pwc-archive/papers-with-abstracts huggingface dataset:
Binary Classifier Calibration: Bayesian Non-Parametric Approach
Authors: Mahdi Pakdaman Naeini, Gregory F. Cooper, Milos Hauskrecht
Date: 2014-01-13 00:00:00
Tasks: BIG-bench Machine Learning, Binary Classification, Classifier calibration

A set of probabilistic predictions is well calibrated if the events that are
predicted to occur with probability p do in fact occur about p fraction of the
time. Well calibrated predictions are particularly important when machine
learning models are used in decision analysis. This paper presents two new
non-parametric methods for calibrating outputs of binary classification models:
a method based on the Bayes optimal selection and a method based on the
Bayesian model averaging. The advantage of these methods is that they are
independent of the algorithm used to learn a predictive model, and they can be
applied in a post-processing step, after the model is learned. This makes them
applicable to a wide variety of machine learning models and methods. These
calibration methods, as well as other methods, are tested on a variety of
datasets in terms of both discrimination and calibration performance. The
results show the methods either outperform or are comparable in performance to
the state-of-the-art calibration methods.
ONTS: "Optima" News Translation System
Authors: Marco Turchi, Martin Atkinson, Alastair Wilcox, Brett Crawley, Stefano Bucci, Ralf Steinberger, Erik van der Goot
Date: 2014-01-13 00:00:00
Proceeding: EACL 2012 4
Tasks: Articles, Machine Translation, Translation

We propose a real-time machine translation system that allows users to select
a news category and to translate the related live news articles from Arabic,
Czech, Danish, Farsi, French, German, Italian, Polish, Portuguese, Spanish and
Turkish into English. The Moses-based system was optimised for the news domain
and differs from other available systems in four ways: (1) News items are
automatically categorised on the source side, before translation; (2) Named
entity translation is optimised by recognising and extracting them on the
source side and by re-inserting their translation in the target language,
making use of a separate entity repository; (3) News titles are translated with
a separate translation system which is optimised for the specific style of news
titles; (4) The system was optimised for speed in order to cope with the large
volume of daily news articles.
A survey of methods to ease the development of highly multilingual text mining applications
Authors: Ralf Steinberger
Date: 2014-01-13 00:00:00
Tasks: Articles

Multilingual text processing is useful because the information content found
in different languages is complementary, both regarding facts and opinions.
While Information Extraction and other text mining software can, in principle,
be developed for many languages, most text analysis tools have only been
applied to small sets of languages because the development effort per language
is large. Self-training tools obviously alleviate the problem, but even the
effort of providing training data and of manually tuning the results is usually
considerable. In this paper, we gather insights by various multilingual system
developers on how to minimise the effort of developing natural language
processing applications for many languages. We also explain the main guidelines
underlying our own effort to develop complex text mining software for tens of
languages. While these guidelines - most of all: extreme simplicity - can be
very restrictive and limiting, we believe to have shown the feasibility of the
approach through the development of the Europe Media Monitor (EMM) family of
applications (http://emm.newsbrief.eu/overview.html). EMM is a set of complex
media monitoring tools that process and analyse up to 100,000 online news
articles per day in between twenty and fifty languages. We will also touch upon
the kind of language resources that would make it easier for all to develop
highly multilingual text mining applications. We will argue that - to achieve
this - the most needed resources would be freely available, simple, parallel
and uniform multilingual dictionaries, corpora and software tools.
A Solution of Degree Constrained Spanning Tree Using Hybrid GA
Authors: Sounak Sadhukhan, Samar Sen Sarma
Date: 2014-01-08 00:00:00

In real life, it is always an urge to reach our goal in minimum effort i.e.,
it should have a minimum constrained path. The path may be shortest route in
practical life, either physical or electronic medium. The scenario is to
represents the ambiance as a graph and to find a spanning tree with custom
design criteria. Here, we have chosen a minimum degree spanning tree, which can
be generated in real time with minimum turnaround time. The problem is
NP-complete in nature [1, 2]. The solution approach, in general, is
approximate. We have used a heuristic approach, namely hybrid genetic algorithm
(GA), with motivated criteria of encoded data structures of graph. We compare
the experimental result with the existing approximate algorithm and the result
is so encouraging that we are interested to use it in our future applications.
Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models
Authors: Tiago P. Peixoto
Date: 2013-10-16 00:00:00

