pwc-archive/papers-with-abstracts huggingface dataset:
Supervised Metric Learning with Generalization Guarantees
Authors: Aurélien Bellet
Date: 2013-07-17 00:00:00
Tasks: Generalization Bounds, Metric Learning

The crucial importance of metrics in machine learning algorithms has led to
an increasing interest in optimizing distance and similarity functions, an area
of research known as metric learning. When data consist of feature vectors, a
large body of work has focused on learning a Mahalanobis distance. Less work
has been devoted to metric learning from structured objects (such as strings or
trees), most of it focusing on optimizing a notion of edit distance. We
identify two important limitations of current metric learning approaches.
First, they allow to improve the performance of local algorithms such as
k-nearest neighbors, but metric learning for global algorithms (such as linear
classifiers) has not been studied so far. Second, the question of the
generalization ability of metric learning methods has been largely ignored. In
this thesis, we propose theoretical and algorithmic contributions that address
these limitations. Our first contribution is the derivation of a new kernel
function built from learned edit probabilities. Our second contribution is a
novel framework for learning string and tree edit similarities inspired by the
recent theory of (e,g,t)-good similarity functions. Using uniform stability
arguments, we establish theoretical guarantees for the learned similarity that
give a bound on the generalization error of a linear classifier built from that
similarity. In our third contribution, we extend these ideas to metric learning
from feature vectors by proposing a bilinear similarity learning method that
efficiently optimizes the (e,g,t)-goodness. Generalization guarantees are
derived for our approach, highlighting that our method minimizes a tighter
bound on the generalization error of the classifier. Our last contribution is a
framework for establishing generalization bounds for a large class of existing
metric learning algorithms based on a notion of algorithmic robustness.
Improved Branch-and-Bound for Low Autocorrelation Binary Sequences
Authors: S. D. Prestwich
Date: 2013-05-27 00:00:00

The Low Autocorrelation Binary Sequence problem has applications in
telecommunications, is of theoretical interest to physicists, and has inspired
many optimisation researchers. Metaheuristics for the problem have progressed
greatly in recent years but complete search has not progressed since a
branch-and-bound method of 1996. In this paper we find four ways of improving
branch-and-bound, leading to a tighter relaxation, faster convergence to
optimality, and better empirical scalability.
The Use of Cuckoo Search in Estimating the Parameters of Software Reliability Growth Models
Authors: Dr. Najla Akram AL-Saati, Marwa Abd-AlKareem
Date: 2013-07-23 00:00:00
Tasks: Attribute

This work aims to investigate the reliability of software products as an
important attribute of computer programs; it helps to decide the degree of
trustworthiness a program has in accomplishing its specific functions. This is
done using the Software Reliability Growth Models (SRGMs) through the
estimation of their parameters. The parameters are estimated in this work based
on the available failure data and with the search techniques of Swarm
Intelligence, namely, the Cuckoo Search (CS) due to its efficiency,
effectiveness and robustness. A number of SRGMs is studied, and the results are
compared to Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO)
and extended ACO. Results show that CS outperformed both PSO and ACO in finding
better parameters tested using identical datasets. It was sometimes
outperformed by the extended ACO. Also in this work, the percentages of
training data to testing data are investigated to show their impact on the
results.
Machine learning of hierarchical clustering to segment 2D and 3D images
Authors: Juan Nunez-Iglesias, Ryan Kennedy, Toufiq Parag, Jianbo Shi, Dmitri B. Chklovskii
Date: 2013-03-25 00:00:00
Tasks: Active Learning, BIG-bench Machine Learning, Clustering, Segmentation, Superpixels

We aim to improve segmentation through the use of machine learning tools
during region agglomeration. We propose an active learning approach for
performing hierarchical agglomerative segmentation from superpixels. Our method
combines multiple features at all scales of the agglomerative process, works
for data with an arbitrary number of dimensions, and scales to very large
datasets. We advocate the use of variation of information to measure
segmentation accuracy, particularly in 3D electron microscopy (EM) images of
neural tissue, and using this metric demonstrate an improvement over competing
algorithms in EM and natural images.
Numerical Methods for Coupled Reconstruction and Registration in Digital Breast Tomosynthesis
Authors: Guang Yang, John H. Hipwell, David J. Hawkes, Simon R. Arridge
Date: 2013-07-23 00:00:00
Tasks: Diagnostic, Image Registration

