pwc-archive/papers-with-abstracts huggingface dataset:
A Health Monitoring System for Elder and Sick Persons
Authors: Ankit Chaudhary, Jagdish L. Raheja
Date: 2013-04-17 00:00:00
Tasks: Fingertip Detection, General Classification

This paper discusses a vision based health monitoring system which would be
very easy in use and deployment. Elder and sick people who are not able to talk
or walk they are dependent on other human beings for their daily needs and need
continuous monitoring. The developed system provides facility to the sick or
elder person to describe his or her need to their caretaker in lingual
description by showing particular hand gesture with the developed system. This
system uses fingertip detection technique for gesture extraction and artificial
neural network for gesture classification and recognition. The system is able
to work in different light conditions and can be connected to different devices
to announce users need on a distant location.
A Hash based Approach for Secure Keyless Steganography in Lossless RGB Images
Authors: Ankit Chaudhary, J. Vasavada, J. L. Raheja, S. Kumar, M. Sharma
Date: 2012-11-23 00:00:00
Tasks: Clustering

This paper proposes an improved steganography approach for hiding text
messages in lossless RGB images. The objective of this work is to increase the
security level and to improve the storage capacity with compression techniques.
The security level is increased by randomly distributing the text message over
the entire image instead of clustering within specific image portions. Storage
capacity is increased by utilizing all the color channels for storing
information and providing the source text message compression. The degradation
of the images can be minimized by changing only one least significant bit per
color channel for hiding the message, incurring a very little change in the
original image. Using steganography alone with simple LSB has a potential
problem that the secret message is easily detectable from the histogram
analysis method. To improve the security as well as the image embedding
capacity indirectly, a compression based scheme is introduced. Various tests
have been done to check the storage capacity and message distribution. These
testes show the superiority of the proposed approach with respect to other
existing approaches.
Easy and hard functions for the Boolean hidden shift problem
Authors: Andrew M. Childs, Robin Kothari, Maris Ozols, Martin Roetteler
Date: 2013-04-16 00:00:00

We study the quantum query complexity of the Boolean hidden shift problem.
Given oracle access to f(x+s) for a known Boolean function f, the task is to
determine the n-bit string s. The quantum query complexity of this problem
depends strongly on f. We demonstrate that the easiest instances of this
problem correspond to bent functions, in the sense that an exact one-query
algorithm exists if and only if the function is bent. We partially characterize
the hardest instances, which include delta functions. Moreover, we show that
the problem is easy for random functions, since two queries suffice. Our
algorithm for random functions is based on performing the pretty good
measurement on several copies of a certain state; its analysis relies on the
Fourier transform. We also use this approach to improve the quantum rejection
sampling approach to the Boolean hidden shift problem.
Speckle Reduction in Polarimetric SAR Imagery with Stochastic Distances and Nonlocal Means
Authors: Leonardo Torres, Sidnei J. S. Sant'Anna, Corina C. Freitas, Alejandro C. Frery
Date: 2013-04-16 00:00:00
Tasks: Image Quality Assessment

This paper presents a technique for reducing speckle in Polarimetric
Synthetic Aperture Radar (PolSAR) imagery using Nonlocal Means and a
statistical test based on stochastic divergences. The main objective is to
select homogeneous pixels in the filtering area through statistical tests
between distributions. This proposal uses the complex Wishart model to describe
PolSAR data, but the technique can be extended to other models. The weights of
the location-variant linear filter are function of the p-values of tests which
verify the hypothesis that two samples come from the same distribution and,
therefore, can be used to compute a local mean. The test stems from the family
of (h-phi) divergences which originated in Information Theory. This novel
technique was compared with the Boxcar, Refined Lee and IDAN filters. Image
quality assessment methods on simulated and real data are employed to validate
the performance of this approach. We show that the proposed filter also
enhances the polarimetric entropy and preserves the scattering information of
the targets.
PAC Quasi-automatizability of Resolution over Restricted Distributions
Authors: Brendan Juba
Date: 2013-04-16 00:00:00
Tasks: Two-sample testing

