pwc-archive/papers-with-abstracts huggingface dataset:
Variational Image Segmentation Model Coupled with Image Restoration Achievements
Authors: Xiaohao Cai
Date: 2014-05-09 00:00:00
Tasks: Image Restoration, Image Segmentation, Segmentation, Semantic Segmentation

Image segmentation and image restoration are two important topics in image
processing with great achievements. In this paper, we propose a new multiphase
segmentation model by combining image restoration and image segmentation
models. Utilizing image restoration aspects, the proposed segmentation model
can effectively and robustly tackle high noisy images, blurry images, images
with missing pixels, and vector-valued images. In particular, one of the most
important segmentation models, the piecewise constant Mumford-Shah model, can
be extended easily in this way to segment gray and vector-valued images
corrupted for example by noise, blur or missing pixels after coupling a new
data fidelity term which comes from image restoration topics. It can be solved
efficiently using the alternating minimization algorithm, and we prove the
convergence of this algorithm with three variables under mild condition.
Experiments on many synthetic and real-world images demonstrate that our method
gives better segmentation results in comparison to others state-of-the-art
segmentation models especially for blurry images and images with missing pixels
values.
Highly comparative feature-based time-series classification
Authors: Ben D. Fulcher, Nick S. Jones
Date: 2014-01-15 00:00:00
Tasks: Classification, Dimensionality Reduction, Dynamic Time Warping, feature selection, General Classification, Time Series, Time Series Analysis, Time Series Classification

A highly comparative, feature-based approach to time series classification is
introduced that uses an extensive database of algorithms to extract thousands
of interpretable features from time series. These features are derived from
across the scientific time-series analysis literature, and include summaries of
time series in terms of their correlation structure, distribution, entropy,
stationarity, scaling properties, and fits to a range of time-series models.
After computing thousands of features for each time series in a training set,
those that are most informative of the class structure are selected using
greedy forward feature selection with a linear classifier. The resulting
feature-based classifiers automatically learn the differences between classes
using a reduced number of time-series properties, and circumvent the need to
calculate distances between time series. Representing time series in this way
results in orders of magnitude of dimensionality reduction, allowing the method
to perform well on very large datasets containing long time series or time
series of different lengths. For many of the datasets studied, classification
performance exceeded that of conventional instance-based classifiers, including
one nearest neighbor classifiers using Euclidean distances and dynamic time
warping and, most importantly, the features selected provide an understanding
of the properties of the dataset, insight that can guide further scientific
investigation.
Improving Image Clustering using Sparse Text and the Wisdom of the Crowds
Authors: Anna Ma, Arjuna Flenner, Deanna Needell, Allon G. Percus
Date: 2014-05-08 00:00:00
Tasks: Clustering, Image Clustering

We propose a method to improve image clustering using sparse text and the
wisdom of the crowds. In particular, we present a method to fuse two different
kinds of document features, image and text features, and use a common
dictionary or "wisdom of the crowds" as the connection between the two
different kinds of documents. With the proposed fusion matrix, we use topic
modeling via non-negative matrix factorization to cluster documents.
A consistent deterministic regression tree for non-parametric prediction of time series
Authors: Pierre Gaillard, Paul Baudin
Date: 2014-05-07 00:00:00
Tasks: Prediction, regression, Time Series, Time Series Analysis

We study online prediction of bounded stationary ergodic processes. To do so,
we consider the setting of prediction of individual sequences and build a
deterministic regression tree that performs asymptotically as well as the best
L-Lipschitz constant predictors. Then, we show why the obtained regret bound
entails the asymptotical optimality with respect to the class of bounded
stationary ergodic processes.
Privately Solving Linear Programs
Authors: Justin Hsu, Aaron Roth, Tim Roughgarden, Jonathan Ullman
Date: 2014-02-15 00:00:00

