pwc-archive/papers-with-abstracts huggingface dataset:
An open diachronic corpus of historical Spanish: annotation criteria and automatic modernisation of spelling
Authors: Felipe Sánchez-Martínez, Isabel Martínez-Sempere, Xavier Ivars-Ribes, Rafael C. Carrasco
Date: 2013-06-16 00:00:00
Tasks: LEMMA, Machine Translation, Translation

The IMPACT-es diachronic corpus of historical Spanish compiles over one
hundred books --containing approximately 8 million words-- in addition to a
complementary lexicon which links more than 10 thousand lemmas with
attestations of the different variants found in the documents. This textual
corpus and the accompanying lexicon have been released under an open license
(Creative Commons by-nc-sa) in order to permit their intensive exploitation in
linguistic research. Approximately 7% of the words in the corpus (a selection
aimed at enhancing the coverage of the most frequent word forms) have been
annotated with their lemma, part of speech, and modern equivalent. This paper
describes the annotation criteria followed and the standards, based on the Text
Encoding Initiative recommendations, used to the represent the texts in digital
form. As an illustration of the possible synergies between diachronic textual
resources and linguistic research, we describe the application of statistical
machine translation techniques to infer probabilistic context-sensitive rules
for the automatic modernisation of spelling. The automatic modernisation with
this type of statistical methods leads to very low character error rates when
the output is compared with the supervised modern version of the text.
Arabizi Detection and Conversion to Arabic
Authors: Kareem Darwish
Date: 2013-06-28 00:00:00
Proceeding: WS 2014 10
Tasks: Language Modeling, Language Modelling, Transliteration

Arabizi is Arabic text that is written using Latin characters. Arabizi is
used to present both Modern Standard Arabic (MSA) or Arabic dialects. It is
commonly used in informal settings such as social networking sites and is often
with mixed with English. In this paper we address the problems of: identifying
Arabizi in text and converting it to Arabic characters. We used word and
sequence-level features to identify Arabizi that is mixed with English. We
achieved an identification accuracy of 98.5%. As for conversion, we used
transliteration mining with language modeling to generate equivalent Arabic
text. We achieved 88.7% conversion accuracy, with roughly a third of errors
being spelling and morphological variants of the forms in ground truth.
Digital Image Tamper Detection Techniques - A Comprehensive Study
Authors: Minati Mishra, Flt. Lt. Dr. M. C. Adhikary
Date: 2013-06-28 00:00:00

Photographs are considered to be the most powerful and trustworthy media of
expression. For a long time, those were accepted as proves of evidences in
varied fields such as journalism, forensic investigations, military
intelligence, scientific research and publications, crime detection and legal
proceedings, investigation of insurance claims, medical imaging etc. Today,
digital images have completely replaced the conventional photographs from every
sphere of life but unfortunately, they seldom enjoy the credibility of their
conventional counterparts, thanks to the rapid advancements in the field of
digital image processing. The increasing availability of low cost and sometimes
free of cost image editing software such as Photoshop, Corel Paint Shop,
Photoscape, PhotoPlus, GIMP and Pixelmator have made the tampering of digital
images even more easier and a common practice. Now it has become quite
impossible to say whether a photograph is a genuine camera output or a
manipulated version of it just by looking at it. As a result, photographs have
almost lost their reliability and place as proves of evidences in all fields.
This is why digital image tamper detection has emerged as an important research
area to establish the authenticity of digital photographs by separating the
tampered lots from the original ones. This paper gives a brief history of image
tampering and a state-of-the-art review of the tamper detection techniques.
A Novel Active Contour Model for Texture Segmentation
Authors: Aditya Tatu, Sumukh Bansal
Date: 2013-06-28 00:00:00
Tasks: Clustering

