computers with this kind of ability are gradually becoming a reality, through
the evolution of speech recognition technologies. Speech is being an important
mode of interaction with computers. In this paper Feature extraction is
implemented using well-known Mel-Frequency Cepstral Coefficients (MFCC).Pattern
matching is done using Dynamic time warping (DTW) algorithm.
An Overview of Hindi Speech Recognition
Authors: Neema Mishra, Urmila Shrawankar, V. M. Thakare
Date: 2013-05-09 00:00:00
Tasks: speech-recognition, Speech Recognition

In this age of information technology, information access in a convenient
manner has gained importance. Since speech is a primary mode of communication
among human beings, it is natural for people to expect to be able to carry out
spoken dialogue with computer. Speech recognition system permits ordinary
people to speak to the computer to retrieve information. It is desirable to
have a human computer dialogue in local language. Hindi being the most widely
spoken Language in India is the natural primary human language candidate for
human machine interaction. There are five pairs of vowels in Hindi languages;
one member is longer than the other one. This paper describes an overview of
speech recognition system that includes how speech is produced and the
properties and characteristics of Hindi Phoneme.
Opportunities & Challenges In Automatic Speech Recognition
Authors: Rashmi Makhijani, Urmila Shrawankar, V. M. Thakare
Date: 2013-05-09 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Automatic speech recognition enables a wide range of current and emerging
applications such as automatic transcription, multimedia content analysis, and
natural human-computer interfaces. This paper provides a glimpse of the
opportunities and challenges that parallelism provides for automatic speech
recognition and related application research from the point of view of speech
researchers. The increasing parallelism in computing platforms opens three
major possibilities for speech recognition systems: improving recognition
accuracy in non-ideal, everyday noisy environments; increasing recognition
throughput in batch processing of speech data; and reducing recognition latency
in realtime usage scenarios. This paper describes technical challenges,
approaches taken, and possible directions for future research to guide the
design of efficient parallel software and hardware infrastructures.