pwc-archive/papers-with-abstracts huggingface dataset:
An Extensive Repot on the Efficiency of AIS-INMACA (A Novel Integrated MACA based Clonal Classifier for Protein Coding and Promoter Region Prediction)
Authors: Pokkuluri Kiran Sree, Inampudi Ramesh Babu
Date: 2014-03-06 00:00:00

This paper exclusively reports the efficiency of AIS-INMACA. AIS-INMACA has
created good impact on solving major problems in bioinformatics like protein
region identification and promoter region prediction with less time (Pokkuluri
Kiran Sree, 2014). This AIS-INMACA is now came with several variations
(Pokkuluri Kiran Sree, 2014) towards projecting it as a tool in bioinformatics
for solving many problems in bioinformatics. So this paper will be very much
useful for so many researchers who are working in the domain of bioinformatics
with cellular automata.
A Primal-Dual Method for Training Recurrent Neural Networks Constrained by the Echo-State Property
Authors: Jianshu Chen, Li Deng
Date: 2013-11-24 00:00:00

We present an architecture of a recurrent neural network (RNN) with a
fully-connected deep neural network (DNN) as its feature extractor. The RNN is
equipped with both causal temporal prediction and non-causal look-ahead, via
auto-regression (AR) and moving-average (MA), respectively. The focus of this
paper is a primal-dual training method that formulates the learning of the RNN
as a formal optimization problem with an inequality constraint that provides a
sufficient condition for the stability of the network dynamics. Experimental
results demonstrate the effectiveness of this new method, which achieves 18.86%
phone recognition error on the TIMIT benchmark for the core test set. The
result approaches the best result of 17.7%, which was obtained by using RNN
with long short-term memory (LSTM). The results also show that the proposed
primal-dual training method produces lower recognition errors than the popular
RNN methods developed earlier based on the carefully tuned threshold parameter
that heuristically prevents the gradient from exploding.
Integer Programming Relaxations for Integrated Clustering and Outlier Detection
Authors: Lionel Ott, Linsey Pang, Fabio Ramos, David Howe, Sanjay Chawla
Date: 2014-03-06 00:00:00
Tasks: Clustering, Outlier Detection

In this paper we present methods for exemplar based clustering with outlier
selection based on the facility location formulation. Given a distance function
and the number of outliers to be found, the methods automatically determine the
number of clusters and outliers. We formulate the problem as an integer program
to which we present relaxations that allow for solutions that scale to large
data sets. The advantages of combining clustering and outlier selection
include: (i) the resulting clusters tend to be compact and semantically
coherent (ii) the clusters are more robust against data perturbations and (iii)
the outliers are contextualised by the clusters and more interpretable, i.e. it
is easier to distinguish between outliers which are the result of data errors
from those that may be indicative of a new pattern emergent in the data. We
present and contrast three relaxations to the integer program formulation: (i)
a linear programming formulation (LP) (ii) an extension of affinity propagation
to outlier detection (APOC) and (iii) a Lagrangian duality based formulation
(LD). Evaluation on synthetic as well as real data shows the quality and
scalability of these different methods.
Multi-view Face Analysis Based on Gabor Features
Authors: Hongli Liu, Weifeng Liu, Yan-Jiang Wang
Date: 2014-03-06 00:00:00
Tasks: Face Recognition

Facial analysis has attracted much attention in the technology for human-machine interface. Different methods of classification based on sparse representation and Gabor kernels have been widely applied in the fields of facial analysis. However, most of these methods treat face from a whole view standpoint. In terms of the importance of different facial views, in this paper, we present multi-view face analysis based on sparse representation and Gabor wavelet coefficients. To evaluate the performance, we conduct face analysis experiments including face recognition (FR) and face expression recognition (FER) on JAFFE database. Experiments are conducted from two parts: (1) Face images are divided into three facial parts which are forehead, eye and mouth. (2) Face images are divided into 8 parts by the orientation of Gabor kernels. Experimental results demonstrate that the proposed methods can significantly boost the performance and perform better than the other methods.
Authorship detection of SMS messages using unigrams
Authors: R. G. Ragel, P. Herath, U. Senanayake
Date: 2014-03-06 00:00:00
Tasks: Author Attribution