We consider principled alternatives to unsupervised learning in data mining
by situating the learning task in the context of the subsequent analysis task.
Specifically, we consider a query-answering (hypothesis-testing) task: In the
combined task, we decide whether an input query formula is satisfied over a
background distribution by using input examples directly, rather than invoking
a two-stage process in which (i) rules over the distribution are learned by an
unsupervised learning algorithm and (ii) a reasoning algorithm decides whether
or not the query formula follows from the learned rules. In a previous work
(2013), we observed that the learning task could satisfy numerous desirable
criteria in this combined context -- effectively matching what could be
achieved by agnostic learning of CNFs from partial information -- that are not
known to be achievable directly. In this work, we show that likewise, there are
reasoning tasks that are achievable in such a combined context that are not
known to be achievable directly (and indeed, have been seriously conjectured to
be impossible, cf. (Alekhnovich and Razborov, 2008)). Namely, we test for a
resolution proof of the query formula of a given size in quasipolynomial time
(that is, "quasi-automatizing" resolution). The learning setting we consider is
a partial-information, restricted-distribution setting that generalizes
learning parities over the uniform distribution from partial information,
another task that is known not to be achievable directly in various models (cf.
(Ben-David and Dichterman, 1998) and (Michael, 2010)).
Learning Heteroscedastic Models by Convex Programming under Group Sparsity
Authors: Arnak S. Dalalyan, Mohamed Hebiri, Katia Méziani, Joseph Salmon
Date: 2013-04-16 00:00:00
Tasks: Time Series, Time Series Analysis

Popular sparse estimation methods based on $\ell_1$-relaxation, such as the
Lasso and the Dantzig selector, require the knowledge of the variance of the
noise in order to properly tune the regularization parameter. This constitutes
a major obstacle in applying these methods in several frameworks---such as time
series, random fields, inverse problems---for which the noise is rarely
homoscedastic and its level is hard to know in advance. In this paper, we
propose a new approach to the joint estimation of the conditional mean and the
conditional variance in a high-dimensional (auto-) regression setting. An
attractive feature of the proposed estimator is that it is efficiently
computable even for very large scale problems by solving a second-order cone
program (SOCP). We present theoretical analysis and numerical results assessing
the performance of the proposed procedure.
Heterogeneous patterns enhancing static and dynamic texture classification
Authors: Núbia Rosa da Silva, Odemir Martinez Bruno
Date: 2013-04-16 00:00:00
Tasks: Classification, General Classification, Texture Classification

Some mixtures, such as colloids like milk, blood, and gelatin, have
homogeneous appearance when viewed with the naked eye, however, to observe them
at the nanoscale is possible to understand the heterogeneity of its components.
The same phenomenon can occur in pattern recognition in which it is possible to
see heterogeneous patterns in texture images. However, current methods of
texture analysis can not adequately describe such heterogeneous patterns.
Common methods used by researchers analyse the image information in a global
way, taking all its features in an integrated manner. Furthermore, multi-scale
analysis verifies the patterns at different scales, but still preserving the
homogeneous analysis. On the other hand various methods use textons to
represent the texture, breaking texture down into its smallest unit. To tackle
this problem, we propose a method to identify texture patterns not small as
textons at distinct scales enhancing the separability among different types of
texture. We find sub patterns of texture according to the scale and then group
similar patterns for a more refined analysis. Tests were performed in four
static texture databases and one dynamic one. Results show that our method
provides better classification rate compared with conventional approaches both
in static and in dynamic texture.
Sentiment Analysis : A Literature Survey
Authors: Subhabrata Mukherjee, Pushpak Bhattacharyya
Date: 2013-04-16 00:00:00
Tasks: Opinion Mining, Sentiment Analysis, Survey