Digital Breast Tomosynthesis (DBT) provides an insight into the fine details
of normal fibroglandular tissues and abnormal lesions by reconstructing a
pseudo-3D image of the breast. In this respect, DBT overcomes a major
limitation of conventional X-ray mammography by reducing the confounding
effects caused by the superposition of breast tissue. In a breast cancer
screening or diagnostic context, a radiologist is interested in detecting
change, which might be indicative of malignant disease. To help automate this
task image registration is required to establish spatial correspondence between
time points. Typically, images, such as MRI or CT, are first reconstructed and
then registered. This approach can be effective if reconstructing using a
complete set of data. However, for ill-posed, limited-angle problems such as
DBT, estimating the deformation is complicated by the significant artefacts
associated with the reconstruction, leading to severe inaccuracies in the
registration. This paper presents a mathematical framework, which couples the
two tasks and jointly estimates both image intensities and the parameters of a
transformation.
  We evaluate our methods using various computational digital phantoms,
uncompressed breast MR images, and in-vivo DBT simulations. Firstly, we compare
both iterative and simultaneous methods to the conventional, sequential method
using an affine transformation model. We show that jointly estimating image
intensities and parametric transformations gives superior results with respect
to reconstruction fidelity and registration accuracy. Also, we incorporate a
non-rigid B-spline transformation model into our simultaneous method. The
results demonstrate a visually plausible recovery of the deformation with
preservation of the reconstruction fidelity.
Perceptron Mistake Bounds
Authors: Mehryar Mohri, Afshin Rostamizadeh
Date: 2013-05-01 00:00:00
Tasks: Survey

We present a brief survey of existing mistake bounds and introduce novel
bounds for the Perceptron or the kernel Perceptron algorithm. Our novel bounds
generalize beyond standard margin-loss type bounds, allow for any convex and
Lipschitz loss function, and admit a very simple proof.
How to minimize the energy consumption in mobile ad-hoc networks
Authors: Abdellah Idrissi
Date: 2013-07-22 00:00:00
Tasks: energy management, Management

In this work we are interested in the problem of energy management in Mobile
Ad-hoc Network (MANET). The solving and optimization of MANET allow assisting
the users to efficiently use their devices in order to minimize the batteries
power consumption. In this framework, we propose a modelling of the MANET in
form of a Constraint Optimization Problem called COMANET. Then, in the
objective to minimize the consumption of batteries power, we present an
approach based on an adaptation of the A star algorithm to the MANET problem
called MANED. Finally, we expose some experimental results showing utility of
this approach.
An Adaptive GMM Approach to Background Subtraction for Application in Real Time Surveillance
Authors: Subra Mukherjee, Karen Das
Date: 2013-07-22 00:00:00
Tasks: Detecting Shadows, Management

Efficient security management has become an important parameter in todays
world. As the problem is growing, there is an urgent need for the introduction
of advanced technology and equipment to improve the state-of art of
surveillance. In this paper we propose a model for real time background
subtraction using AGMM. The proposed model is robust and adaptable to dynamic
background, fast illumination changes, repetitive motion. Also we have
incorporated a method for detecting shadows using the Horpresert color model.
The proposed model can be employed for monitoring areas where movement or entry
is highly restricted. So on detection of any unexpected events in the scene an
alarm can be triggered and hence we can achieve real time surveillance even in
the absence of constant human monitoring.
Bayesian inference for logistic models using Polya-Gamma latent variables
Authors: Nicholas G. Polson, James G. Scott, Jesse Windle
Date: 2012-05-02 00:00:00
Tasks: Bayesian Inference, Computational Efficiency, Data Augmentation, Numerical Integration, regression