SMS messaging is a popular media of communication. Because of its popularity
and privacy, it could be used for many illegal purposes. Additionally, since
they are part of the day to day life, SMSes can be used as evidence for many
legal disputes. Since a cellular phone might be accessible to people close to
the owner, it is important to establish the fact that the sender of the message
is indeed the owner of the phone. For this purpose, the straight forward
solutions seem to be the use of popular stylometric methods. However, in
comparison with the data used for stylometry in the literature, SMSes have
unusual characteristics making it hard or impossible to apply these methods in
a conventional way. Our target is to come up with a method of authorship
detection of SMS messages that could still give a usable accuracy. We argue
that, considering the methods of author attribution, the best method that could
be applied to SMS messages is an n-gram method. To prove our point, we checked
two different methods of distribution comparison with varying number of
training and testing data. We specifically try to compare how well our
algorithms work under less amount of testing data and large number of candidate
authors (which we believe to be the real world scenario) against controlled
tests with less number of authors and selected SMSes with large number of
words. To counter the lack of information in an SMS message, we propose the
method of stacking together few SMSes.
AntiPlag: Plagiarism Detection on Electronic Submissions of Text Based Assignments
Authors: M. A. C. Jiffriya, M. A. C. Akmal Jahan, R. G. Ragel, S. Deegalla
Date: 2014-03-06 00:00:00
Tasks: Clustering

Plagiarism is one of the growing issues in academia and is always a concern
in Universities and other academic institutions. The situation is becoming even
worse with the availability of ample resources on the web. This paper focuses
on creating an effective and fast tool for plagiarism detection for text based
electronic assignments. Our plagiarism detection tool named AntiPlag is
developed using the tri-gram sequence matching technique. Three sets of text
based assignments were tested by AntiPlag and the results were compared against
an existing commercial plagiarism detection tool. AntiPlag showed better
results in terms of false positives compared to the commercial tool due to the
pre-processing steps performed in AntiPlag. In addition, to improve the
detection latency, AntiPlag applies a data clustering technique making it four
times faster than the commercial tool considered. AntiPlag could be used to
isolate plagiarized text based assignments from non-plagiarised assignments
easily. Therefore, we present AntiPlag, a fast and effective tool for
plagiarism detection on text based electronic assignments.
Local Similarities, Global Coding: An Algorithm for Feature Coding and its Applications
Authors: Amirreza Shaban, Hamid R. Rabiee, Mahyar Najibi
Date: 2013-11-24 00:00:00

Data coding as a building block of several image processing algorithms has
been received great attention recently. Indeed, the importance of the locality
assumption in coding approaches is studied in numerous works and several
methods are proposed based on this concept. We probe this assumption and claim
that taking the similarity between a data point and a more global set of anchor
points does not necessarily weaken the coding method as long as the underlying
structure of the anchor points are taken into account. Based on this fact, we
propose to capture this underlying structure by assuming a random walker over
the anchor points. We show that our method is a fast approximate learning
algorithm based on the diffusion map kernel. The experiments on various
datasets show that making different state-of-the-art coding algorithms aware of
this structure boosts them in different learning tasks.
On Fast Dropout and its Applicability to Recurrent Networks
Authors: Justin Bayer, Christian Osendorfer, Daniela Korhammer, Nutan Chen, Sebastian Urban, Patrick van der Smagt
Date: 2013-11-04 00:00:00

