pwc-archive/papers-with-abstracts huggingface dataset:
Personal Identification from Lip-Print Features using a Statistical Model
Authors: Saptarshi Bhattacharjee, S Arunkumar, Samir Kumar Bandyopadhyay
Date: 2013-09-30 00:00:00
Tasks: Edge Detection

This paper presents a novel approach towards identification of human beings
from the statistical analysis of their lip prints. Lip features are extracted
by studying the spatial orientations of the grooves present in lip prints of
individuals using standard edge detection techniques. Horizontal, vertical and
diagonal groove features are analysed using connected-component analysis to
generate the region-specific edge datasets. Comparison between test and
reference sample datasets against a threshold value to define a match yield
satisfactory results. FAR, FRR and ROC metrics have been used to gauge the
performance of the algorithm for real-world deployment in unimodal and
multimodal biometric verification systems.
An Image-Based Fluid Surface Pattern Model
Authors: Mauro de Amorim, Ricardo Fabbri, Lucia Maria dos Santos Pinto, Francisco Duarte Moura Neto
Date: 2013-09-30 00:00:00
Tasks: Dimensionality Reduction, model

This work aims at generating a model of the ocean surface and its dynamics
from one or more video cameras. The idea is to model wave patterns from video
as a first step towards a larger system of photogrammetric monitoring of marine
conditions for use in offshore oil drilling platforms. The first part of the
proposed approach consists in reducing the dimensionality of sensor data made
up of the many pixels of each frame of the input video streams. This enables
finding a concise number of most relevant parameters to model the temporal
dataset, yielding an efficient data-driven model of the evolution of the
observed surface. The second part proposes stochastic modeling to better
capture the patterns embedded in the data. One can then draw samples from the
final model, which are expected to simulate the behavior of previously observed
flow, in order to determine conditions that match new observations. In this
paper we focus on proposing and discussing the overall approach and on
comparing two different techniques for dimensionality reduction in the first
stage: principal component analysis and diffusion maps. Work is underway on the
second stage of constructing better stochastic models of fluid surface dynamics
as proposed here.
Linear Regression from Strategic Data Sources
Authors: Nicolas Gast, Stratis Ioannidis, Patrick Loiseau, Benjamin Roussillon
Date: 2013-09-30 00:00:00
Tasks: regression

Linear regression is a fundamental building block of statistical data analysis. It amounts to estimating the parameters of a linear model that maps input features to corresponding outputs. In the classical setting where the precision of each data point is fixed, the famous Aitken/Gauss-Markov theorem in statistics states that generalized least squares (GLS) is a so-called "Best Linear Unbiased Estimator" (BLUE). In modern data science, however, one often faces strategic data sources, namely, individuals who incur a cost for providing high-precision data. In this paper, we study a setting in which features are public but individuals choose the precision of the outputs they reveal to an analyst. We assume that the analyst performs linear regression on this dataset, and individuals benefit from the outcome of this estimation. We model this scenario as a game where individuals minimize a cost comprising two components: (a) an (agent-specific) disclosure cost for providing high-precision data; and (b) a (global) estimation cost representing the inaccuracy in the linear model estimate. In this game, the linear model estimate is a public good that benefits all individuals. We establish that this game has a unique non-trivial Nash equilibrium. We study the efficiency of this equilibrium and we prove tight bounds on the price of stability for a large class of disclosure and estimation costs. Finally, we study the estimator accuracy achieved at equilibrium. We show that, in general, Aitken's theorem does not hold under strategic data sources, though it does hold if individuals have identical disclosure costs (up to a multiplicative factor). When individuals have non-identical costs, we derive a bound on the improvement of the equilibrium estimation cost that can be achieved by deviating from GLS, under mild assumptions on the disclosure cost functions.
On statistics, computation and scalability
Authors: Michael. I. Jordan
Date: 2013-09-30 00:00:00

