pwc-archive/papers-with-abstracts huggingface dataset:
Optimisation using Natural Language Processing: Personalized Tour Recommendation for Museums
Authors: Mayeul Mathias, Assema Moussa, Fen Zhou, Juan-Manuel Torres-Moreno, Marie-Sylvie Poli, Didier Josselin, Marc El-Bèze, Andréa Carneiro Linhares, Francoise Rigat
Date: 2015-01-06 00:00:00

This paper proposes a new method to provide personalized tour recommendation
for museum visits. It combines an optimization of preference criteria of
visitors with an automatic extraction of artwork importance from museum
information based on Natural Language Processing using textual energy. This
project includes researchers from computer and social sciences. Some results
are obtained with numerical experiments. They show that our model clearly
improves the satisfaction of the visitor who follows the proposed tour. This
work foreshadows some interesting outcomes and applications about on-demand
personalized visit of museums in a very near future.
Un résumeur à base de graphes, indépéndant de la langue
Authors: Juan-Manuel Torres-Moreno, Javier Ramirez, Iria da Cunha
Date: 2015-01-06 00:00:00
Tasks: Text Summarization

In this paper we present REG, a graph-based approach for study a fundamental
problem of Natural Language Processing (NLP): the automatic text summarization.
The algorithm maps a document as a graph, then it computes the weight of their
sentences. We have applied this approach to summarize documents in three
languages.
Analog Signal Processing Approach for Coarse and Fine Depth Estimation
Authors: Nihar Athreyas, Zhiguo Lai, Jai Gupta, Dev Gupta
Date: 2015-01-06 00:00:00
Tasks: Depth Estimation

Imaging and Image sensors is a field that is continuously evolving. There are
new products coming into the market every day. Some of these have very severe
Size, Weight and Power constraints whereas other devices have to handle very
high computational loads. Some require both these conditions to be met
simultaneously. Current imaging architectures and digital image processing
solutions will not be able to meet these ever increasing demands. There is a
need to develop novel imaging architectures and image processing solutions to
address these requirements. In this work we propose analog signal processing as
a solution to this problem. The analog processor is not suggested as a
replacement to a digital processor but it will be used as an augmentation
device which works in parallel with the digital processor, making the system
faster and more efficient. In order to show the merits of analog processing two
stereo correspondence algorithms are implemented. We propose novel
modifications to the algorithms and new imaging architectures which,
significantly reduces the computation time.
An Effective Semi-supervised Divisive Clustering Algorithm
Authors: Teng Qiu, Yong-Jie Li
Date: 2014-12-24 00:00:00
Tasks: Astronomy, Clustering

Nowadays, data are generated massively and rapidly from scientific fields as
bioinformatics, neuroscience and astronomy to business and engineering fields.
Cluster analysis, as one of the major data analysis tools, is therefore more
significant than ever. We propose in this work an effective Semi-supervised
Divisive Clustering algorithm (SDC). Data points are first organized by a
minimal spanning tree. Next, this tree structure is transitioned to the in-tree
structure, and then divided into sub-trees under the supervision of the labeled
data, and in the end, all points in the sub-trees are directly associated with
specific cluster centers. SDC is fully automatic, non-iterative, involving no
free parameter, insensitive to noise, able to detect irregularly shaped cluster
structures, applicable to the data sets of high dimensionality and different
attributes. The power of SDC is demonstrated on several datasets.
A Study on Clustering for Clustering Based Image De-Noising
Authors: Hossein Bakhshi Golestani, Mohsen Joneidi, Mostafa Sadeghi
Date: 2015-01-06 00:00:00
Tasks: Clustering, Dictionary Learning