Recurrent Neural Networks (RNNs) are rich models for the processing of
sequential data. Recent work on advancing the state of the art has been focused
on the optimization or modelling of RNNs, mostly motivated by adressing the
problems of the vanishing and exploding gradients. The control of overfitting
has seen considerably less attention. This paper contributes to that by
analyzing fast dropout, a recent regularization method for generalized linear
models and neural networks from a back-propagation inspired perspective. We
show that fast dropout implements a quadratic form of an adaptive,
per-parameter regularizer, which rewards large weights in the light of
underfitting, penalizes them for overconfident predictions and vanishes at
minima of an unregularized training loss. The derivatives of that regularizer
are exclusively based on the training error signal. One consequence of this is
the absense of a global weight attractor, which is particularly appealing for
RNNs, since the dynamics are not biased towards a certain regime. We positively
test the hypothesis that this improves the performance of RNNs on four musical
data sets.
Electricity Market Forecasting via Low-Rank Multi-Kernel Learning
Authors: Vassilis Kekatos, Yu Zhang, Georgios B. Giannakis
Date: 2013-10-02 00:00:00
Tasks: Computational Efficiency

The smart grid vision entails advanced information technology and data
analytics to enhance the efficiency, sustainability, and economics of the power
grid infrastructure. Aligned to this end, modern statistical learning tools are
leveraged here for electricity market inference. Day-ahead price forecasting is
cast as a low-rank kernel learning problem. Uniquely exploiting the market
clearing process, congestion patterns are modeled as rank-one components in the
matrix of spatio-temporally varying prices. Through a novel nuclear norm-based
regularization, kernels across pricing nodes and hours can be systematically
selected. Even though market-wide forecasting is beneficial from a learning
perspective, it involves processing high-dimensional market data. The latter
becomes possible after devising a block-coordinate descent algorithm for
solving the non-convex optimization problem involved. The algorithm utilizes
results from block-sparse vector recovery and is guaranteed to converge to a
stationary point. Numerical tests on real data from the Midwest ISO (MISO)
market corroborate the prediction accuracy, computational efficiency, and the
interpretative merits of the developed approach over existing alternatives.
Latent Semantic Word Sense Disambiguation Using Global Co-occurrence Information
Authors: Minoru Sasaki
Date: 2014-03-05 00:00:00
Tasks: Word Sense Disambiguation

In this paper, I propose a novel word sense disambiguation method based on
the global co-occurrence information using NMF. When I calculate the dependency
relation matrix, the existing method tends to produce very sparse co-occurrence
matrix from a small training set. Therefore, the NMF algorithm sometimes does
not converge to desired solutions. To obtain a large number of co-occurrence
relations, I propose to use co-occurrence frequencies of dependency relations
between word features in the whole training set. This enables us to solve data
sparseness problem and induce more effective latent features. To evaluate the
efficiency of the method of word sense disambiguation, I make some experiments
to compare with the result of the two baseline methods. The results of the
experiments show this method is effective for word sense disambiguation in
comparison with the all baseline methods. Moreover, the proposed method is
effective for obtaining a stable effect by analyzing the global co-occurrence
information.
Is getting the right answer just about choosing the right words? The role of syntactically-informed features in short answer scoring
Authors: Derrick Higgins, Chris Brew, Michael Heilman, Ramon Ziai, Lei Chen, Aoife Cahill, Michael Flor, Nitin Madnani, Joel Tetreault, Daniel Blanchard, Diane Napolitano, Chong MIn Lee, John Blackmore
Date: 2014-03-04 00:00:00

Developments in the educational landscape have spurred greater interest in
the problem of automatically scoring short answer questions. A recent shared
task on this topic revealed a fundamental divide in the modeling approaches
that have been applied to this problem, with the best-performing systems split
between those that employ a knowledge engineering approach and those that
almost solely leverage lexical information (as opposed to higher-level
syntactic information) in assigning a score to a given response. This paper
aims to introduce the NLP community to the largest corpus currently available
for short-answer scoring, provide an overview of methods used in the shared
task using this data, and explore the extent to which more
syntactically-informed features can contribute to the short answer scoring task
in a way that avoids the question-specific manual effort of the knowledge
engineering approach.
Skolemization for Weighted First-Order Model Counting
Authors: Guy Van den Broeck, Wannes Meert, Adnan Darwiche
Date: 2013-12-19 00:00:00
Tasks: model