We present an efficient algorithm for the inference of stochastic block
models in large networks. The algorithm can be used as an optimized Markov
chain Monte Carlo (MCMC) method, with a fast mixing time and a much reduced
susceptibility to getting trapped in metastable states, or as a greedy
agglomerative heuristic, with an almost linear $O(N\ln^2N)$ complexity, where
$N$ is the number of nodes in the network, independent on the number of blocks
being inferred. We show that the heuristic is capable of delivering results
which are indistinguishable from the more exact and numerically expensive MCMC
method in many artificial and empirical networks, despite being much faster.
The method is entirely unbiased towards any specific mixing pattern, and in
particular it does not favor assortative community structures.
Tensor Representation and Manifold Learning Methods for Remote Sensing Images
Authors: Lefei Zhang
Date: 2014-01-13 00:00:00
Tasks: Earth Observation, Sparse Learning, Transfer Learning

One of the main purposes of earth observation is to extract interested
information and knowledge from remote sensing (RS) images with high efficiency
and accuracy. However, with the development of RS technologies, RS system
provide images with higher spatial and temporal resolution and more spectral
channels than before, and it is inefficient and almost impossible to manually
interpret these images. Thus, it is of great interests to explore automatic and
intelligent algorithms to quickly process such massive RS data with high
accuracy. This thesis targets to develop some efficient information extraction
algorithms for RS images, by relying on the advanced technologies in machine
learning. More precisely, we adopt the manifold learning algorithms as the
mainline and unify the regularization theory, tensor-based method, sparse
learning and transfer learning into the same framework. The main contributions
of this thesis are as follows.
lp-Recovery of the Most Significant Subspace among Multiple Subspaces with Outliers
Authors: Gilad Lerman, Teng Zhang
Date: 2010-12-18 00:00:00

We assume data sampled from a mixture of d-dimensional linear subspaces with
spherically symmetric distributions within each subspace and an additional
outlier component with spherically symmetric distribution within the ambient
space (for simplicity we may assume that all distributions are uniform on their
corresponding unit spheres). We also assume mixture weights for the different
components. We say that one of the underlying subspaces of the model is most
significant if its mixture weight is higher than the sum of the mixture weights
of all other subspaces. We study the recovery of the most significant subspace
by minimizing the lp-averaged distances of data points from d-dimensional
subspaces, where p>0. Unlike other lp minimization problems, this minimization
is non-convex for all p>0 and thus requires different methods for its analysis.
We show that if 0<p<=1, then for any fraction of outliers the most significant
subspace can be recovered by lp minimization with overwhelming probability
(which depends on the generating distribution and its parameters). We show that
when adding small noise around the underlying subspaces the most significant
subspace can be nearly recovered by lp minimization for any 0<p<=1 with an
error proportional to the noise level. On the other hand, if p>1 and there is
more than one underlying subspace, then with overwhelming probability the most
significant subspace cannot be recovered or nearly recovered. This last result
does not require spherically symmetric outliers.
GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation
Authors: Edward Meeds, Max Welling
Date: 2014-01-13 00:00:00

Scientists often express their understanding of the world through a
computationally demanding simulation program. Analyzing the posterior
distribution of the parameters given observations (the inverse problem) can be
extremely challenging. The Approximate Bayesian Computation (ABC) framework is
the standard statistical tool to handle these likelihood free problems, but
they require a very large number of simulations. In this work we develop two
new ABC sampling algorithms that significantly reduce the number of simulations
necessary for posterior inference. Both algorithms use confidence estimates for
the accept probability in the Metropolis Hastings step to adaptively choose the
number of necessary simulations. Our GPS-ABC algorithm stores the information
obtained from every simulation in a Gaussian process which acts as a surrogate
function for the simulated statistics. Experiments on a challenging realistic
biological problem illustrate the potential of these algorithms.
Insights into analysis operator learning: From patch-based sparse models to higher-order MRFs
Authors: Yunjin Chen, René Ranftl, Thomas Pock
Date: 2014-01-13 00:00:00
Tasks: Denoising, Image Denoising, Image Restoration, Operator learning