How should statistical procedures be designed so as to be scalable
computationally to the massive datasets that are increasingly the norm? When
coupled with the requirement that an answer to an inferential question be
delivered within a certain time budget, this question has significant
repercussions for the field of statistics. With the goal of identifying
"time-data tradeoffs," we investigate some of the statistical consequences of
computational perspectives on scability, in particular divide-and-conquer
methodology and hierarchies of convex relaxations.
Random walk kernels and learning curves for Gaussian process regression on random graphs
Authors: Matthew Urry, Peter Sollich
Date: 2012-11-06 00:00:00
Tasks: Gaussian Processes, regression

We consider learning on graphs, guided by kernels that encode similarity
between vertices. Our focus is on random walk kernels, the analogues of squared
exponential kernels in Euclidean spaces. We show that on large, locally
treelike, graphs these have some counter-intuitive properties, specifically in
the limit of large kernel lengthscales. We consider using these kernels as
covariance matrices of e.g.\ Gaussian processes (GPs). In this situation one
typically scales the prior globally to normalise the average of the prior
variance across vertices. We demonstrate that, in contrast to the Euclidean
case, this generically leads to significant variation in the prior variance
across vertices, which is undesirable from the probabilistic modelling point of
view. We suggest the random walk kernel should be normalised locally, so that
each vertex has the same prior variance, and analyse the consequences of this
by studying learning curves for Gaussian process regression. Numerical
calculations as well as novel theoretical predictions for the learning curves
using belief propagation make it clear that one obtains distinctly different
probabilistic models depending on the choice of normalisation. Our method for
predicting the learning curves using belief propagation is significantly more
accurate than previous approximations and should become exact in the limit of
large random graphs.
Generic Image Classification Approaches Excel on Face Recognition
Authors: Fumin Shen, Chunhua Shen
Date: 2013-09-22 00:00:00
Tasks: Classification, Dictionary Learning, Face Recognition, General Classification, image-classification, Image Classification

The main finding of this work is that the standard image classification pipeline, which consists of dictionary learning, feature encoding, spatial pyramid pooling and linear classification, outperforms all state-of-the-art face recognition methods on the tested benchmark datasets (we have tested on AR, Extended Yale B, the challenging FERET, and LFW-a datasets). This surprising and prominent result suggests that those advances in generic image classification can be directly applied to improve face recognition systems. In other words, face recognition may not need to be viewed as a separate object classification problem. While recently a large body of residual based face recognition methods focus on developing complex dictionary learning algorithms, in this work we show that a dictionary of randomly extracted patches (even from non-face images) can achieve very promising results using the image classification pipeline. That means, the choice of dictionary learning methods may not be important. Instead, we find that learning multiple dictionaries using different low-level image features often improve the final classification accuracy. Our proposed face recognition approach offers the best reported results on the widely-used face recognition benchmark datasets. In particular, on the challenging FERET and LFW-a datasets, we improve the best reported accuracies in the literature by about 20% and 30% respectively.
Signed Networks, Triadic Interactions and the Evolution of Cooperation
Authors: Simone Righi, Károly Takács
Date: 2013-09-30 00:00:00

We outline a model to study the evolution of cooperation in a population of
agents playing the prisoner's dilemma in signed networks. We highlight that if
only dyadic interactions are taken into account, cooperation never evolves.
However, when triadic considerations are introduced, a window of opportunity
for emergence of cooperation as a stable behaviour emerges.
Semi-structured data extraction and modelling: the WIA Project
Authors: Gianluca Colombo, Ettore Colombo, Andrea Bonomi, Alessandro Mosca, Simone Bassis
Date: 2013-09-30 00:00:00
Tasks: Evolutionary Algorithms, Management