Texture is intuitively defined as a repeated arrangement of a basic pattern
or object in an image. There is no mathematical definition of a texture though.
The human visual system is able to identify and segment different textures in a
given image. Automating this task for a computer is far from trivial. There are
three major components of any texture segmentation algorithm: (a) The features
used to represent a texture, (b) the metric induced on this representation
space and (c) the clustering algorithm that runs over these features in order
to segment a given image into different textures. In this paper, we propose an
active contour based novel unsupervised algorithm for texture segmentation. We
use intensity covariance matrices of regions as the defining feature of
textures and find regions that have the most inter-region dissimilar covariance
matrices using active contours. Since covariance matrices are symmetric
positive definite, we use geodesic distance defined on the manifold of
symmetric positive definite matrices PD(n) as a measure of dissimlarity between
such matrices. We demonstrate performance of our algorithm on both artificial
and real texture images.
Measurements of collective machine intelligence
Authors: Michel Halmes
Date: 2013-06-27 00:00:00
Tasks: Decision Making

Independent from the still ongoing research in measuring individual
intelligence, we anticipate and provide a framework for measuring collective
intelligence. Collective intelligence refers to the idea that several
individuals can collaborate in order to achieve high levels of intelligence. We
present thus some ideas on how the intelligence of a group can be measured and
simulate such tests. We will however focus here on groups of artificial
intelligence agents (i.e., machines). We will explore how a group of agents is
able to choose the appropriate problem and to specialize for a variety of
tasks. This is a feature which is an important contributor to the increase of
intelligence in a group (apart from the addition of more agents and the
improvement due to common decision making). Our results reveal some interesting
results about how (collective) intelligence can be modeled, about how
collective intelligence tests can be designed and about the underlying dynamics
of collective intelligence. As it will be useful for our simulations, we
provide also some improvements of the threshold allocation model originally
used in the area of swarm intelligence but further generalized here.
Optimal Feature Selection in High-Dimensional Discriminant Analysis
Authors: Mladen Kolar, Han Liu
Date: 2013-06-27 00:00:00
Tasks: feature selection, Variable Selection, Vocal Bursts Intensity Prediction

We consider the high-dimensional discriminant analysis problem. For this
problem, different methods have been proposed and justified by establishing
exact convergence rates for the classification risk, as well as the l2
convergence results to the discriminative rule. However, sharp theoretical
analysis for the variable selection performance of these procedures have not
been established, even though model interpretation is of fundamental importance
in scientific data analysis. This paper bridges the gap by providing sharp
sufficient conditions for consistent variable selection using the sparse
discriminant analysis (Mai et al., 2012). Through careful analysis, we
establish rates of convergence that are significantly faster than the best
known results and admit an optimal scaling of the sample size n, dimensionality
p, and sparsity level s in the high-dimensional setting. Sufficient conditions
are complemented by the necessary information theoretic limits on the variable
selection problem in the context of high-dimensional discriminant analysis.
Exploiting a numerical equivalence result, our method also establish the
optimal results for the ROAD estimator (Fan et al., 2012) and the sparse
optimal scaling estimator (Clemmensen et al., 2011). Furthermore, we analyze an
exhaustive search procedure, whose performance serves as a benchmark, and show
that it is variable selection consistent under weaker conditions. Extensive
simulations demonstrating the sharpness of the bounds are also provided.
Solving Relational MDPs with Exogenous Events and Additive Rewards
Authors: S. Joshi, R. Khardon, P. Tadepalli, A. Raghavan, A. Fern
Date: 2013-06-26 00:00:00

We formalize a simple but natural subclass of service domains for relational
planning problems with object-centered, independent exogenous events and
additive rewards capturing, for example, problems in inventory control.
Focusing on this subclass, we present a new symbolic planning algorithm which
is the first algorithm that has explicit performance guarantees for relational
MDPs with exogenous events. In particular, under some technical conditions, our
planning algorithm provides a monotonic lower bound on the optimal value
function. To support this algorithm we present novel evaluation and reduction
techniques for generalized first order decision diagrams, a knowledge
representation for real-valued functions over relational world states. Our
planning algorithm uses a set of focus states, which serves as a training set,
to simplify and approximate the symbolic solution, and can thus be seen to
perform learning for planning. A preliminary experimental evaluation
demonstrates the validity of our approach.
A Fuzzy Topsis Multiple-Attribute Decision Making for Scholarship Selection
Authors: Shofwatul 'Uyun, Imam Riadi
Date: 2013-06-27 00:00:00
Tasks: Attribute, Decision Making