We propose a new data-augmentation strategy for fully Bayesian inference in
models with binomial likelihoods. The approach appeals to a new class of
Polya-Gamma distributions, which are constructed in detail. A variety of
examples are presented to show the versatility of the method, including
logistic regression, negative binomial regression, nonlinear mixed-effects
models, and spatial models for count data. In each case, our data-augmentation
strategy leads to simple, effective methods for posterior inference that: (1)
circumvent the need for analytic approximations, numerical integration, or
Metropolis-Hastings; and (2) outperform other known data-augmentation
strategies, both in ease of use and in computational efficiency. All methods,
including an efficient sampler for the Polya-Gamma distribution, are
implemented in the R package BayesLogit.
  In the technical supplement appended to the end of the paper, we provide
further details regarding the generation of Polya-Gamma random variables; the
empirical benchmarks reported in the main manuscript; and the extension of the
basic data-augmentation framework to contingency tables and multinomial
outcomes.
Appearance Descriptors for Person Re-identification: a Comprehensive Review
Authors: Riccardo Satta
Date: 2013-07-22 00:00:00
Tasks: Person Re-Identification

In video-surveillance, person re-identification is the task of recognising
whether an individual has already been observed over a network of cameras.
Typically, this is achieved by exploiting the clothing appearance, as classical
biometric traits like the face are impractical in real-world video surveillance
scenarios. Clothing appearance is represented by means of low-level
\textit{local} and/or \textit{global} features of the image, usually extracted
according to some part-based body model to treat different body parts (e.g.
torso and legs) independently. This paper provides a comprehensive review of
current approaches to build appearance descriptors for person
re-identification. The most relevant techniques are described in detail, and
categorised according to the body models and features used. The aim of this
work is to provide a structured body of knowledge and a starting point for
researchers willing to conduct novel investigations on this challenging topic.
A New Strategy of Cost-Free Learning in the Class Imbalance Problem
Authors: Xiaowan Zhang, Bao-Gang Hu
Date: 2013-07-22 00:00:00
Tasks: General Classification

In this work, we define cost-free learning (CFL) formally in comparison with
cost-sensitive learning (CSL). The main difference between them is that a CFL
approach seeks optimal classification results without requiring any cost
information, even in the class imbalance problem. In fact, several CFL
approaches exist in the related studies, such as sampling and some
criteria-based pproaches. However, to our best knowledge, none of the existing
CFL and CSL approaches are able to process the abstaining classifications
properly when no information is given about errors and rejects. Based on
information theory, we propose a novel CFL which seeks to maximize normalized
mutual information of the targets and the decision outputs of classifiers.
Using the strategy, we can deal with binary/multi-class classifications
with/without abstaining. Significant features are observed from the new
strategy. While the degree of class imbalance is changing, the proposed
strategy is able to balance the errors and rejects accordingly and
automatically. Another advantage of the strategy is its ability of deriving
optimal rejection thresholds for abstaining classifications and the
"equivalent" costs in binary classifications. The connection between rejection
thresholds and ROC curve is explored. Empirical investigation is made on
several benchmark data sets in comparison with other existing approaches. The
classification results demonstrate a promising perspective of the strategy in
machine learning.
Top-down and Bottom-up Feature Combination for Multi-sensor Attentive Robots
Authors: Esther L. Colombini, Alexandre S. Simões, Carlos H. C. Ribeiro
Date: 2013-07-22 00:00:00

The information available to robots in real tasks is widely distributed both
in time and space, requiring the agent to search for relevant data. In humans,
that face the same problem when sounds, images and smells are presented to
their sensors in a daily scene, a natural system is applied: Attention. As
vision plays an important role in our routine, most research regarding
attention has involved this sensorial system and the same has been replicated
to the robotics field. However,most of the robotics tasks nowadays do not rely
only in visual data, that are still costly. To allow the use of attentive
concepts with other robotics sensors that are usually used in tasks such as
navigation, self-localization, searching and mapping, a generic attentional
model has been previously proposed. In this work, feature mapping functions
were designed to build feature maps to this attentive model from data from
range scanner and sonar sensors. Experiments were performed in a high fidelity
simulated robotics environment and results have demonstrated the capability of
the model on dealing with both salient stimuli and goal-driven attention over
multiple features extracted from multiple sensors.
Understanding Humans' Strategies in Maze Solving
Authors: Min Zhao, Andre G. Marquez
Date: 2013-07-22 00:00:00
Tasks: Error Understanding