First-order model counting emerged recently as a novel reasoning task, at the
core of efficient algorithms for probabilistic logics. We present a
Skolemization algorithm for model counting problems that eliminates existential
quantifiers from a first-order logic theory without changing its weighted model
count. For certain subsets of first-order logic, lifted model counters were
shown to run in time polynomial in the number of objects in the domain of
discourse, where propositional model counters require exponential time.
However, these guarantees apply only to Skolem normal form theories (i.e., no
existential quantifiers) as the presence of existential quantifiers reduces
lifted model counters to propositional ones. Since textbook Skolemization is
not sound for model counting, these restrictions precluded efficient model
counting for directed models, such as probabilistic logic programs, which rely
on existential quantification. Our Skolemization procedure extends the
applicability of first-order model counters to these representations. Moreover,
it simplifies the design of lifted model counting algorithms.
Artificial Neuron Modelling Based on Wave Shape
Authors: Kieran Greer
Date: 2014-03-05 00:00:00

This paper describes a new model for an artificial neural network processing
unit or neuron. It is slightly different to a traditional feedforward network
by the fact that it favours a mechanism of trying to match the wave-like
'shape' of the input with the shape of the output against specific value error
corrections. The expectation is then that a best fit shape can be transposed
into the desired output values more easily. This allows for notions of
reinforcement through resonance and also the construction of synapses.
Fast methods for denoising matrix completion formulations, with applications to robust seismic data interpolation
Authors: Aleksandr Y. Aravkin, Rajiv Kumar, Hassan Mansour, Ben Recht, Felix J. Herrmann
Date: 2013-02-20 00:00:00
Tasks: Collaborative Filtering, Denoising, Matrix Completion

Recent SVD-free matrix factorization formulations have enabled rank
minimization for systems with millions of rows and columns, paving the way for
matrix completion in extremely large-scale applications, such as seismic data
interpolation.
  In this paper, we consider matrix completion formulations designed to hit a
target data-fitting error level provided by the user, and propose an algorithm
called LR-BPDN that is able to exploit factorized formulations to solve the
corresponding optimization problem. Since practitioners typically have strong
prior knowledge about target error level, this innovation makes it easy to
apply the algorithm in practice, leaving only the factor rank to be determined.
  Within the established framework, we propose two extensions that are highly
relevant to solving practical challenges of data interpolation. First, we
propose a weighted extension that allows known subspace information to improve
the results of matrix completion formulations. We show how this weighting can
be used in the context of frequency continuation, an essential aspect to
seismic data interpolation. Second, we propose matrix completion formulations
that are robust to large measurement errors in the available data.
  We illustrate the advantages of LR-BPDN on the collaborative filtering
problem using the MovieLens 1M, 10M, and Netflix 100M datasets. Then, we use
the new method, along with its robust and subspace re-weighted extensions, to
obtain high-quality reconstructions for large scale seismic interpolation
problems with real data, even in the presence of data contamination.
K-Tangent Spaces on Riemannian Manifolds for Improved Pedestrian Detection
Authors: Andres Sanin, Conrad Sanderson, Mehrtash T. Harandi, Brian C. Lovell
Date: 2014-03-05 00:00:00
Tasks: Pedestrian Detection

For covariance-based image descriptors, taking into account the curvature of
the corresponding feature space has been shown to improve discrimination
performance. This is often done through representing the descriptors as points
on Riemannian manifolds, with the discrimination accomplished on a tangent
space. However, such treatment is restrictive as distances between arbitrary
points on the tangent space do not represent true geodesic distances, and hence
do not represent the manifold structure accurately. In this paper we propose a
general discriminative model based on the combination of several tangent
spaces, in order to preserve more details of the structure. The model can be
used as a weak learner in a boosting-based pedestrian detection framework.
Experiments on the challenging INRIA and DaimlerChrysler datasets show that the
proposed model leads to considerably higher performance than methods based on
histograms of oriented gradients as well as previous Riemannian-based
techniques.
High-Accuracy Total Variation for Compressed Video Sensing
Authors: Mahdi S. Hosseini, Konstantinos N. Plataniotis
Date: 2013-09-01 00:00:00
Tasks: Image Restoration, Vocal Bursts Intensity Prediction