As the education fees are becoming more expensive, more students apply for
scholarships. Consequently, hundreds and even thousands of applications need to
be handled by the sponsor. To solve the problems, some alternatives based on
several attributes (criteria) need to be selected. In order to make a decision
on such fuzzy problems, Fuzzy Multiple Attribute Decision Making (FMDAM) can be
applied. In this study, Unified Modeling Language (UML) in FMADM with TOPSIS
and Weighted Product (WP) methods is applied to select the candidates for
academic and non-academic scholarships at Universitas Islam Negeri Sunan
Kalijaga. Data used were a crisp and fuzzy data. The results show that TOPSIS
and Weighted Product FMADM methods can be used to select the most suitable
candidates to receive the scholarships since the preference values applied in
this method can show applicants with the highest eligibility
Traffic data reconstruction based on Markov random field modeling
Authors: Shun Kataoka, Muneki Yasuda, Cyril Furtlehner, Kazuyuki Tanaka
Date: 2013-06-27 00:00:00
Tasks: BIG-bench Machine Learning

We consider the traffic data reconstruction problem. Suppose we have the
traffic data of an entire city that are incomplete because some road data are
unobserved. The problem is to reconstruct the unobserved parts of the data. In
this paper, we propose a new method to reconstruct incomplete traffic data
collected from various traffic sensors. Our approach is based on Markov random
field modeling of road traffic. The reconstruction is achieved by using
mean-field method and a machine learning method. We numerically verify the
performance of our method using realistic simulated traffic data for the real
road network of Sendai, Japan.
Close the Gaps: A Learning-while-Doing Algorithm for a Class of Single-Product Revenue Management Problems
Authors: Zizhuo Wang, Shiming Deng, Yinyu Ye
Date: 2011-01-24 00:00:00
Tasks: Management

We consider a retailer selling a single product with limited on-hand
inventory over a finite selling season. Customer demand arrives according to a
Poisson process, the rate of which is influenced by a single action taken by
the retailer (such as price adjustment, sales commission, advertisement
intensity, etc.). The relationship between the action and the demand rate is
not known in advance. However, the retailer is able to learn the optimal action
"on the fly" as she maximizes her total expected revenue based on the observed
demand reactions.
  Using the pricing problem as an example, we propose a dynamic
"learning-while-doing" algorithm that only involves function value estimation
to achieve a near-optimal performance. Our algorithm employs a series of
shrinking price intervals and iteratively tests prices within that interval
using a set of carefully chosen parameters. We prove that the convergence rate
of our algorithm is among the fastest of all possible algorithms in terms of
asymptotic "regret" (the relative loss comparing to the full information
optimal solution). Our result closes the performance gaps between parametric
and non-parametric learning and between a post-price mechanism and a
customer-bidding mechanism. Important managerial insight from this research is
that the values of information on both the parametric form of the demand
function as well as each customer's exact reservation price are less important
than prior literature suggests. Our results also suggest that firms would be
better off to perform dynamic learning and action concurrently rather than
sequentially.
Metaheuristics in Flood Disaster Management and Risk Assessment
Authors: Vena Pearl Bongolan, Florencio C. Ballesteros, Jr., Joyce Anne M. Banting, Aina Marie Q. Olaes, Charlymagne R. Aquino
Date: 2013-06-26 00:00:00
Tasks: Management

A conceptual area is divided into units or barangays, each was allowed to
evolve under a physical constraint. A risk assessment method was then used to
identify the flood risk in each community using the following risk factors: the
area's urbanized area ratio, literacy rate, mortality rate, poverty incidence,
radio/TV penetration, and state of structural and non-structural measures.
Vulnerability is defined as a weighted-sum of these components. A penalty was
imposed for reduced vulnerability. Optimization comparison was done with
MatLab's Genetic Algorithms and Simulated Annealing; results showed 'extreme'
solutions and realistic designs, for simulated annealing and genetic algorithm,
respectively.
A Data Mining Approach to Solve the Goal Scoring Problem
Authors: Renato Oliveira, Paulo Adeodato, Arthur Carvalho, Icamaan Viegas, Christian Diego, Tsang Ing-Ren
Date: 2013-05-21 00:00:00

