pwc-archive/papers-with-abstracts huggingface dataset:
A Statistical Modeling Approach to Computer-Aided Quantification of Dental Biofilm
Authors: Awais Mansoor, Valery Patsekin, Dale Scherl, J. Paul Robinson, Bartlomiej Rajwa
Date: 2014-07-09 00:00:00
Tasks: Superpixels

Biofilm is a formation of microbial material on tooth substrata. Several
methods to quantify dental biofilm coverage have recently been reported in the
literature, but at best they provide a semi-automated approach to
quantification with significant input from a human grader that comes with the
graders bias of what are foreground, background, biofilm, and tooth.
Additionally, human assessment indices limit the resolution of the
quantification scale; most commercial scales use five levels of quantification
for biofilm coverage (0%, 25%, 50%, 75%, and 100%). On the other hand, current
state-of-the-art techniques in automatic plaque quantification fail to make
their way into practical applications owing to their inability to incorporate
human input to handle misclassifications. This paper proposes a new interactive
method for biofilm quantification in Quantitative light-induced fluorescence
(QLF) images of canine teeth that is independent of the perceptual bias of the
grader. The method partitions a QLF image into segments of uniform texture and
intensity called superpixels; every superpixel is statistically modeled as a
realization of a single 2D Gaussian Markov random field (GMRF) whose parameters
are estimated; the superpixel is then assigned to one of three classes
(background, biofilm, tooth substratum) based on the training set of data. The
quantification results show a high degree of consistency and precision. At the
same time, the proposed method gives pathologists full control to post-process
the automatic quantification by flipping misclassified superpixels to a
different state (background, tooth, biofilm) with a single click, providing
greater usability than simply marking the boundaries of biofilm and tooth as
done by current state-of-the-art methods.
Approximate resilience, monotonicity, and the complexity of agnostic learning
Authors: Dana Dachman-Soled, Vitaly Feldman, Li-Yang Tan, Andrew Wan, Karl Wimmer
Date: 2014-05-21 00:00:00

A function $f$ is $d$-resilient if all its Fourier coefficients of degree at
most $d$ are zero, i.e., $f$ is uncorrelated with all low-degree parities. We
study the notion of $\mathit{approximate}$ $\mathit{resilience}$ of Boolean
functions, where we say that $f$ is $\alpha$-approximately $d$-resilient if $f$
is $\alpha$-close to a $[-1,1]$-valued $d$-resilient function in $\ell_1$
distance. We show that approximate resilience essentially characterizes the
complexity of agnostic learning of a concept class $C$ over the uniform
distribution. Roughly speaking, if all functions in a class $C$ are far from
being $d$-resilient then $C$ can be learned agnostically in time $n^{O(d)}$ and
conversely, if $C$ contains a function close to being $d$-resilient then
agnostic learning of $C$ in the statistical query (SQ) framework of Kearns has
complexity of at least $n^{\Omega(d)}$. This characterization is based on the
duality between $\ell_1$ approximation by degree-$d$ polynomials and
approximate $d$-resilience that we establish. In particular, it implies that
$\ell_1$ approximation by low-degree polynomials, known to be sufficient for
agnostic learning over product distributions, is in fact necessary.
  Focusing on monotone Boolean functions, we exhibit the existence of
near-optimal $\alpha$-approximately
$\widetilde{\Omega}(\alpha\sqrt{n})$-resilient monotone functions for all
$\alpha>0$. Prior to our work, it was conceivable even that every monotone
function is $\Omega(1)$-far from any $1$-resilient function. Furthermore, we
construct simple, explicit monotone functions based on ${\sf Tribes}$ and ${\sf
CycleRun}$ that are close to highly resilient functions. Our constructions are
based on a fairly general resilience analysis and amplification. These
structural results, together with the characterization, imply nearly optimal
lower bounds for agnostic learning of monotone juntas.
On Strong and Default Negation in Logic Program Updates (Extended Version)
Authors: Martin Slota, Martin Baláz, João Leite
Date: 2014-04-27 00:00:00
Tasks: Negation