Over the last decades, the amount of data of all kinds available
electronically has increased dramatically. Data are accessible through a range
of interfaces including Web browsers, database query languages,
application-specific interfaces, built on top of a number of different data
exchange formats. All these data span from un-structured to highly structured
data. Very often, some of them have structure even if the structure is
implicit, and not as rigid or regular as that found in standard database
systems. Spreadsheet documents are prototypical in this respect. Spreadsheets
are the lightweight technology able to supply companies with easy to build
business management and business intelligence applications, and business people
largely adopt spreadsheets as smart vehicles for data files generation and
sharing. Actually, the more spreadsheets grow in complexity (e.g., their use in
product development plans and quoting), the more their arrangement,
maintenance, and analysis appear as a knowledge-driven activity. The
algorithmic approach to the problem of automatic data structure extraction from
spreadsheet documents (i.e., grid-structured and free topological-related data)
emerges from the WIA project: Worksheets Intelligent Analyser. The
WIA-algorithm shows how to provide a description of spreadsheet contents in
terms of higher level of abstractions or conceptualisations. In particular, the
WIA-algorithm target is about the extraction of i) the calculus work-flow
implemented in the spreadsheets formulas and ii) the logical role played by the
data which take part into the calculus. The aim of the resulting
conceptualisations is to provide spreadsheets with abstract representations
useful for further model refinements and optimizations through evolutionary
algorithms computations.
A Hybrid Monte Carlo Ant Colony Optimization Approach for Protein Structure Prediction in the HP Model
Authors: Andrea G. Citrolo, Giancarlo Mauri
Date: 2013-09-30 00:00:00
Tasks: Protein Structure Prediction

The hydrophobic-polar (HP) model has been widely studied in the field of
protein structure prediction (PSP) both for theoretical purposes and as a
benchmark for new optimization strategies. In this work we introduce a new
heuristics based on Ant Colony Optimization (ACO) and Markov Chain Monte Carlo
(MCMC) that we called Hybrid Monte Carlo Ant Colony Optimization (HMCACO). We
describe this method and compare results obtained on well known HP instances in
the 3 dimensional cubic lattice to those obtained with standard ACO and
Simulated Annealing (SA). All methods were implemented using an unconstrained
neighborhood and a modified objective function to prevent the creation of
overlapping walks. Results show that our methods perform better than the other
heuristics in all benchmark instances.
An upper bound on prototype set size for condensed nearest neighbor
Authors: Eric Christiansen
Date: 2013-09-29 00:00:00
Tasks: General Classification

The condensed nearest neighbor (CNN) algorithm is a heuristic for reducing
the number of prototypical points stored by a nearest neighbor classifier,
while keeping the classification rule given by the reduced prototypical set
consistent with the full set. I present an upper bound on the number of
prototypical points accumulated by CNN. The bound originates in a bound on the
number of times the decision rule is updated during training in the multiclass
perceptron algorithm, and thus is independent of training set size.
Error AMP Chain Graphs
Authors: Jose M. Peña
Date: 2013-06-28 00:00:00
Tasks: Selection bias

Any regular Gaussian probability distribution that can be represented by an
AMP chain graph (CG) can be expressed as a system of linear equations with
correlated errors whose structure depends on the CG. However, the CG represents
the errors implicitly, as no nodes in the CG correspond to the errors. We
propose in this paper to add some deterministic nodes to the CG in order to
represent the errors explicitly. We call the result an EAMP CG. We will show
that, as desired, every AMP CG is Markov equivalent to its corresponding EAMP
CG under marginalization of the error nodes. We will also show that every EAMP
CG under marginalization of the error nodes is Markov equivalent to some LWF CG
under marginalization of the error nodes, and that the latter is Markov
equivalent to some directed and acyclic graph (DAG) under marginalization of
the error nodes and conditioning on some selection nodes. This is important
because it implies that the independence model represented by an AMP CG can be
accounted for by some data generating process that is partially observed and
has selection bias. Finally, we will show that EAMP CGs are closed under
marginalization. This is a desirable feature because it guarantees parsimonious
models under marginalization.
Correcting Multi-focus Images via Simple Standard Deviation for Image Fusion
Authors: Firas A. Jassim
Date: 2013-09-29 00:00:00
Tasks: Image Registration