In soccer, scoring goals is a fundamental objective which depends on many
conditions and constraints. Considering the RoboCup soccer 2D-simulator, this
paper presents a data mining-based decision system to identify the best time
and direction to kick the ball towards the goal to maximize the overall chances
of scoring during a simulated soccer match. Following the CRISP-DM methodology,
data for modeling were extracted from matches of major international
tournaments (10691 kicks), knowledge about soccer was embedded via
transformation of variables and a Multilayer Perceptron was used to estimate
the scoring chance. Experimental performance assessment to compare this
approach against previous LDA-based approach was conducted from 100 matches.
Several statistical metrics were used to analyze the performance of the system
and the results showed an increase of 7.7% in the number of kicks, producing an
overall increase of 78% in the number of goals scored.
Compressive Acquisition of Dynamic Scenes
Authors: Aswin C. Sankaranarayanan, Pavan K Turaga, Rama Chellappa, Richard G. Baraniuk
Date: 2012-01-23 00:00:00
Tasks: Compressive Sensing

Compressive sensing (CS) is a new approach for the acquisition and recovery
of sparse signals and images that enables sampling rates significantly below
the classical Nyquist rate. Despite significant progress in the theory and
methods of CS, little headway has been made in compressive video acquisition
and recovery. Video CS is complicated by the ephemeral nature of dynamic
events, which makes direct extensions of standard CS imaging architectures and
signal models difficult. In this paper, we develop a new framework for video CS
for dynamic textured scenes that models the evolution of the scene as a linear
dynamical system (LDS). This reduces the video recovery problem to first
estimating the model parameters of the LDS from compressive measurements, and
then reconstructing the image frames. We exploit the low-dimensional dynamic
parameters (the state sequence) and high-dimensional static parameters (the
observation matrix) of the LDS to devise a novel compressive measurement
strategy that measures only the dynamic part of the scene at each instant and
accumulates measurements over time to estimate the static parameters. This
enables us to lower the compressive measurement rate considerably. We validate
our approach with a range of experiments involving both video recovery, sensing
hyper-spectral data, and classification of dynamic scenes from compressive
data. Together, these applications demonstrate the effectiveness of the
approach.
Compressive Coded Aperture Keyed Exposure Imaging with Optical Flow Reconstruction
Authors: Zachary T. Harmany, Roummel F. Marcia, Rebecca M. Willett
Date: 2013-06-26 00:00:00
Tasks: compressed sensing, Optical Flow Estimation, Video Reconstruction

This paper describes a coded aperture and keyed exposure approach to
compressive video measurement which admits a small physical platform, high
photon efficiency, high temporal resolution, and fast reconstruction
algorithms. The proposed projections satisfy the Restricted Isometry Property
(RIP), and hence compressed sensing theory provides theoretical guarantees on
the video reconstruction quality. Moreover, the projections can be easily
implemented using existing optical elements such as spatial light modulators
(SLMs). We extend these coded mask designs to novel dual-scale masks (DSMs)
which enable the recovery of a coarse-resolution estimate of the scene with
negligible computational cost. We develop fast numerical algorithms which
utilize both temporal correlations and optical flow in the video sequence as
well as the innovative structure of the projections. Our numerical experiments
demonstrate the efficacy of the proposed approach on short-wave infrared data.
How to find real-world applications for compressive sensing
Authors: Leslie N. Smith
Date: 2013-05-06 00:00:00
Tasks: Compressive Sensing