Our day-to-day life has always been influenced by what people think. Ideas
and opinions of others have always affected our own opinions. The explosion of
Web 2.0 has led to increased activity in Podcasting, Blogging, Tagging,
Contributing to RSS, Social Bookmarking, and Social Networking. As a result
there has been an eruption of interest in people to mine these vast resources
of data for opinions. Sentiment Analysis or Opinion Mining is the computational
treatment of opinions, sentiments and subjectivity of text. In this report, we
take a look at the various challenges and applications of Sentiment Analysis.
We will discuss in details various approaches to perform a computational
treatment of sentiments and opinions. Various supervised or data-driven
techniques to SA like Na\"ive Byes, Maximum Entropy, SVM, and Voted Perceptrons
will be discussed and their strengths and drawbacks will be touched upon. We
will also see a new dimension of analyzing sentiments by Cognitive Psychology
mainly through the work of Janyce Wiebe, where we will see ways to detect
subjectivity, perspective in narrative and understanding the discourse
structure. We will also study some specific topics in Sentiment Analysis and
the contemporary works in those areas.
Mining to Compact CNF Propositional Formulae
Authors: Said Jabbour, Lakhdar Sais, Yakoub Salhi
Date: 2013-04-16 00:00:00

In this paper, we propose a first application of data mining techniques to
propositional satisfiability. Our proposed Mining4SAT approach aims to discover
and to exploit hidden structural knowledge for reducing the size of
propositional formulae in conjunctive normal form (CNF). Mining4SAT combines
both frequent itemset mining techniques and Tseitin's encoding for a compact
representation of CNF formulae. The experiments of our Mining4SAT approach show
interesting reductions of the sizes of many application instances taken from
the last SAT competitions.
A generalized risk approach to path inference based on hidden Markov models
Authors: Jüri Lember, Alexey A. Koloydenko
Date: 2010-07-21 00:00:00

Motivated by the unceasing interest in hidden Markov models (HMMs), this
paper re-examines hidden path inference in these models, using primarily a
risk-based framework. While the most common maximum a posteriori (MAP), or
Viterbi, path estimator and the minimum error, or Posterior Decoder (PD), have
long been around, other path estimators, or decoders, have been either only
hinted at or applied more recently and in dedicated applications generally
unfamiliar to the statistical learning community. Over a decade ago, however, a
family of algorithmically defined decoders aiming to hybridize the two standard
ones was proposed (Brushe et al., 1998). The present paper gives a careful
analysis of this hybridization approach, identifies several problems and issues
with it and other previously proposed approaches, and proposes practical
resolutions of those. Furthermore, simple modifications of the classical
criteria for hidden path recognition are shown to lead to a new class of
decoders. Dynamic programming algorithms to compute these decoders in the usual
forward-backward manner are presented. A particularly interesting subclass of
such estimators can be also viewed as hybrids of the MAP and PD estimators.
Similar to previously proposed MAP-PD hybrids, the new class is parameterized
by a small number of tunable parameters. Unlike their algorithmic predecessors,
the new risk-based decoders are more clearly interpretable, and, most
importantly, work "out of the box" in practice, which is demonstrated on some
real bioinformatics tasks and data. Some further generalizations and
applications are discussed in conclusion.
Efficient Computation of Mean Truncated Hitting Times on Very Large Graphs
Authors: Joel Lang, James Henderson
Date: 2013-04-16 00:00:00
Tasks: Collaborative Filtering

Previous work has shown the effectiveness of random walk hitting times as a
measure of dissimilarity in a variety of graph-based learning problems such as
collaborative filtering, query suggestion or finding paraphrases. However,
application of hitting times has been limited to small datasets because of
computational restrictions. This paper develops a new approximation algorithm
with which hitting times can be computed on very large, disk-resident graphs,
making their application possible to problems which were previously out of
reach. This will potentially benefit a range of large-scale problems.
Sparse Coding and Dictionary Learning for Symmetric Positive Definite Matrices: A Kernel Approach
Authors: Mehrtash T. Harandi, Conrad Sanderson, Richard Hartley, Brian C. Lovell
Date: 2013-04-16 00:00:00
Tasks: Dictionary Learning, Face Recognition, General Classification, Person Re-Identification, Texture Classification