Image fusion is one of the recent trends in image registration which is an
essential field of image processing. The basic principle of this paper is to
fuse multi-focus images using simple statistical standard deviation. Firstly,
the simple standard deviation for the k-by-k window inside each of the
multi-focus images was computed. The contribution in this paper came from the
idea that the focused part inside an image had high details rather than the
unfocused part. Hence, the dispersion between pixels inside the focused part is
higher than the dispersion inside the unfocused part. Secondly, a simple
comparison between the standard deviation for each k-by-k window in the
multi-focus images could be computed. The highest standard deviation between
all the computed standard deviations for the multi-focus images could be
treated as the optimal that is to be placed in the fused image. The
experimental visual results show that the proposed method produces very
satisfactory results in spite of its simplicity.
Fast Marginalized Block Sparse Bayesian Learning Algorithm
Authors: Benyuan Liu, Zhilin Zhang, Hongqi Fan, Qiang Fu
Date: 2012-11-21 00:00:00

The performance of sparse signal recovery from noise corrupted,
underdetermined measurements can be improved if both sparsity and correlation
structure of signals are exploited. One typical correlation structure is the
intra-block correlation in block sparse signals. To exploit this structure, a
framework, called block sparse Bayesian learning (BSBL), has been proposed
recently. Algorithms derived from this framework showed superior performance
but they are not very fast, which limits their applications. This work derives
an efficient algorithm from this framework, using a marginalized likelihood
maximization method. Compared to existing BSBL algorithms, it has close
recovery performance but is much faster. Therefore, it is more suitable for
large scale datasets and applications requiring real-time implementation.
Context-aware recommendations from implicit data via scalable tensor factorization
Authors: Balázs Hidasi, Domonkos Tikk
Date: 2013-09-29 00:00:00
Tasks: Computational Efficiency

Albeit the implicit feedback based recommendation problem - when only the
user history is available but there are no ratings - is the most typical
setting in real-world applications, it is much less researched than the
explicit feedback case. State-of-the-art algorithms that are efficient on the
explicit case cannot be automatically transformed to the implicit case if
scalability should be maintained. There are few implicit feedback benchmark
data sets, therefore new ideas are usually experimented on explicit benchmarks.
In this paper, we propose a generic context-aware implicit feedback recommender
algorithm, coined iTALS. iTALS applies a fast, ALS-based tensor factorization
learning method that scales linearly with the number of non-zero elements in
the tensor. We also present two approximate and faster variants of iTALS using
coordinate descent and conjugate gradient methods at learning. The method also
allows us to incorporate various contextual information into the model while
maintaining its computational efficiency. We present two context-aware variants
of iTALS incorporating seasonality and item purchase sequentiality into the
model to distinguish user behavior at different time intervals, and product
types with different repetitiveness. Experiments run on six data sets shows
that iTALS clearly outperforms context-unaware models and context aware
baselines, while it is on par with factorization machines (beats 7 times out of
12 cases) both in terms of recall and MAP.
Identificación y Registro Catastral de Cuerpos de Agua mediante Técnicas de Procesamiento Digital de Imagenes
Authors: Kevin Rojas Laura, Christhian Cardenas Alvarez
Date: 2013-09-29 00:00:00

The effects of global climate change on Peruvian glaciers have brought about
several processes of deglaciation during the last few years. The immediate
effect is the change of size of lakes and rivers. Public institutions that
monitor water resources currently have only recent studies which make up less
than 10% of the total. The effects of climate change and the lack of updated
information intensify social-economic problems related to water resources in
Peru. The objective of this research is to develop a software application to
automate the Cadastral Registry of Water Bodies in Peru, using techniques of
digital image processing, which would provide tools for detection, record,
temporal analysis and visualization of water bodies. The images used are from
the satellite Landsat5, which undergo a pre-processing of calibration and
correction of the satellite. Detection results are archived into a file that
contains location vectors and images of the segmentated bodies of water.
On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations
Authors: Tamir Hazan, Subhransu Maji, Tommi Jaakkola
Date: 2013-09-29 00:00:00
Proceeding: NeurIPS 2013 12