In this paper, the problem of de-noising of an image contaminated with
Additive White Gaussian Noise (AWGN) is studied. This subject is an open
problem in signal processing for more than 50 years. Local methods suggested in
recent years, have obtained better results than global methods. However by more
intelligent training in such a way that first, important data is more effective
for training, second, clustering in such way that training blocks lie in
low-rank subspaces, we can design a dictionary applicable for image de-noising
and obtain results near the state of the art local methods. In the present
paper, we suggest a method based on global clustering of image constructing
blocks. As the type of clustering plays an important role in clustering-based
de-noising methods, we address two questions about the clustering. The first,
which parts of the data should be considered for clustering? and the second,
what data clustering method is suitable for de-noising.? Then clustering is
exploited to learn an over complete dictionary. By obtaining sparse
decomposition of the noisy image blocks in terms of the dictionary atoms, the
de-noised version is achieved. In addition to our framework, 7 popular
dictionary learning methods are simulated and compared. The results are
compared based on two major factors: (1) de-noising performance and (2)
execution time. Experimental results show that our dictionary learning
framework outperforms its competitors in terms of both factors.
A Novel Technique for Grading of Dates using Shape and Texture Features
Authors: S. H. Mohana, C. J. Prabhakar
Date: 2015-01-06 00:00:00

This paper presents a novel method to grade the date fruits based on the
combination of shape and texture features. The method begins with reducing the
specular reflection and small noise using a bilateral filter. Threshold based
segmentation is performed for background removal and fruit part selection from
the given image. Shape features is extracted using the contour of the date
fruit and texture features are extracted using Curvelet transform and Local
Binary Pattern (LBP) from the selected date fruit region. Finally, combinations
of shape and texture features are fused to grade the dates into six grades.
k-Nearest Neighbour(k-NN) classifier yields the best grading rate compared to
other two classifiers such as Support Vector Machine (SVM) and Linear
Discriminant(LDA) classifiers. The experiment result shows that our technique
achieves highest accuracy.
A Novel Design of a Parallel Machine Learnt Generational Garbage Collector
Authors: Vasanthakumar Soundararajan
Date: 2015-01-06 00:00:00
Tasks: Object

The Generational Garbage collection involves organizing the heap into
different divisions of memory space in-order to filter long-lived objects from
short-lived objects through moving the surviving object of each generation
Garbage Collection cycle to another memory space updating its age and
reclaiming space from the dead ones. The problem in this method is that the
longer an object is alive during its initial generations the longer the garbage
collector will have to deal with it by checking for its reachability from the
root and promoting it to other space divisions where as the ultimate goal of
the Garbage Collector is to reclaim memory from unreachable objects at a
minimal time possible. This paper is a proposal of a method where the lifetime
of every object getting into the heap will be predicted and will be placed in
heap accordingly for the garbage collector to deal more with reclaiming space
from dead objects and less in promoting the live ones to the higher level.
Stem-Calyx Recognition of an Apple using Shape Descriptors
Authors: S. H. Mohana, C. J. Prabhakar
Date: 2015-01-06 00:00:00
Tasks: Segmentation

This paper presents a novel method to recognize stem - calyx of an apple
using shape descriptors. The main drawback of existing apple grading techniques
is that stem - calyx part of an apple is treated as defects, this leads to poor
grading of apples. In order to overcome this drawback, we proposed an approach
to recognize stem-calyx and differentiated from true defects based on shape
features. Our method comprises of steps such as segmentation of apple using
grow-cut method, candidate objects such as stem-calyx and small defects are
detected using multi-threshold segmentation. The shape features are extracted
from detected objects using Multifractal, Fourier and Radon descriptor and
finally stem-calyx regions are recognized and differentiated from true defects
using SVM classifier. The proposed algorithm is evaluated using experiments
conducted on apple image dataset and results exhibit considerable improvement
in recognition of stem-calyx region compared to other techniques.
The Effect of Wedge Tip Angles on Stress Intensity Factors in the Contact Problem between Tilted Wedge and a Half Plane with an Edge Crack Using Digital Image Correlation
Authors: Seyedmeysam Khaleghian, Anahita Emami, Mohammad Yadegari, Nasser Soltani
Date: 2015-01-06 00:00:00