Recent advances suggest that a wide range of computer vision problems can be
addressed more appropriately by considering non-Euclidean geometry. This paper
tackles the problem of sparse coding and dictionary learning in the space of
symmetric positive definite matrices, which form a Riemannian manifold. With
the aid of the recently introduced Stein kernel (related to a symmetric version
of Bregman matrix divergence), we propose to perform sparse coding by embedding
Riemannian manifolds into reproducing kernel Hilbert spaces. This leads to a
convex and kernel version of the Lasso problem, which can be solved
efficiently. We furthermore propose an algorithm for learning a Riemannian
dictionary (used for sparse coding), closely tied to the Stein kernel.
Experiments on several classification tasks (face recognition, texture
classification, person re-identification) show that the proposed sparse coding
approach achieves notable improvements in discrimination accuracy, in
comparison to state-of-the-art methods such as tensor sparse coding, Riemannian
locality preserving projection, and symmetry-driven accumulation of local
features.
Why Size Matters: Feature Coding as Nystrom Sampling
Authors: Oriol Vinyals, Yangqing Jia, Trevor Darrell
Date: 2013-01-15 00:00:00
Tasks: Computational Efficiency

Recently, the computer vision and machine learning community has been in
favor of feature extraction pipelines that rely on a coding step followed by a
linear classifier, due to their overall simplicity, well understood properties
of linear classifiers, and their computational efficiency. In this paper we
propose a novel view of this pipeline based on kernel methods and Nystrom
sampling. In particular, we focus on the coding of a data point with a local
representation based on a dictionary with fewer elements than the number of
data points, and view it as an approximation to the actual function that would
compute pair-wise similarity to all data points (often too many to compute in
practice), followed by a Nystrom sampling step to select a subset of all data
points.
  Furthermore, since bounds are known on the approximation power of Nystrom
sampling as a function of how many samples (i.e. dictionary size) we consider,
we can derive bounds on the approximation of the exact (but expensive to
compute) kernel matrix, and use it as a proxy to predict accuracy as a function
of the dictionary size, which has been observed to increase but also to
saturate as we increase its size. This model may help explaining the positive
effect of the codebook size and justifying the need to stack more layers (often
referred to as deep learning), as flat models empirically saturate as we add
more complexity.
Local Structure Matching Driven by Joint-Saliency-Structure Adaptive Kernel Regression
Authors: Binjie Qin, Zhuangming Shen, Zien Zhou, Jiawei Zhou, Jiuai Sun, HUI ZHANG, Mingxing Hu, Yisong Lv
Date: 2013-02-03 00:00:00
Tasks: Image Registration, regression

For nonrigid image registration, matching the particular structures (or the
outliers) that have missing correspondence and/or local large deformations, can
be more difficult than matching the common structures with small deformations
in the two images. Most existing works depend heavily on the outlier
segmentation to remove the outlier effect in the registration. Moreover, these
works do not handle simultaneously the missing correspondences and local large
deformations. In this paper, we defined the nonrigid image registration as a
local adaptive kernel regression which locally reconstruct the moving image's
dense deformation vectors from the sparse deformation vectors in the
multi-resolution block matching. The kernel function of the kernel regression
adapts its shape and orientation to the reference image's structure to gather
more deformation vector samples of the same structure for the iterative
regression computation, whereby the moving image's local deformations could be
compliant with the reference image's local structures. To estimate the local
deformations around the outliers, we use joint saliency map that highlights the
corresponding saliency structures (called Joint Saliency Structures, JSSs) in
the two images to guide the dense deformation reconstruction by emphasizing
those JSSs' sparse deformation vectors in the kernel regression. The
experimental results demonstrate that by using local JSS adaptive kernel
regression, the proposed method achieves almost the best performance in
alignment of all challenging image pairs with outlier structures compared with
other five state-of-the-art nonrigid registration algorithms.
Shadow Estimation Method for "The Episolar Constraint: Monocular Shape from Shadow Correspondence"
Authors: Austin Abrams, Chris Hawley, Kylia Miskell, Adina Stoica, Nathan Jacobs, Robert Pless
Date: 2013-04-15 00:00:00