In this paper we describe how MAP inference can be used to sample efficiently
from Gibbs distributions. Specifically, we provide means for drawing either
approximate or unbiased samples from Gibbs' distributions by introducing low
dimensional perturbations and solving the corresponding MAP assignments. Our
approach also leads to new ways to derive lower bounds on partition functions.
We demonstrate empirically that our method excels in the typical "high signal -
high coupling" regime. The setting results in ragged energy landscapes that are
challenging for alternative approaches to sampling and/or lower bounds.
Meme and Variations: A Computer Model of Cultural Evolution
Authors: Liane Gabora
Date: 2013-09-29 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Diversity

Holland's (1975) genetic algorithm is a minimal computer model of natural selection that made it possible to investigate the effect of manipulating specific parameters on the evolutionary process. If culture is, like biology, a form of evolution, it should be possible to similarly abstract the underlying skeleton of the process and develop a minimal model of it. Meme and Variations, or MAV, is a computational model, inspired by the genetic algorithm, of how ideas evolve in a society of interacting individuals (Gabora 1995). The name is a pun on the classical music form 'theme and variations', because it is based on the premise that novel ideas are variations of old ones; they result from tweaking or combining existing ideas in new ways (Holland et al. 1981). MAV explores the impact of biological phenomena such as over-dominance and epistasis as well as cognitive and social phenomena such as the ability to learn generalizations or imitate others on the fitness and diversity of cultural transmissible actions.
An Application of Backpropagation Artificial Neural Network Method for Measuring The Severity of Osteoarthritis
Authors: Dian Pratiwi, Diaz D. Santika, Bens Pardamean
Date: 2013-09-29 00:00:00
Tasks: Quantization

The examination of Osteoarthritis disease through X-ray by rheumatology can
be classified into four grade of severity. This paper discusses about the
application of artificial neural network backpropagation method for measuring
the severity of the disease, where the observed X-ray range from wrist to
fingers. The main procedures of system in this paper is divided into three,
which are image processing, feature extraction, and artificial neural network
process. First, an X-ray image digital (200x150 pixels and greyscale) will be
thresholded, then extracted features based on probabilistic values of the color
intensity of seven bit quantization result, and statistical textures. That
feature values then will be normalizing to interval [0.1, 0.9], and then the
result would be processing on backpropagation artificial neural network system
as input to determine the severity of disease from an X-ray had input before
it. From testing with learning rate 0.3, momentum 0.4, hidden units five pieces
and about 132 feature vectors, this system had had a level of accuracy of 100%
for learning data, 80% for learning and non-learning data, and 66.6% for
non-learning data
CSIFT Based Locality-constrained Linear Coding for Image Classification
Authors: Chen Junzhou, Li Qing, Peng Qiang, Kin Hong Wong
Date: 2013-09-28 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

In the past decade, SIFT descriptor has been witnessed as one of the most
robust local invariant feature descriptors and widely used in various vision
tasks. Most traditional image classification systems depend on the
luminance-based SIFT descriptors, which only analyze the gray level variations
of the images. Misclassification may happen since their color contents are
ignored. In this article, we concentrate on improving the performance of
existing image classification algorithms by adding color information. To
achieve this purpose, different kinds of colored SIFT descriptors are
introduced and implemented. Locality-constrained Linear Coding (LLC), a
state-of-the-art sparse coding technology, is employed to construct the image
classification system for the evaluation. The real experiments are carried out
on several benchmarks. With the enhancements of color SIFT, the proposed image
classification system obtains approximate 3% improvement of classification
accuracy on the Caltech-101 dataset and approximate 4% improvement of
classification accuracy on the Caltech-256 dataset.