This paper addresses a new learning algorithm for the recently introduced
co-sparse analysis model. First, we give new insights into the co-sparse
analysis model by establishing connections to filter-based MRF models, such as
the Field of Experts (FoE) model of Roth and Black. For training, we introduce
a technique called bi-level optimization to learn the analysis operators.
Compared to existing analysis operator learning approaches, our training
procedure has the advantage that it is unconstrained with respect to the
analysis operator. We investigate the effect of different aspects of the
co-sparse analysis model and show that the sparsity promoting function (also
called penalty function) is the most important factor in the model. In order to
demonstrate the effectiveness of our training approach, we apply our trained
models to various classical image restoration problems. Numerical experiments
show that our trained models clearly outperform existing analysis operator
learning approaches and are on par with state-of-the-art image denoising
algorithms. Our approach develops a framework that is intuitive to understand
and easy to implement.
A Low-Dimensional Representation for Robust Partial Isometric Correspondences Computation
Authors: Alan Brunton, Michael Wand, Stefanie Wuhrer, Hans-Peter Seidel, Tino Weinkauf
Date: 2013-08-30 00:00:00

Intrinsic isometric shape matching has become the standard approach for pose
invariant correspondence estimation among deformable shapes. Most existing
approaches assume global consistency, i.e., the metric structure of the whole
manifold must not change significantly. While global isometric matching is well
understood, only a few heuristic solutions are known for partial matching.
Partial matching is particularly important for robustness to topological noise
(incomplete data and contacts), which is a common problem in real-world 3D
scanner data. In this paper, we introduce a new approach to partial, intrinsic
isometric matching. Our method is based on the observation that isometries are
fully determined by purely local information: a map of a single point and its
tangent space fixes an isometry for both global and the partial maps. From this
idea, we develop a new representation for partial isometric maps based on
equivalence classes of correspondences between pairs of points and their
tangent spaces. From this, we derive a local propagation algorithm that find
such mappings efficiently. In contrast to previous heuristics based on RANSAC
or expectation maximization, our method is based on a simple and sound
theoretical model and fully deterministic. We apply our approach to register
partial point clouds and compare it to the state-of-the-art methods, where we
obtain significant improvements over global methods for real-world data and
stronger guarantees than previous heuristic partial matching algorithms.
A variational Bayes framework for sparse adaptive estimation
Authors: Konstantinos E. Themelis, Athanasios A. Rontogiannis, Konstantinos D. Koutroumbas
Date: 2014-01-13 00:00:00

Recently, a number of mostly $\ell_1$-norm regularized least squares type
deterministic algorithms have been proposed to address the problem of
\emph{sparse} adaptive signal estimation and system identification. From a
Bayesian perspective, this task is equivalent to maximum a posteriori
probability estimation under a sparsity promoting heavy-tailed prior for the
parameters of interest. Following a different approach, this paper develops a
unifying framework of sparse \emph{variational Bayes} algorithms that employ
heavy-tailed priors in conjugate hierarchical form to facilitate posterior
inference. The resulting fully automated variational schemes are first
presented in a batch iterative form. Then it is shown that by properly
exploiting the structure of the batch estimation task, new sparse adaptive
variational Bayes algorithms can be derived, which have the ability to impose
and track sparsity during real-time processing in a time-varying environment.
The most important feature of the proposed algorithms is that they completely
eliminate the need for computationally costly parameter fine-tuning, a
necessary ingredient of sparse adaptive deterministic algorithms. Extensive
simulation results are provided to demonstrate the effectiveness of the new
sparse variational Bayes algorithms against state-of-the-art deterministic
techniques for adaptive channel estimation. The results show that the proposed
algorithms are numerically robust and exhibit in general superior estimation
performance compared to their deterministic counterparts.
PSMACA: An Automated Protein Structure Prediction Using MACA (Multiple Attractor Cellular Automata)
Authors: Pokkuluri Kiran Sree, Inamupudi Ramesh Babu, SSSN Usha Devi N
Date: 2014-01-13 00:00:00
Tasks: feature selection, Protein Structure Prediction