Recovering shadows is an important step for many vision algorithms. Current
approaches that work with time-lapse sequences are limited to simple
thresholding heuristics. We show these approaches only work with very careful
tuning of parameters, and do not work well for long-term time-lapse sequences
taken over the span of many months. We introduce a parameter-free expectation
maximization approach which simultaneously estimates shadows, albedo, surface
normals, and skylight. This approach is more accurate than previous methods,
works over both very short and very long sequences, and is robust to the
effects of nonlinear camera response. Finally, we demonstrate that the shadow
masks derived through this algorithm substantially improve the performance of
sun-based photometric stereo compared to earlier shadow mask estimation.
Identifying Independence in Relational Models
Authors: Marc Maier, David Jensen
Date: 2012-06-15 00:00:00

The rules of d-separation provide a framework for deriving conditional
independence facts from model structure. However, this theory only applies to
simple directed graphical models. We introduce relational d-separation, a
theory for deriving conditional independence in relational models. We provide a
sound, complete, and computationally efficient method for relational
d-separation, and we present empirical results that demonstrate effectiveness.
Link Prediction with Social Vector Clocks
Authors: Conrad Lee, Bobo Nick, Ulrik Brandes, Pádraig Cunningham
Date: 2013-04-15 00:00:00
Tasks: Distributed Computing, Link Prediction, Prediction

State-of-the-art link prediction utilizes combinations of complex features
derived from network panel data. We here show that computationally less
expensive features can achieve the same performance in the common scenario in
which the data is available as a sequence of interactions. Our features are
based on social vector clocks, an adaptation of the vector-clock concept
introduced in distributed computing to social interaction networks. In fact,
our experiments suggest that by taking into account the order and spacing of
interactions, social vector clocks exploit different aspects of link formation
so that their combination with previous approaches yields the most accurate
predictor to date.
Coordinating metaheuristic agents with swarm intelligence
Authors: Mehmet Emin Aydin
Date: 2013-04-15 00:00:00

Coordination of multi agent systems remains as a problem since there is no
prominent method to completely solve this problem. Metaheuristic agents are
specific implementations of multi-agent systems, which imposes working together
to solve optimisation problems with metaheuristic algorithms. The idea borrowed
from swarm intelligence seems working much better than those implementations
suggested before. This paper reports the performance of swarms of simulated
annealing agents collaborating with particle swarm optimization algorithm. The
proposed approach is implemented for multidimensional knapsack problem and has
resulted much better than some other works published before.
Multispectral Spatial Characterization: Application to Mitosis Detection in Breast Cancer Histopathology
Authors: H. Irshad, A. Gouaillard, L. Roux, D. Racoceanu
Date: 2013-04-15 00:00:00
Tasks: General Classification, Mitosis Detection

Accurate detection of mitosis plays a critical role in breast cancer
histopathology. Manual detection and counting of mitosis is tedious and subject
to considerable inter- and intra-reader variations. Multispectral imaging is a
recent medical imaging technology, proven successful in increasing the
segmentation accuracy in other fields. This study aims at improving the
accuracy of mitosis detection by developing a specific solution using
multispectral and multifocal imaging of breast cancer histopathological data.
We propose to enable clinical routine-compliant quality of mitosis
discrimination from other objects. The proposed framework includes
comprehensive analysis of spectral bands and z-stack focus planes, detection of
expected mitotic regions (candidates) in selected focus planes and spectral
bands, computation of multispectral spatial features for each candidate,
selection of multispectral spatial features and a study of different
state-of-the-art classification methods for candidates classification as
mitotic or non mitotic figures. This framework has been evaluated on MITOS
multispectral medical dataset and achieved 60% detection rate and 57%
F-Measure. Our results indicate that multispectral spatial features have more
information for mitosis classification in comparison with white spectral band
features, being therefore a very promising exploration area to improve the
quality of the diagnosis assistance in histopathology.