Existing semantics for answer-set program updates fall into two categories:
either they consider only strong negation in heads of rules, or they primarily
rely on default negation in heads of rules and optionally provide support for
strong negation by means of a syntactic transformation. In this paper we
pinpoint the limitations of both these approaches and argue that both types of
negation should be first-class citizens in the context of updates. We identify
principles that plausibly constrain their interaction but are not
simultaneously satisfied by any existing rule update semantics. Then we extend
one of the most advanced semantics with direct support for strong negation and
show that it satisfies the outlined principles as well as a variety of other
desirable properties.
A least-squares method for sparse low rank approximation of multivariate functions
Authors: Mathilde Chevreuil, Régis Lebrun, Anthony Nouy, Prashant Rai
Date: 2013-04-30 00:00:00

In this paper, we propose a low-rank approximation method based on discrete
least-squares for the approximation of a multivariate function from random,
noisy-free observations. Sparsity inducing regularization techniques are used
within classical algorithms for low-rank approximation in order to exploit the
possible sparsity of low-rank approximations. Sparse low-rank approximations
are constructed with a robust updated greedy algorithm which includes an
optimal selection of regularization parameters and approximation ranks using
cross validation techniques. Numerical examples demonstrate the capability of
approximating functions of many variables even when very few function
evaluations are available, thus proving the interest of the proposed algorithm
for the propagation of uncertainties through complex computational models.
A Survey of Named Entity Recognition in Assamese and other Indian Languages
Authors: Gitimoni Talukdar, Pranjal Protim Borah, Arup Baruah
Date: 2014-07-09 00:00:00
Tasks: Document Summarization, Machine Translation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Question Answering, Translation

Named Entity Recognition is always important when dealing with major Natural
Language Processing tasks such as information extraction, question-answering,
machine translation, document summarization etc so in this paper we put forward
a survey of Named Entities in Indian Languages with particular reference to
Assamese. There are various rule-based and machine learning approaches
available for Named Entity Recognition. At the very first of the paper we give
an idea of the available approaches for Named Entity Recognition and then we
discuss about the related research in this field. Assamese like other Indian
languages is agglutinative and suffers from lack of appropriate resources as
Named Entity Recognition requires large data sets, gazetteer list, dictionary
etc and some useful feature like capitalization as found in English cannot be
found in Assamese. Apart from this we also describe some of the issues faced in
Assamese while doing Named Entity Recognition.
CNN: Single-label to Multi-label
Authors: Yunchao Wei, Wei Xia, Junshi Huang, Bingbing Ni, Jian Dong, Yao Zhao, Shuicheng Yan
Date: 2014-06-22 00:00:00
Tasks: image-classification, Image Classification

Convolutional Neural Network (CNN) has demonstrated promising performance in
single-label image classification tasks. However, how CNN best copes with
multi-label images still remains an open problem, mainly due to the complex
underlying object layouts and insufficient multi-label training images. In this
work, we propose a flexible deep CNN infrastructure, called
Hypotheses-CNN-Pooling (HCP), where an arbitrary number of object segment
hypotheses are taken as the inputs, then a shared CNN is connected with each
hypothesis, and finally the CNN output results from different hypotheses are
aggregated with max pooling to produce the ultimate multi-label predictions.
Some unique characteristics of this flexible deep CNN infrastructure include:
1) no ground truth bounding box information is required for training; 2) the
whole HCP infrastructure is robust to possibly noisy and/or redundant
hypotheses; 3) no explicit hypothesis label is required; 4) the shared CNN may
be well pre-trained with a large-scale single-label image dataset, e.g.
ImageNet; and 5) it may naturally output multi-label prediction results.
Experimental results on Pascal VOC2007 and VOC2012 multi-label image datasets
well demonstrate the superiority of the proposed HCP infrastructure over other
state-of-the-arts. In particular, the mAP reaches 84.2% by HCP only and 90.3%
after the fusion with our complementary result in [47] based on hand-crafted
features on the VOC2012 dataset, which significantly outperforms the
state-of-the-arts with a large margin of more than 7%.
Online Stroke and Akshara Recognition GUI in Assamese Language Using Hidden Markov Model
Authors: SRM Prasanna, Rituparna Devi, Deepjoy Das, Subhankar Ghosh, Krishna Naik
Date: 2014-07-09 00:00:00
Tasks: valid