Protein Structure Predication from sequences of amino acid has gained a
remarkable attention in recent years. Even though there are some prediction
techniques addressing this problem, the approximate accuracy in predicting the
protein structure is closely 75%. An automated procedure was evolved with MACA
(Multiple Attractor Cellular Automata) for predicting the structure of the
protein. Most of the existing approaches are sequential which will classify the
input into four major classes and these are designed for similar sequences.
PSMACA is designed to identify ten classes from the sequences that share
twilight zone similarity and identity with the training sequences. This method
also predicts three states (helix, strand, and coil) for the structure. Our
comprehensive design considers 10 feature selection methods and 4 classifiers
to develop MACA (Multiple Attractor Cellular Automata) based classifiers that
are build for each of the ten classes. We have tested the proposed classifier
with twilight-zone and 1-high-similarity benchmark datasets with over three
dozens of modern competing predictors shows that PSMACA provides the best
overall accuracy that ranges between 77% and 88.7% depending on the dataset.
A parameterless scale-space approach to find meaningful modes in histograms - Application to image and spectrum segmentation
Authors: Jérôme Gilles, Kathryn Heal
Date: 2014-01-13 00:00:00
Tasks: Clustering, Image Segmentation, Segmentation, Semantic Segmentation

In this paper, we present an algorithm to automatically detect meaningful
modes in a histogram. The proposed method is based on the behavior of local
minima in a scale-space representation. We show that the detection of such
meaningful modes is equivalent in a two classes clustering problem on the
length of minima scale-space curves. The algorithm is easy to implement, fast,
and does not require any parameters. We present several results on histogram
and spectrum segmentation, grayscale image segmentation and color image
reduction.
Dictionary-Based Concept Mining: An Application for Turkish
Authors: Cem Rıfkı Aydın, Ali Erkan, Tunga Güngör, Hidayet Takçı
Date: 2014-01-12 00:00:00

In this study, a dictionary-based method is used to extract expressive
concepts from documents. So far, there have been many studies concerning
concept mining in English, but this area of study for Turkish, an agglutinative
language, is still immature. We used dictionary instead of WordNet, a lexical
database grouping words into synsets that is widely used for concept
extraction. The dictionaries are rarely used in the domain of concept mining,
but taking into account that dictionary entries have synonyms, hypernyms,
hyponyms and other relationships in their meaning texts, the success rate has
been high for determining concepts. This concept extraction method is
implemented on documents, that are collected from different corpora.
The Missing Ones: Key Ingredients Towards Effective Ambient Assisted Living Systems
Authors: Hong Sun, Vincenzo De Florio, Ning Gui, Chris Blondia
Date: 2014-01-12 00:00:00

The population of elderly people keeps increasing rapidly, which becomes a
predominant aspect of our societies. As such, solutions both efficacious and
cost-effective need to be sought. Ambient Assisted Living (AAL) is a new
approach which promises to address the needs from elderly people. In this
paper, we claim that human participation is a key ingredient towards effective
AAL systems, which not only saves social resources, but also has positive
relapses on the psychological health of the elderly people. Challenges in
increasing the human participation in ambient assisted living are discussed in
this paper and solutions to meet those challenges are also proposed. We use our
proposed mutual assistance community, which is built with service oriented
approach, as an example to demonstrate how to integrate human tasks in AAL
systems. Our preliminary simulation results are presented, which support the
effectiveness of human participation.
An Overview of Schema Theory
Authors: David White
Date: 2014-01-12 00:00:00
Tasks: Philosophy