In this paper, we initiate the systematic study of solving linear programs
under differential privacy. The first step is simply to define the problem: to
this end, we introduce several natural classes of private linear programs that
capture different ways sensitive data can be incorporated into a linear
program. For each class of linear programs we give an efficient, differentially
private solver based on the multiplicative weights framework, or we give an
impossibility result.
Joint Tabling of Logic Program Abductions and Updates
Authors: Ari Saptawijaya, Luís Moniz Pereira
Date: 2014-05-08 00:00:00
Tasks: Decision Making

Abductive logic programs offer a formalism to declaratively represent and
reason about problems in a variety of areas: diagnosis, decision making,
hypothetical reasoning, etc. On the other hand, logic program updates allow us
to express knowledge changes, be they internal (or self) and external (or
world) changes. Abductive logic programs and logic program updates thus
naturally coexist in problems that are susceptible to hypothetical reasoning
about change. Taking this as a motivation, in this paper we integrate abductive
logic programs and logic program updates by jointly exploiting tabling features
of logic programming. The integration is based on and benefits from the two
implementation techniques we separately devised previously, viz., tabled
abduction and incremental tabling for query-driven propagation of logic program
updates. A prototype of the integrated system is implemented in XSB Prolog.
Reproducing kernel Hilbert space based estimation of systems of ordinary differential equations
Authors: Javier González, Ivan Vujačić, Ernst Wit
Date: 2013-11-14 00:00:00

Non-linear systems of differential equations have attracted the interest in
fields like system biology, ecology or biochemistry, due to their flexibility
and their ability to describe dynamical systems. Despite the importance of such
models in many branches of science they have not been the focus of systematic
statistical analysis until recently. In this work we propose a general approach
to estimate the parameters of systems of differential equations measured with
noise. Our methodology is based on the maximization of the penalized likelihood
where the system of differential equations is used as a penalty. To do so, we
use a Reproducing Kernel Hilbert Space approach that allows to formulate the
estimation problem as an unconstrained numeric maximization problem easy to
solve. The proposed method is tested with synthetically simulated data and it
is used to estimate the unobserved transcription factor CdaR in Steptomyes
coelicolor using gene expression data of the genes it regulates.
Image Resolution and Contrast Enhancement of Satellite Geographical Images with Removal of Noise using Wavelet Transforms
Authors: Prajakta P. Khairnar, C. A. Manjare
Date: 2014-05-08 00:00:00

In this paper the technique for resolution and contrast enhancement of
satellite geographical images based on discrete wavelet transform (DWT),
stationary wavelet transform (SWT) and singular value decomposition (SVD) has
been proposed. In this, the noise is added in the input low resolution and low
contrast image. The median filter is used remove noise from the input image.
This low resolution, low contrast image without noise is decomposed into four
sub-bands by using DWT and SWT. The resolution enhancement technique is based
on the interpolation of high frequency components obtained by DWT and input
image. SWT is used to enhance input image. DWT is used to decompose an image
into four frequency sub bands and these four sub-bands are interpolated using
bicubic interpolation technique. All these sub-bands are reconstructed as high
resolution image by using inverse DWT (IDWT). To increase the contrast the
proposed technique uses DWT and SVD. GHE is used to equalize an image. The
equalized image is decomposed into four sub-bands using DWT and new LL sub-band
is reconstructed using SVD. All sub-bands are reconstructed using IDWT to
generate high resolution and contrast image over conventional techniques. The
experimental result shows superiority of the proposed technique over
conventional techniques.
  Key words: Discrete wavelet transform (DWT), General histogram equalization
(GHE), Median filter, Singular value decomposition (SVD), Stationary wavelet
transform (SWT).
An Expert System for Automatic Reading of A Text Written in Standard Arabic
Authors: Tebbi Hanane, Azzoune Hamid
Date: 2014-05-08 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech

In this work we present our expert system of Automatic reading or speech
synthesis based on a text written in Standard Arabic, our work is carried out
in two great stages: the creation of the sound data base, and the
transformation of the written text into speech (Text To Speech TTS). This
transformation is done firstly by a Phonetic Orthographical Transcription (POT)
of any written Standard Arabic text with the aim of transforming it into his
corresponding phonetics sequence, and secondly by the generation of the voice
signal which corresponds to the chain transcribed. We spread out the different
of conception of the system, as well as the results obtained compared to others
works studied to realize TTS based on Standard Arabic.
A Computational Theory of Subjective Probability
Authors: Phil Maguire, Philippe Moser, Rebecca Maguire, Mark Keane
Date: 2014-05-08 00:00:00

In this article we demonstrate how algorithmic probability theory is applied
to situations that involve uncertainty. When people are unsure of their model
of reality, then the outcome they observe will cause them to update their
beliefs. We argue that classical probability cannot be applied in such cases,
and that subjective probability must instead be used. In Experiment 1 we show
that, when judging the probability of lottery number sequences, people apply
subjective rather than classical probability. In Experiment 2 we examine the
conjunction fallacy and demonstrate that the materials used by Tversky and
Kahneman (1983) involve model uncertainty. We then provide a formal
mathematical proof that, for every uncertain model, there exists a conjunction
of outcomes which is more subjectively probable than either of its constituents
in isolation.
Dialogues for proof search
Authors: Jesse Alama
Date: 2014-05-08 00:00:00

Dialogue games are a two-player semantics for a variety of logics, including
intuitionistic and classical logic. Dialogues can be viewed as a kind of
analytic calculus not unlike tableaux. Can dialogue games be an effective
foundation for proof search in intuitionistic logic (both first-order and
propositional)? We announce Kuno, an automated theorem prover for
intuitionistic first-order logic based on dialogue games.
A Vague Improved Markov Model Approach for Web Page Prediction
Authors: Priya Bajaj, Supriya Raheja
Date: 2014-05-08 00:00:00
Tasks: Prediction

Today most of the information in all areas is available over the web. It
increases the web utilization as well as attracts the interest of researchers
to improve the effectiveness of web access and web utilization. As the number
of web clients gets increased, the bandwidth sharing is performed that
decreases the web access efficiency. Web page prefetching improves the
effectiveness of web access by availing the next required web page before the
user demand. It is an intelligent predictive mining that analyze the user web
access history and predict the next page. In this work, vague improved markov
model is presented to perform the prediction. In this work, vague rules are
suggested to perform the pruning at different levels of markov model. Once the
prediction table is generated, the association mining will be implemented to
identify the most effective next page. In this paper, an integrated model is
suggested to improve the prediction accuracy and effectiveness.
Integrating Vague Association Mining with Markov Model
Authors: Priya Bajaj, Supriya Raheja
Date: 2014-05-08 00:00:00
Tasks: Clustering, model

The increasing demand of world wide web raises the need of predicting the
user's web page request.The most widely used approach to predict the web pages
is the pattern discovery process of Web usage mining. This process involves
inevitability of many techniques like Markov model, association rules and
clustering. Fuzzy theory with different techniques has been introduced for the
better results. Our focus is on Markov models. This paper is introducing the
vague Rules with Markov models for more accuracy using the vague set theory.
Implementation And Performance Evaluation Of Background Subtraction Algorithms
Authors: Deepjoy Das, Dr. Sarat Saharia
Date: 2014-05-08 00:00:00

The study evaluates three background subtraction techniques. The techniques
ranges from very basic algorithm to state of the art published techniques
categorized based on speed, memory requirements and accuracy. Such a review can
effectively guide the designer to select the most suitable method for a given
application in a principled way. The algorithms used in the study ranges from
varying levels of accuracy and computational complexity. Few of them can also
deal with real time challenges like rain, snow, hails, swaying branches,
objects overlapping, varying light intensity or slow moving objects.
Geodesic Distance Function Learning via Heat Flow on Vector Fields
Authors: Binbin Lin, Ji Yang, Xiaofei He, Jieping Ye
Date: 2014-05-01 00:00:00