The work describes the development of Online Assamese Stroke & Akshara
Recognizer based on a set of language rules. In handwriting literature strokes
are composed of two coordinate trace in between pen down and pen up labels. The
Assamese aksharas are combination of a number of strokes, the maximum number of
strokes taken to make a combination being eight. Based on these combinations
eight language rule models have been made which are used to test if a set of
strokes form a valid akshara. A Hidden Markov Model is used to train 181
different stroke patterns which generates a model used during stroke level
testing. Akshara level testing is performed by integrating a GUI (provided by
CDAC-Pune) with the Binaries of HTK toolkit classifier, HMM train model and the
language rules using a dynamic linked library (dll). We have got a stroke level
performance of 94.14% and akshara level performance of 84.2%.
Representation as a Service
Authors: Ouais Alsharif, Philip Bachman, Joelle Pineau
Date: 2014-02-24 00:00:00
Tasks: Lifelong learning

Consider a Machine Learning Service Provider (MLSP) designed to rapidly
create highly accurate learners for a never-ending stream of new tasks. The
challenge is to produce task-specific learners that can be trained from few
labeled samples, even if tasks are not uniquely identified, and the number of
tasks and input dimensionality are large. In this paper, we argue that the MLSP
should exploit knowledge from previous tasks to build a good representation of
the environment it is in, and more precisely, that useful representations for
such a service are ones that minimize generalization error for a new hypothesis
trained on a new task. We formalize this intuition with a novel method that
minimizes an empirical proxy of the intra-task small-sample generalization
error. We present several empirical results showing state-of-the art
performance on single-task transfer, multitask learning, and the full lifelong
learning problem.
The jump set under geometric regularisation. Part 2: Higher-order approaches
Authors: Tuomo Valkonen
Date: 2014-07-09 00:00:00
Tasks: Denoising

In Part 1, we developed a new technique based on Lipschitz pushforwards for
proving the jump set containment property $\mathcal{H}^{m-1}(J_u \setminus
J_f)=0$ of solutions $u$ to total variation denoising. We demonstrated that the
technique also applies to Huber-regularised TV. Now, in this Part 2, we extend
the technique to higher-order regularisers. We are not quite able to prove the
property for total generalised variation (TGV) based on the symmetrised
gradient for the second-order term. We show that the property holds under three
conditions: First, the solution $u$ is locally bounded. Second, the
second-order variable is of locally bounded variation, $w \in
\mbox{BV}_\mbox{loc}(\Omega; \mathbb{R}^m)$, instead of just bounded
deformation, $w \in \mbox{BD}(\Omega)$. Third, $w$ does not jump on $J_u$
parallel to it. The second condition can be achieved for non-symmetric TGV.
Both the second and third condition can be achieved if we change the Radon (or
$L^1$) norm of the symmetrised gradient $Ew$ into an $L^p$ norm, $p>1$, in
which case Korn's inequality holds. We also consider the application of the
technique to infimal convolution TV, and study the limiting behaviour of the
singular part of $D u$, as the second parameter of $\mbox{TGV}^2$ goes to zero.
Unsurprisingly, it vanishes, but in numerical discretisations the situation
looks quite different. Finally, our work additionally includes a result on
TGV-strict approximation in $\mbox{BV}(\Omega)$.
Collaborative Recommendation with Auxiliary Data: A Transfer Learning View
Authors: Weike Pan
Date: 2014-07-09 00:00:00
Tasks: Transfer Learning

Intelligent recommendation technology has been playing an increasingly
important role in various industry applications such as e-commerce product
promotion and Internet advertisement display. Besides users' feedbacks (e.g.,
numerical ratings) on items as usually exploited by some typical recommendation
algorithms, there are often some additional data such as users' social circles
and other behaviors. Such auxiliary data are usually related to users'
preferences on items behind the numerical ratings. Collaborative recommendation
with auxiliary data (CRAD) aims to leverage such additional information so as
to improve the personalization services, which have received much attention
from both researchers and practitioners.
  Transfer learning (TL) is proposed to extract and transfer knowledge from
some auxiliary data in order to assist the learning task on some target data.
In this paper, we consider the CRAD problem from a transfer learning view,
especially on how to achieve knowledge transfer from some auxiliary data.
First, we give a formal definition of transfer learning for CRAD (TL-CRAD).
Second, we extend the existing categorization of TL techniques (i.e., adaptive,
collective and integrative knowledge transfer algorithm styles) with three
knowledge transfer strategies (i.e., prediction rule, regularization and
constraint). Third, we propose a novel generic knowledge transfer framework for
TL-CRAD. Fourth, we describe some representative works of each specific
knowledge transfer strategy of each algorithm style in detail, which are
expected to inspire further works. Finally, we conclude the paper with some
summary discussions and several future directions.
Optimal Cooperative Cognitive Relaying and Spectrum Access for an Energy Harvesting Cognitive Radio: Reinforcement Learning Approach
Authors: Ahmed El Shafie, Tamer Khattab, Hussien Saad, Amr Mohamed
Date: 2014-03-30 00:00:00
Tasks: Reinforcement Learning