The purpose of this paper is to give an introduction to the field of Schema
Theory written by a mathematician and for mathematicians. In particular, we
endeavor to to highlight areas of the field which might be of interest to a
mathematician, to point out some related open problems, and to suggest some
large-scale projects. Schema theory seeks to give a theoretical justification
for the efficacy of the field of genetic algorithms, so readers who have
studied genetic algorithms stand to gain the most from this paper. However,
nothing beyond basic probability theory is assumed of the reader, and for this
reason we write in a fairly informal style.
  Because the mathematics behind the theorems in schema theory is relatively
elementary, we focus more on the motivation and philosophy. Many of these
results have been proven elsewhere, so this paper is designed to serve a
primarily expository role. We attempt to cast known results in a new light,
which makes the suggested future directions natural. This involves devoting a
substantial amount of time to the history of the field.
  We hope that this exposition will entice some mathematicians to do research
in this area, that it will serve as a road map for researchers new to the
field, and that it will help explain how schema theory developed. Furthermore,
we hope that the results collected in this document will serve as a useful
reference. Finally, as far as the author knows, the questions raised in the
final section are new.
Graphical Modelling in Genetics and Systems Biology
Authors: Marco Scutari
Date: 2012-10-14 00:00:00

Graphical modelling has a long history in statistics as a tool for the
analysis of multivariate data, starting from Wright's path analysis and Gibbs'
applications to statistical physics at the beginning of the last century. In
its modern form, it was pioneered by Lauritzen and Wermuth and Pearl in the
1980s, and has since found applications in fields as diverse as bioinformatics,
customer satisfaction surveys and weather forecasts.
  Genetics and systems biology are unique among these fields in the dimension
of the data sets they study, which often contain several hundreds of variables
and only a few tens or hundreds of observations. This raises problems in both
computational complexity and the statistical significance of the resulting
networks, collectively known as the "curse of dimensionality". Furthermore, the
data themselves are difficult to model correctly due to the limited
understanding of the underlying mechanisms. In the following, we will
illustrate how such challenges affect practical graphical modelling and some
possible solutions.
Towards a Generic Framework for the Development of Unicode Based Digital Sindhi Dictionaries
Authors: Imdad Ali Ismaili, Zeeshan Bhatti, Azhar Ali Shah
Date: 2014-01-12 00:00:00

Dictionaries are essence of any language providing vital linguistic recourse
for the language learners, researchers and scholars. This paper focuses on the
methodology and techniques used in developing software architecture for a
UBSESD (Unicode Based Sindhi to English and English to Sindhi Dictionary). The
proposed system provides an accurate solution for construction and
representation of Unicode based Sindhi characters in a dictionary implementing
Hash Structure algorithm and a custom java Object as its internal data
structure saved in a file. The System provides facilities for Insertion,
Deletion and Editing of new records of Sindhi. Through this framework any type
of Sindhi to English and English to Sindhi Dictionary (belonging to different
domains of knowledge, e.g. engineering, medicine, computer, biology etc.) could
be developed easily with accurate representation of Unicode Characters in font
independent manner.
Sentiment Analysis Using Collaborated Opinion Mining
Authors: Deepali Virmani, Vikrant Malhotra, Ridhi Tyagi
Date: 2014-01-12 00:00:00
Tasks: Opinion Mining, Sentiment Analysis

Opinion mining and Sentiment analysis have emerged as a field of study since
the widespread of World Wide Web and internet. Opinion refers to extraction of
those lines or phrase in the raw and huge data which express an opinion.
Sentiment analysis on the other hand identifies the polarity of the opinion
being extracted. In this paper we propose the sentiment analysis in
collaboration with opinion extraction, summarization, and tracking the records
of the students. The paper modifies the existing algorithm in order to obtain
the collaborated opinion about the students. The resultant opinion is
represented as very high, high, moderate, low and very low. The paper is based
on a case study where teachers give their remarks about the students and by
applying the proposed sentiment analysis algorithm the opinion is extracted and
represented.