The first and second mode stress intensity factors (SIFs) of a contact
problem between a half-plane with an edge crack and an asymmetric tilted wedge
were obtained using experimental method of Digital Image Correlation (DIC). In
this technique, displacement and strain fields can be measured using two
digital images of the same sample at different stages of loading. However,
several images were taken consequently in each stage of this experiment to
avoid the noise effect. A pair of images of each stage was compared to each
other. Then, the correlation coefficients between them were studied using a
computer code. The pairs with the correlation coefficient higher than 0.8 were
selected as the acceptable match for displacement measurements near the crack
tip. Subsequently, the SIFs of specimens were calculated using displacement
fields obtained from DIC method. The effect of wedge tips angle on their SIFs
was also studied. Moreover, the results of DIC method were compared with the
results of photoelasticity method and a close agreement between them was
observed.
Skincure: An Innovative Smart Phone-Based Application To Assist In Melanoma Early Detection And Prevention
Authors: Omar Abuzaghleh, Miad Faezipour, Buket D. Barkana
Date: 2015-01-06 00:00:00
Tasks: General Classification, Lesion Segmentation, Prognosis

Melanoma spreads through metastasis, and therefore it has been proven to be
very fatal. Statistical evidence has revealed that the majority of deaths
resulting from skin cancer are as a result of melanoma. Further investigations
have shown that the survival rates in patients depend on the stage of the
infection; early detection and intervention of melanoma implicates higher
chances of cure. Clinical diagnosis and prognosis of melanoma is challenging
since the processes are prone to misdiagnosis and inaccuracies due to doctors
subjectivity. This paper proposes an innovative and fully functional
smart-phone based application to assist in melanoma early detection and
prevention. The application has two major components; the first component is a
real-time alert to help users prevent skin burn caused by sunlight; a novel
equation to compute the time for skin to burn is thereby introduced. The second
component is an automated image analysis module which contains image
acquisition, hair detection and exclusion, lesion segmentation, feature
extraction, and classification. The proposed system exploits PH2 Dermoscopy
image database from Pedro Hispano Hospital for development and testing
purposes. The image database contains a total of 200 dermoscopy images of
lesions, including normal, atypical, and melanoma cases. The experimental
results show that the proposed system is efficient, achieving classification of
the normal, atypical and melanoma images with accuracy of 96.3%, 95.7% and
97.5%, respectively.
Improved Graph Clustering
Authors: Yudong Chen, Sujay Sanghavi, Huan Xu
Date: 2012-10-11 00:00:00
Tasks: Clustering, Graph Clustering, Stochastic Block Model

Graph clustering involves the task of dividing nodes into clusters, so that
the edge density is higher within clusters as opposed to across clusters. A
natural, classic and popular statistical setting for evaluating solutions to
this problem is the stochastic block model, also referred to as the planted
partition model.
  In this paper we present a new algorithm--a convexified version of Maximum
Likelihood--for graph clustering. We show that, in the classic stochastic block
model setting, it outperforms existing methods by polynomial factors when the
cluster size is allowed to have general scalings. In fact, it is within
logarithmic factors of known lower bounds for spectral methods, and there is
evidence suggesting that no polynomial time algorithm would do significantly
better.
  We then show that this guarantee carries over to a more general extension of
the stochastic block model. Our method can handle the settings of semi-random
graphs, heterogeneous degree distributions, unequal cluster sizes, unaffiliated
nodes, partially observed graphs and planted clique/coloring etc. In
particular, our results provide the best exact recovery guarantees to date for
the planted partition, planted k-disjoint-cliques and planted noisy coloring
models with general cluster sizes; in other settings, we match the best
existing results up to logarithmic factors.
On Generalized Rectangular Fuzzy Model for Assessment
Authors: Igor Yakov Subbotin
Date: 2015-01-05 00:00:00
Tasks: model

The article is dedicated to the analysis of the existing models for
assessment based of the fuzzy logic centroid technique. A new Generalized
Rectangular Model were developed. Some generalizations of the existing models
are offered.
Declarative Statistical Modeling with Datalog
Authors: Vince Barany, Balder ten Cate, Benny Kimelfeld, Dan Olteanu, Zografoula Vagena
Date: 2014-12-06 00:00:00
Tasks: Probabilistic Programming