Navigating through a visual maze relies on the strategic use of eye movements
to select and identify the route. When navigating the maze, there are
trade-offs between exploring to the environment and relying on memory. This
study examined strategies used to navigating through novel and familiar mazes
that were viewed from above and traversed by a mouse cursor. Eye and mouse
movements revealed two modes that almost never occurred concurrently:
exploration and guidance. Analyses showed that people learned mazes and were
able to devise and carry out complex, multi-faceted strategies that traded-off
visual exploration against active motor performance. These strategies took into
account available visual information, memory, confidence, the estimated cost in
time for exploration, and idiosyncratic tolerance for error. Understanding the
strategies humans used for maze solving is valuable for applications in
cognitive neuroscience as well as in AI, robotics and human-robot interactions.
Saliency-Guided Perceptual Grouping Using Motion Cues in Region-Based Artificial Visual Attention
Authors: Jan Tünnermann, Dieter Enns, Bärbel Mertsching
Date: 2013-07-22 00:00:00
Tasks: Object

Region-based artificial attention constitutes a framework for bio-inspired
attentional processes on an intermediate abstraction level for the use in
computer vision and mobile robotics. Segmentation algorithms produce regions of
coherently colored pixels. These serve as proto-objects on which the
attentional processes determine image portions of relevance. A single
region---which not necessarily represents a full object---constitutes the focus
of attention. For many post-attentional tasks, however, such as identifying or
tracking objects, single segments are not sufficient. Here, we present a
saliency-guided approach that groups regions that potentially belong to the
same object based on proximity and similarity of motion. We compare our results
to object selection by thresholding saliency maps and a further
attention-guided strategy.
Is Bottom-Up Attention Useful for Scene Recognition?
Authors: Samuel F. Dodge, Lina J. Karam
Date: 2013-07-22 00:00:00
Tasks: Classification, General Classification, Scene Recognition

The human visual system employs a selective attention mechanism to understand
the visual world in an eficient manner. In this paper, we show how
computational models of this mechanism can be exploited for the computer vision
application of scene recognition. First, we consider saliency weighting and
saliency pruning, and provide a comparison of the performance of different
attention models in these approaches in terms of classification accuracy.
Pruning can achieve a high degree of computational savings without
significantly sacrificing classification accuracy. In saliency weighting,
however, we found that classification performance does not improve. In
addition, we present a new method to incorporate salient and non-salient
regions for improved classification accuracy. We treat the salient and
non-salient regions separately and combine them using Multiple Kernel Learning.
We evaluate our approach using the UIUC sports dataset and find that with a
small training size, our method improves upon the classification accuracy of
the baseline bag of features approach.
Visual saliency estimation by integrating features using multiple kernel learning
Authors: Yasin Kavak, Erkut Erdem, Aykut Erdem
Date: 2013-07-22 00:00:00
Tasks: Object, Saliency Prediction

In the last few decades, significant achievements have been attained in
predicting where humans look at images through different computational models.
However, how to determine contributions of different visual features to overall
saliency still remains an open problem. To overcome this issue, a recent class
of models formulates saliency estimation as a supervised learning problem and
accordingly apply machine learning techniques. In this paper, we also address
this challenging problem and propose to use multiple kernel learning (MKL) to
combine information coming from different feature dimensions and to perform
integration at an intermediate level. Besides, we suggest to use responses of a
recently proposed filterbank of object detectors, known as Object-Bank, as
additional semantic high-level features. Here we show that our MKL-based
framework together with the proposed object-specific features provide
state-of-the-art performance as compared to SVM or AdaBoost-based saliency
models.
A study of parameters affecting visual saliency assessment
Authors: Nicolas Riche, Matthieu Duvinage, Matei Mancas, Bernard Gosselin, Thierry Dutoit
Date: 2013-07-22 00:00:00