Learning a distance function or metric on a given data manifold is of great
importance in machine learning and pattern recognition. Many of the previous
works first embed the manifold to Euclidean space and then learn the distance
function. However, such a scheme might not faithfully preserve the distance
function if the original manifold is not Euclidean. Note that the distance
function on a manifold can always be well-defined. In this paper, we propose to
learn the distance function directly on the manifold without embedding. We
first provide a theoretical characterization of the distance function by its
gradient field. Based on our theoretical analysis, we propose to first learn
the gradient field of the distance function and then learn the distance
function itself. Specifically, we set the gradient field of a local distance
function as an initial vector field. Then we transport it to the whole manifold
via heat flow on vector fields. Finally, the geodesic distance function can be
obtained by requiring its gradient field to be close to the normalized vector
field. Experimental results on both synthetic and real data demonstrate the
effectiveness of our proposed algorithm.
Sparse PCA through Low-rank Approximations
Authors: Dimitris S. Papailiopoulos, Alexandros G. Dimakis, Stavros Korokythakis
Date: 2013-03-03 00:00:00

We introduce a novel algorithm that computes the $k$-sparse principal
component of a positive semidefinite matrix $A$. Our algorithm is combinatorial
and operates by examining a discrete set of special vectors lying in a
low-dimensional eigen-subspace of $A$. We obtain provable approximation
guarantees that depend on the spectral decay profile of the matrix: the faster
the eigenvalue decay, the better the quality of our approximation. For example,
if the eigenvalues of $A$ follow a power-law decay, we obtain a polynomial-time
approximation algorithm for any desired accuracy.
  A key algorithmic component of our scheme is a combinatorial feature
elimination step that is provably safe and in practice significantly reduces
the running complexity of our algorithm. We implement our algorithm and test it
on multiple artificial and real data sets. Due to the feature elimination step,
it is possible to perform sparse PCA on data sets consisting of millions of
entries in a few minutes. Our experimental evaluation shows that our scheme is
nearly optimal while finding very sparse vectors. We compare to the prior state
of the art and show that our scheme matches or outperforms previous algorithms
in all tested data sets.
On Tensor Completion via Nuclear Norm Minimization
Authors: Ming Yuan, Cun-Hui Zhang
Date: 2014-05-07 00:00:00
Tasks: Matrix Completion

Many problems can be formulated as recovering a low-rank tensor. Although an
increasingly common task, tensor recovery remains a challenging problem because
of the delicacy associated with the decomposition of higher order tensors. To
overcome these difficulties, existing approaches often proceed by unfolding
tensors into matrices and then apply techniques for matrix completion. We show
here that such matricization fails to exploit the tensor structure and may lead
to suboptimal procedure. More specifically, we investigate a convex
optimization approach to tensor completion by directly minimizing a tensor
nuclear norm and prove that this leads to an improved sample size requirement.
To establish our results, we develop a series of algebraic and probabilistic
techniques such as characterization of subdifferetial for tensor nuclear norm
and concentration inequalities for tensor martingales, which may be of
independent interests and could be useful in other tensor related problems.
Learning Alternative Name Spellings
Authors: Jeffrey Sukharev, Leonid Zhukov, Alexandrin Popescul
Date: 2014-05-07 00:00:00
Tasks: Entity Resolution, Machine Translation, Retrieval, Translation

Name matching is a key component of systems for entity resolution or record
linkage. Alternative spellings of the same names are a com- mon occurrence in
many applications. We use the largest collection of genealogy person records in
the world together with user search query logs to build name matching models.
The procedure for building a crowd-sourced training set is outlined together
with the presentation of our method. We cast the problem of learning
alternative spellings as a machine translation problem at the character level.
We use in- formation retrieval evaluation methodology to show that this method
substantially outperforms on our data a number of standard well known phonetic
and string similarity methods in terms of precision and re- call. Additionally,
we rigorously compare the performance of standard methods when compared with
each other. Our result can lead to a significant practical impact in entity
resolution applications.
Entropy Based Cartoon Texture Separation
Authors: Kutlu Emre Yilmaz
Date: 2014-05-07 00:00:00
Tasks: Image Compression, Image Inpainting, Image Segmentation, Semantic Segmentation