Formalisms for specifying statistical models, such as
probabilistic-programming languages, typically consist of two components: a
specification of a stochastic process (the prior), and a specification of
observations that restrict the probability space to a conditional subspace (the
posterior). Use cases of such formalisms include the development of algorithms
in machine learning and artificial intelligence. We propose and investigate a
declarative framework for specifying statistical models on top of a database,
through an appropriate extension of Datalog. By virtue of extending Datalog,
our framework offers a natural integration with the database, and has a robust
declarative semantics. Our Datalog extension provides convenient mechanisms to
include numerical probability functions; in particular, conclusions of rules
may contain values drawn from such functions. The semantics of a program is a
probability distribution over the possible outcomes of the input database with
respect to the program; these outcomes are minimal solutions with respect to a
related program with existentially quantified variables in conclusions.
Observations are naturally incorporated by means of integrity constraints over
the extensional and intensional relations. We focus on programs that use
discrete numerical distributions, but even then the space of possible outcomes
may be uncountable (as a solution can be infinite). We define a probability
measure over possible outcomes by applying the known concept of cylinder sets
to a probabilistic chase procedure. We show that the resulting semantics is
robust under different chases. We also identify conditions guaranteeing that
all possible outcomes are finite (and then the probability space is discrete).
We argue that the framework we propose retains the purely declarative nature of
Datalog, and allows for natural specifications of statistical models.
Adaptive Objectness for Object Tracking
Authors: Pengpeng Liang, Chunyuan Liao, Xue Mei, Haibin Ling
Date: 2015-01-05 00:00:00
Tasks: Object, Object Tracking, Visual Object Tracking, Visual Tracking

Object tracking is a long standing problem in vision. While great efforts
have been spent to improve tracking performance, a simple yet reliable prior
knowledge is left unexploited: the target object in tracking must be an object
other than non-object. The recently proposed and popularized objectness measure
provides a natural way to model such prior in visual tracking. Thus motivated,
in this paper we propose to adapt objectness for visual object tracking.
Instead of directly applying an existing objectness measure that is generic and
handles various objects and environments, we adapt it to be compatible to the
specific tracking sequence and object. More specifically, we use the newly
proposed BING objectness as the base, and then train an object-adaptive
objectness for each tracking task. The training is implemented by using an
adaptive support vector machine that integrates information from the specific
tracking target into the BING measure. We emphasize that the benefit of the
proposed adaptive objectness, named ADOBING, is generic. To show this, we
combine ADOBING with seven top performed trackers in recent evaluations. We run
the ADOBING-enhanced trackers with their base trackers on two popular
benchmarks, the CVPR2013 benchmark (50 sequences) and the Princeton Tracking
Benchmark (100 sequences). On both benchmarks, our methods not only
consistently improve the base trackers, but also achieve the best known
performances. Noting that the way we integrate objectness in visual tracking is
generic and straightforward, we expect even more improvement by using
tracker-specific objectness.
Likelihood Estimation with Incomplete Array Variate Observations
Authors: Deniz Akdemir
Date: 2012-09-12 00:00:00
Tasks: Imputation

Missing data is an important challenge when dealing with high dimensional
data arranged in the form of an array. In this paper, we propose methods for
estimation of the parameters of array variate normal probability model from
partially observed multiway data. The methods developed here are useful for
missing data imputation, estimation of mean and covariance parameters for
multiway data. A multiway semi-parametric mixed effects model that allows
separation of multiway covariance effects is also defined and an efficient
algorithm for estimation is recommended. We provide simulation results along
with real life data from genetics to demonstrate these methods.
Fast forward feature selection for the nonlinear classification of hyperspectral images
Authors: Mathieu Fauvel, Clement Dechesne, Anthony Zullo, Frédéric Ferraty
Date: 2015-01-05 00:00:00
Tasks: Classification, feature selection, General Classification, Hyperspectral Image Classification