Numerous total variation (TV) regularizers, engaged in image restoration
problem, encode the gradients by means of simple $[-1,1]$ FIR filter. Despite
its low computational processing, this filter severely deviates signal's high
frequency components pertinent to edge/discontinuous information and cause
several deficiency issues known as texture and geometric loss. This paper
addresses this problem by proposing an alternative model to the TV
regularization problem via high order accuracy differential FIR filters to
preserve rapid transitions in signal recovery. A numerical encoding scheme is
designed to extend the TV model into multidimensional representation (tensorial
decomposition). We adopt this design to regulate the spatial and temporal
redundancy in compressed video sensing problem to jointly recover frames from
under-sampled measurements. We then seek the solution via alternating direction
methods of multipliers and find a unique solution to quadratic minimization
step with capability of handling different boundary conditions. The resulting
algorithm uses much lower sampling rate and highly outperforms alternative
state-of-the-art methods. This is evaluated both in terms of restoration
accuracy and visual quality of the recovered frames.
Exploiting the Statistics of Learning and Inference
Authors: Max Welling
Date: 2014-02-26 00:00:00

When dealing with datasets containing a billion instances or with simulations
that require a supercomputer to execute, computational resources become part of
the equation. We can improve the efficiency of learning and inference by
exploiting their inherent statistical nature. We propose algorithms that
exploit the redundancy of data relative to a model by subsampling data-cases
for every update and reasoning about the uncertainty created in this process.
In the context of learning we propose to test for the probability that a
stochastically estimated gradient points more than 180 degrees in the wrong
direction. In the context of MCMC sampling we use stochastic gradients to
improve the efficiency of MCMC updates, and hypothesis tests based on adaptive
mini-batches to decide whether to accept or reject a proposed parameter update.
Finally, we argue that in the context of likelihood free MCMC one needs to
store all the information revealed by all simulations, for instance in a
Gaussian process. We conclude that Bayesian methods will remain to play a
crucial role in the era of big data and big simulations, but only if we
overcome a number of computational challenges.
Dynamic stochastic blockmodels for time-evolving social networks
Authors: Kevin S. Xu, Alfred O. Hero III
Date: 2014-03-04 00:00:00

Significant efforts have gone into the development of statistical models for
analyzing data in the form of networks, such as social networks. Most existing
work has focused on modeling static networks, which represent either a single
time snapshot or an aggregate view over time. There has been recent interest in
statistical modeling of dynamic networks, which are observed at multiple points
in time and offer a richer representation of many complex phenomena. In this
paper, we present a state-space model for dynamic networks that extends the
well-known stochastic blockmodel for static networks to the dynamic setting. We
fit the model in a near-optimal manner using an extended Kalman filter (EKF)
augmented with a local search. We demonstrate that the EKF-based algorithm
performs competitively with a state-of-the-art algorithm based on Markov chain
Monte Carlo sampling but is significantly less computationally demanding.
One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling
Authors: Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, Phillipp Koehn, Tony Robinson
Date: 2013-12-11 00:00:00
Tasks: Language Modeling, Language Modelling

We propose a new benchmark corpus to be used for measuring progress in
statistical language modeling. With almost one billion words of training data,
we hope this benchmark will be useful to quickly evaluate novel language
modeling techniques, and to compare their contribution when combined with other
advanced techniques. We show performance of several well-known types of
language models, with the best results achieved with a recurrent neural network
based language model. The baseline unpruned Kneser-Ney 5-gram model achieves
perplexity 67.6; a combination of techniques leads to 35% reduction in
perplexity, or 10% reduction in cross-entropy (bits), over that baseline.
  The benchmark is available as a code.google.com project; besides the scripts
needed to rebuild the training/held-out data, it also makes available
log-probability values for each word in each of ten held-out data sets, for
each of the baseline n-gram models.