Separating an image into cartoon and texture components comes useful in image
processing applications, such as image compression, image segmentation, image
inpainting. Yves Meyer's influential cartoon texture decomposition model
involves deriving an energy functional by choosing appropriate spaces and
functionals. Minimizers of the derived energy functional are cartoon and
texture components of an image. In this study, cartoon part of an image is
separated, by reconstructing it from pixels of multi scale Total-Variation
filtered versions of the original image which is sought to be decomposed into
cartoon and texture parts. An information theoretic pixel by pixel selection
criteria is employed to choose the contributing pixels and their scales.
DepecheMood: a Lexicon for Emotion Analysis from Crowd-Annotated News
Authors: Jacopo Staiano, Marco Guerini
Date: 2014-05-07 00:00:00
Tasks: Articles, Emotion Recognition, General Classification, regression, Sentiment Analysis

While many lexica annotated with words polarity are available for sentiment
analysis, very few tackle the harder task of emotion analysis and are usually
quite limited in coverage. In this paper, we present a novel approach for
extracting - in a totally automated way - a high-coverage and high-precision
lexicon of roughly 37 thousand terms annotated with emotion scores, called
DepecheMood. Our approach exploits in an original way 'crowd-sourced' affective
annotation implicitly provided by readers of news articles from rappler.com. By
providing new state-of-the-art performances in unsupervised settings for
regression and classification tasks, even using a na\"{\i}ve approach, our
experiments show the beneficial impact of harvesting social media data for
affective lexicon building.
PAC-Bayes Mini-tutorial: A Continuous Union Bound
Authors: Tim van Erven
Date: 2014-05-07 00:00:00
Tasks: BIG-bench Machine Learning, Relation

When I first encountered PAC-Bayesian concentration inequalities they seemed
to me to be rather disconnected from good old-fashioned results like
Hoeffding's and Bernstein's inequalities. But, at least for one flavour of the
PAC-Bayesian bounds, there is actually a very close relation, and the main
innovation is a continuous version of the union bound, along with some
ingenious applications. Here's the gist of what's going on, presented from a
machine learning perspective.
Learning Boolean Halfspaces with Small Weights from Membership Queries
Authors: Hasan Abasi, Ali Z. Abdi, Nader H. Bshouty
Date: 2014-05-07 00:00:00

We consider the problem of proper learning a Boolean Halfspace with integer
weights $\{0,1,\ldots,t\}$ from membership queries only. The best known
algorithm for this problem is an adaptive algorithm that asks $n^{O(t^5)}$
membership queries where the best lower bound for the number of membership
queries is $n^t$ [Learning Threshold Functions with Small Weights Using
Membership Queries. COLT 1999]
  In this paper we close this gap and give an adaptive proper learning
algorithm with two rounds that asks $n^{O(t)}$ membership queries. We also give
a non-adaptive proper learning algorithm that asks $n^{O(t^3)}$ membership
queries.
An expert system for recommending suitable ornamental fish addition to an aquarium based on aquarium condition
Authors: Mohammad Mohammadi, Shahram Jafari
Date: 2014-05-07 00:00:00

Expert systems prove to be suitable replacement for human experts when human
experts are unavailable for different reasons. Various expert system has been
developed for wide range of application. Although some expert systems in the
field of fishery and aquaculture has been developed but a system that aids user
in process of selecting a new addition to their aquarium tank never been
designed. This paper proposed an expert system that suggests new addition to an
aquarium tank based on current environmental condition of aquarium and
currently existing fishes in aquarium. The system suggest the best fit for
aquarium condition and most compatible to other fishes in aquarium.