A fast forward feature selection algorithm is presented in this paper. It is
based on a Gaussian mixture model (GMM) classifier. GMM are used for
classifying hyperspectral images. The algorithm selects iteratively spectral
features that maximizes an estimation of the classification rate. The
estimation is done using the k-fold cross validation. In order to perform fast
in terms of computing time, an efficient implementation is proposed. First, the
GMM can be updated when the estimation of the classification rate is computed,
rather than re-estimate the full model. Secondly, using marginalization of the
GMM, sub models can be directly obtained from the full model learned with all
the spectral features. Experimental results for two real hyperspectral data
sets show that the method performs very well in terms of classification
accuracy and processing time. Furthermore, the extracted model contains very
few spectral channels.
Neural Network Regularization via Robust Weight Factorization
Authors: Jan Rudy, Weiguang Ding, Daniel Jiwoong Im, Graham W. Taylor
Date: 2014-12-20 00:00:00

Regularization is essential when training large neural networks. As deep
neural networks can be mathematically interpreted as universal function
approximators, they are effective at memorizing sampling noise in the training
data. This results in poor generalization to unseen data. Therefore, it is no
surprise that a new regularization technique, Dropout, was partially
responsible for the now-ubiquitous winning entry to ImageNet 2012 by the
University of Toronto. Currently, Dropout (and related methods such as
DropConnect) are the most effective means of regularizing large neural
networks. These amount to efficiently visiting a large number of related models
at training time, while aggregating them to a single predictor at test time.
The proposed FaMe model aims to apply a similar strategy, yet learns a
factorization of each weight matrix such that the factors are robust to noise.
Chasing the Ghosts of Ibsen: A computational stylistic analysis of drama in translation
Authors: Gerard Lynch, Carl Vogel
Date: 2015-01-05 00:00:00
Tasks: Translation

Research into the stylistic properties of translations is an issue which has
received some attention in computational stylistics. Previous work by Rybicki
(2006) on the distinguishing of character idiolects in the work of Polish
author Henryk Sienkiewicz and two corresponding English translations using
Burrow's Delta method concluded that idiolectal differences could be observed
in the source texts and this variation was preserved to a large degree in both
translations. This study also found that the two translations were also highly
distinguishable from one another. Burrows (2002) examined English translations
of Juvenal also using the Delta method, results of this work suggest that some
translators are more adept at concealing their own style when translating the
works of another author whereas other authors tend to imprint their own style
to a greater extent on the work they translate. Our work examines the writing
of a single author, Norwegian playwright Henrik Ibsen, and these writings
translated into both German and English from Norwegian, in an attempt to
investigate the preservation of characterization, defined here as the
distinctiveness of textual contributions of characters.
Inverse Renormalization Group Transformation in Bayesian Image Segmentations
Authors: Kazuyuki Tanaka, Shun Kataoka, Muneki Yasuda, Masayuki Ohzeki
Date: 2015-01-05 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

A new Bayesian image segmentation algorithm is proposed by combining a loopy
belief propagation with an inverse real space renormalization group
transformation to reduce the computational time. In results of our experiment,
we observe that the proposed method can reduce the computational time to less
than one-tenth of that taken by conventional Bayesian approaches.
Group $K$-Means
Authors: Jianfeng Wang, Shuicheng Yan, Yi Yang, Mohan S. Kankanhalli, Shipeng Li, Jingdong Wang
Date: 2015-01-05 00:00:00

We study how to learn multiple dictionaries from a dataset, and approximate
any data point by the sum of the codewords each chosen from the corresponding
dictionary. Although theoretically low approximation errors can be achieved by
the global solution, an effective solution has not been well studied in
practice. To solve the problem, we propose a simple yet effective algorithm
\textit{Group $K$-Means}. Specifically, we take each dictionary, or any two
selected dictionaries, as a group of $K$-means cluster centers, and then deal
with the approximation issue by minimizing the approximation errors. Besides,
we propose a hierarchical initialization for such a non-convex problem.
Experimental results well validate the effectiveness of the approach.