Since the early 2000s, computational visual saliency has been a very active
research area. Each year, more and more new models are published in the main
computer vision conferences. Nowadays, one of the big challenges is to find a
way to fairly evaluate all of these models. In this paper, a new framework is
proposed to assess models of visual saliency. This evaluation is divided into
three experiments leading to the proposition of a new evaluation framework.
Each experiment is based on a basic question: 1) there are two ground truths
for saliency evaluation: what are the differences between eye fixations and
manually segmented salient regions?, 2) the properties of the salient regions:
for example, do large, medium and small salient regions present different
difficulties for saliency models? and 3) the metrics used to assess saliency
models: what advantages would there be to mix them with PCA? Statistical
analysis is used here to answer each of these three questions.
Using a Dynamic Neural Field Model to Explore a Direct Collicular Inhibition Account of Inhibition of Return
Authors: Jason Satel, Ross Story, Matthew D. Hilchey, Zhiguo Wang, Raymond M. Klein
Date: 2013-07-22 00:00:00

When the interval between a transient ash of light (a "cue") and a second
visual response signal (a "target") exceeds at least 200ms, responding is
slowest in the direction indicated by the first signal. This phenomenon is
commonly referred to as inhibition of return (IOR). The dynamic neural field
model (DNF) has proven to have broad explanatory power for IOR, effectively
capturing many empirical results. Previous work has used a short-term
depression (STD) implementation of IOR, but this approach fails to explain many
behavioral phenomena observed in the literature. Here, we explore a variant
model of IOR involving a combination of STD and delayed direct collicular
inhibition. We demonstrate that this hybrid model can better reproduce
established behavioural results. We use the results of this model to propose
several experiments that would yield particularly valuable insight into the
nature of the neurophysiological mechanisms underlying IOR.
Sub- Diving Labeling Method for Optimization Problem by Genetic Algorithm
Authors: Masoumeh Vali
Date: 2013-07-22 00:00:00
Tasks: global-optimization

In many global Optimization Problems, it is required to evaluate a global
point (min or max) in large space that calculation effort is very high. In this
paper is presented new approach for optimization problem with subdivision
labeling method (SLM) but in this method for higher dimensional has high
computational. SLM Genetic Algorithm (SLMGA) in optimization problems is one of
the solutions of this problem. In proposed algorithm the initial population is
crossing points and subdividing in each step is according to mutation. RSLMGA
is compared with other well known algorithms: DE, PGA, Grefensstette and
Eshelman and numerical results show that RSLMGA achieve global optimal point
with more decision by smaller generations.
Sub-Dividing Genetic Method for Optimization Problems
Authors: Masoumeh Vali
Date: 2013-07-22 00:00:00

Nowadays, optimization problem have more application in all major but they
have problem in computation. Computation global point in continuous functions
have high calculation and this became clearer in large space .In this paper, we
proposed Sub- Dividing Genetic Method(SGM) that have less computation than
other method for achieving global points . This method userotation mutation and
crossover based sub-division method that sub diving method is used for minimize
search space and rotation mutation with crossover is used for finding global
optimal points. In experimental, SGM algorithm is implemented on De Jong
function. The numerical examples show that SGM is performed more optimal than
other methods such as Grefensstette, Random Value, and PNG.
A New Approach for Finding the Global Optimal Point Using Subdividing Labeling Method (SLM)
Authors: Masoumeh Vali
Date: 2013-07-22 00:00:00
Tasks: global-optimization

In most global optimization problems, finding global optimal point inthe
multidimensional and great search space needs high computations. In this paper,
we present a new approach to find global optimal point with the low computation
and few steps using subdividing labeling method (SLM) which can also be used in
the multi-dimensional and great search space. In this approach, in each step,
crossing points will be labeled and complete label polytope search space of
selected polytope will be subdivided after being selected. SLM algorithm finds
the global point until h (subdivision function) turns into zero. SLM will be
implemented on five applications and compared with the latest techniques such
as random search, random search-walk and simulated annealing method. The
results of the proposed method demonstrate that our new approach is faster and
more reliable and presents an optimal time complexity O (logn).