The potential of compressive sensing (CS) has spurred great interest in the
research community and is a fast growing area of research. However, research
translating CS theory into practical hardware and demonstrating clear and
significant benefits with this hardware over current, conventional imaging
techniques has been limited. This article helps researchers to find those niche
applications where the CS approach provides substantial gain over conventional
approaches by articulating lessons learned in finding one such application; sea
skimming missile detection. As a proof of concept, it is demonstrated that a
simplified CS missile detection architecture and algorithm provides comparable
results to the conventional imaging approach but using a smaller FPA. The
primary message is that all of the excitement surrounding CS is necessary and
appropriate for encouraging our creativity but we all must also take off our
"rose colored glasses" and critically judge our ideas, methods and results
relative to conventional imaging approaches.
A maximal-information color to gray conversion method for document images: Toward an optimal grayscale representation for document image binarization
Authors: Reza Farrahi Moghaddam, Shaohua Chen, Rachid Hedjam, Mohamed Cheriet
Date: 2013-06-25 00:00:00
Tasks: Binarization

A novel method to convert color/multi-spectral images to gray-level images is
introduced to increase the performance of document binarization methods. The
method uses the distribution of the pixel data of the input document image in a
color space to find a transformation, called the dual transform, which balances
the amount of information on all color channels. Furthermore, in order to
reduce the intensity variations on the gray output, a color reduction
preprocessing step is applied. Then, a channel is selected as the gray value
representation of the document image based on the homogeneity criterion on the
text regions. In this way, the proposed method can provide a
luminance-independent contrast enhancement. The performance of the method is
evaluated against various images from two databases, the ICDAR'03 Robust
Reading, the KAIST and the DIBCO'09 datasets, subjectively and objectively with
promising results. The ground truth images for the images from the ICDAR'03
Robust Reading dataset have been created manually by the authors.
Scaling Up Robust MDPs by Reinforcement Learning
Authors: Aviv Tamar, Huan Xu, Shie Mannor
Date: 2013-06-26 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider large-scale Markov decision processes (MDPs) with parameter
uncertainty, under the robust MDP paradigm. Previous studies showed that robust
MDPs, based on a minimax approach to handle uncertainty, can be solved using
dynamic programming for small to medium sized problems. However, due to the
"curse of dimensionality", MDPs that model real-life problems are typically
prohibitively large for such approaches. In this work we employ a reinforcement
learning approach to tackle this planning problem: we develop a robust
approximate dynamic programming method based on a projected fixed point
equation to approximately solve large scale robust MDPs. We show that the
proposed method provably succeeds under certain technical conditions, and
demonstrate its effectiveness through simulation of an option pricing problem.
To the best of our knowledge, this is the first attempt to scale up the robust
MDPs paradigm.
Competency Tracking for English as a Second or Foreign Language Learners
Authors: Robert Bishop Jr
Date: 2013-06-26 00:00:00
Tasks: Decision Making, Management

My system utilizes the outcomes feature found in Moodle and other learning
content management systems (LCMSs) to keep track of where students are in terms
of what language competencies they have mastered and the competencies they need
to get where they want to go. These competencies are based on the Common
European Framework for (English) Language Learning. This data can be available
for everyone involved with a given student's progress (e.g. educators, parents,
supervisors and the students themselves). A given student's record of past
accomplishments can also be meshed with those of his classmates. Not only are a
student's competencies easily seen and tracked, educators can view competencies
of a group of students that were achieved prior to enrollment in the class.
This should make curriculum decision making easier and more efficient for
educators.
A Computational Approach to Politeness with Application to Social Factors
Authors: Cristian Danescu-Niculescu-Mizil, Moritz Sudhof, Dan Jurafsky, Jure Leskovec, Christopher Potts
Date: 2013-06-25 00:00:00
Proceeding: ACL 2013 8

We propose a computational framework for identifying linguistic aspects of
politeness. Our starting point is a new corpus of requests annotated for
politeness, which we use to evaluate aspects of politeness theory and to
uncover new interactions between politeness markers and context. These findings
guide our construction of a classifier with domain-independent lexical and
syntactic features operationalizing key components of politeness theory, such
as indirection, deference, impersonalization and modality. Our classifier
achieves close to human performance and is effective across domains. We use our
framework to study the relationship between politeness and social power,
showing that polite Wikipedia editors are more likely to achieve high status
through elections, but, once elevated, they become less polite. We see a
similar negative correlation between politeness and power on Stack Exchange,
where users at the top of the reputation scale are less polite than those at
the bottom. Finally, we apply our classifier to a preliminary analysis of
politeness variation by gender and community.