In this paper, we consider a cognitive setting under the context of
cooperative communications, where the cognitive radio (CR) user is assumed to
be a self-organized relay for the network. The CR user and the PU are assumed
to be energy harvesters. The CR user cooperatively relays some of the
undelivered packets of the primary user (PU). Specifically, the CR user stores
a fraction of the undelivered primary packets in a relaying queue (buffer). It
manages the flow of the undelivered primary packets to its relaying queue using
the appropriate actions over time slots. Moreover, it has the decision of
choosing the used queue for channel accessing at idle time slots (slots where
the PU's queue is empty). It is assumed that one data packet transmission
dissipates one energy packet. The optimal policy changes according to the
primary and CR users arrival rates to the data and energy queues as well as the
channels connectivity. The CR user saves energy for the PU by taking the
responsibility of relaying the undelivered primary packets. It optimally
organizes its own energy packets to maximize its payoff as time progresses.
Inferring latent structures via information inequalities
Authors: R. Chaves, L. Luft, T. O. Maciel, D. Gross, D. Janzing, B. Schölkopf
Date: 2014-07-08 00:00:00

One of the goals of probabilistic inference is to decide whether an
empirically observed distribution is compatible with a candidate Bayesian
network. However, Bayesian networks with hidden variables give rise to highly
non-trivial constraints on the observed distribution. Here, we propose an
information-theoretic approach, based on the insight that conditions on
entropies of Bayesian networks take the form of simple linear inequalities. We
describe an algorithm for deriving entropic tests for latent structures. The
well-known conditional independence tests appear as a special case. While the
approach applies for generic Bayesian networks, we presently adopt the causal
view, and show the versatility of the framework by treating several relevant
problems from that domain: detecting common ancestors, quantifying the strength
of causal influence, and inferring the direction of causation from two-variable
marginals.
The tractability of CSP classes defined by forbidden patterns
Authors: David A. Cohen, Martin C. Cooper, Páidí Creed, András Z. Salamon
Date: 2011-03-08 00:00:00

The constraint satisfaction problem (CSP) is a general problem central to
computer science and artificial intelligence. Although the CSP is NP-hard in
general, considerable effort has been spent on identifying tractable
subclasses. The main two approaches consider structural properties
(restrictions on the hypergraph of constraint scopes) and relational properties
(restrictions on the language of constraint relations). Recently, some authors
have considered hybrid properties that restrict the constraint hypergraph and
the relations simultaneously.
  Our key contribution is the novel concept of a CSP pattern and classes of
problems defined by forbidden patterns (which can be viewed as forbidding
generic subproblems). We describe the theoretical framework which can be used
to reason about classes of problems defined by forbidden patterns. We show that
this framework generalises relational properties and allows us to capture known
hybrid tractable classes.
  Although we are not close to obtaining a dichotomy concerning the
tractability of general forbidden patterns, we are able to make some progress
in a special case: classes of problems that arise when we can only forbid
binary negative patterns (generic subproblems in which only inconsistent tuples
are specified). In this case we are able to characterise very large classes of
tractable and NP-hard forbidden patterns. This leaves the complexity of just
one case unresolved and we conjecture that this last case is tractable.
Meteorological time series forecasting with pruned multi-layer perceptron and 2-stage Levenberg-Marquardt method
Authors: Cyril Voyant, Wani W. Tamas, Marie Laure Nivet, Gilles Notton, Christophe Paoli, Aurélia Balu, Marc Muselli
Date: 2014-07-08 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting

A Multi-Layer Perceptron (MLP) defines a family of artificial neural networks
often used in TS modeling and forecasting. Because of its "black box" aspect,
many researchers refuse to use it. Moreover, the optimization (often based on
the exhaustive approach where "all" configurations are tested) and learning
phases of this artificial intelligence tool (often based on the
Levenberg-Marquardt algorithm; LMA) are weaknesses of this approach
(exhaustively and local minima). These two tasks must be repeated depending on
the knowledge of each new problem studied, making the process, long, laborious
and not systematically robust. In this paper a pruning process is proposed.
This method allows, during the training phase, to carry out an inputs selecting
method activating (or not) inter-nodes connections in order to verify if
forecasting is improved. We propose to use iteratively the popular damped
least-squares method to activate inputs and neurons. A first pass is applied to
10% of the learning sample to determine weights significantly different from 0
and delete other. Then a classical batch process based on LMA is used with the
new MLP. The validation is done using 25 measured meteorological TS and
cross-comparing the prediction results of the classical LMA and the 2-stage
LMA.
Assamese-English Bilingual Machine Translation
Authors: Kalyanee Kanchan Baruah, Pranjal Das, Abdul Hannan, Shikhar Kr. Sarma
Date: 2014-07-08 00:00:00
Tasks: Language Modeling, Language Modelling, Machine Translation, Translation, Transliteration

Machine translation is the process of translating text from one language to
another. In this paper, Statistical Machine Translation is done on Assamese and
English language by taking their respective parallel corpus. A statistical
phrase based translation toolkit Moses is used here. To develop the language
model and to align the words we used two another tools IRSTLM, GIZA
respectively. BLEU score is used to check our translation system performance,
how good it is. A difference in BLEU scores is obtained while translating
sentences from Assamese to English and vice-versa. Since Indian languages are
morphologically very rich hence translation is relatively harder from English
to Assamese resulting in a low BLEU score. A statistical transliteration system
is also introduced with our translation system to deal basically with proper
nouns, OOV (out of vocabulary) words which are not present in our corpus.
A Critical Reassessment of Evolutionary Algorithms on the cryptanalysis of the simplified data encryption standard algorithm
Authors: Fabien Teytaud, Cyril Fonlupt
Date: 2014-07-08 00:00:00
Tasks: Cryptanalysis, Evolutionary Algorithms

In this paper we analyze the cryptanalysis of the simplified data encryption
standard algorithm using meta-heuristics and in particular genetic algorithms.
The classic fitness function when using such an algorithm is to compare n-gram
statistics of a the decrypted message with those of the target message. We show
that using such a function is irrelevant in case of Genetic Algorithm, simply
because there is no correlation between the distance to the real key (the
optimum) and the value of the fitness, in other words, there is no hidden
gradient. In order to emphasize this assumption we experimentally show that a
genetic algorithm perform worse than a random search on the cryptanalysis of
the simplified data encryption standard algorithm.
Inter-Rater Agreement Study on Readability Assessment in Bengali
Authors: Shanta Phani, Shibamouli Lahiri, Arindam Biswas
Date: 2014-07-08 00:00:00

An inter-rater agreement study is performed for readability assessment in
Bengali. A 1-7 rating scale was used to indicate different levels of
readability. We obtained moderate to fair agreement among seven independent
annotators on 30 text passages written by four eminent Bengali authors. As a by
product of our study, we obtained a readability-annotated ground truth dataset
in Bengali. .
Regression-Based Image Alignment for General Object Categories
Authors: Hilton Bristow, Simon Lucey
Date: 2014-07-08 00:00:00
Tasks: Object, regression

Gradient-descent methods have exhibited fast and reliable performance for
image alignment in the facial domain, but have largely been ignored by the
broader vision community. They require the image function be smooth and
(numerically) differentiable -- properties that hold for pixel-based
representations obeying natural image statistics, but not for more general
classes of non-linear feature transforms. We show that transforms such as Dense
SIFT can be incorporated into a Lucas Kanade alignment framework by predicting
descent directions via regression. This enables robust matching of instances
from general object categories whilst maintaining desirable properties of Lucas
Kanade such as the capacity to handle high-dimensional warp parametrizations
and a fast rate of convergence. We present alignment results on a number of
objects from ImageNet, and an extension of the method to unsupervised joint
alignment of objects from a corpus of images.