pwc-archive/papers-with-abstracts huggingface dataset:
A Fast Greedy Algorithm for Generalized Column Subset Selection
Authors: Ahmed K. Farahat, Ali Ghodsi, Mohamed S. Kamel
Date: 2013-12-24 00:00:00

This paper defines a generalized column subset selection problem which is
concerned with the selection of a few columns from a source matrix A that best
approximate the span of a target matrix B. The paper then proposes a fast
greedy algorithm for solving this problem and draws connections to different
problems that can be efficiently solved using the proposed algorithm.
Iterative Nearest Neighborhood Oversampling in Semisupervised Learning from Imbalanced Data
Authors: Fengqi Li, Chuang Yu, Nanhai Yang, Feng Xia, Guangming Li, Fatemeh Kaveh-Yazdy
Date: 2013-12-24 00:00:00
Tasks: General Classification, imbalanced classification

Transductive graph-based semi-supervised learning methods usually build an
undirected graph utilizing both labeled and unlabeled samples as vertices.
Those methods propagate label information of labeled samples to neighbors
through their edges in order to get the predicted labels of unlabeled samples.
Most popular semi-supervised learning approaches are sensitive to initial label
distribution happened in imbalanced labeled datasets. The class boundary will
be severely skewed by the majority classes in an imbalanced classification. In
this paper, we proposed a simple and effective approach to alleviate the
unfavorable influence of imbalance problem by iteratively selecting a few
unlabeled samples and adding them into the minority classes to form a balanced
labeled dataset for the learning methods afterwards. The experiments on UCI
datasets and MNIST handwritten digits dataset showed that the proposed approach
outperforms other existing state-of-art methods.
Suffix Stripping Problem as an Optimization Problem
Authors: B. P. Pande, Pawan Tamta, H. S. Dhami
Date: 2013-12-24 00:00:00
Tasks: Information Retrieval, Retrieval

Stemming or suffix stripping, an important part of the modern Information
Retrieval systems, is to find the root word (stem) out of a given cluster of
words. Existing algorithms targeting this problem have been developed in a
haphazard manner. In this work, we model this problem as an optimization
problem. An Integer Program is being developed to overcome the shortcomings of
the existing approaches. The sample results of the proposed method are also
being compared with an established technique in the field for English language.
An AMPL code for the same IP has also been given.
Comparative study of Authorship Identification Techniques for Cyber Forensics Analysis
Authors: Smita Nirkhi, R. V. Dharaskar
Date: 2013-12-24 00:00:00
Tasks: Authorship Attribution, Information Retrieval, Retrieval

Authorship Identification techniques are used to identify the most
appropriate author from group of potential suspects of online messages and find
evidences to support the conclusion. Cybercriminals make misuse of online
communication for sending blackmail or a spam email and then attempt to hide
their true identities to void detection.Authorship Identification of online
messages is the contemporary research issue for identity tracing in cyber
forensics. This is highly interdisciplinary area as it takes advantage of
machine learning, information retrieval, and natural language processing. In
this paper, a study of recent techniques and automated approaches to
attributing authorship of online messages is presented. The focus of this
review study is to summarize all existing authorship identification techniques
used in literature to identify authors of online messages. Also it discusses
evaluation criteria and parameters for authorship attribution studies and list
open questions that will attract future work in this area.
IVSS Integration of Color Feature Extraction Techniques for Intelligent Video Search Systems
Authors: Avinash N Bhute, B. B. Meshram
Date: 2013-12-24 00:00:00

As large amount of visual Information is available on web in form of images,
graphics, animations and videos, so it is important in internet era to have an
effective video search system. As there are number of video search engine
(blinkx, Videosurf, Google, YouTube, etc.) which search for relevant videos
based on user keyword or term, But very less commercial video search engine are
available which search videos based on visual image/clip/video. In this paper
we are recommending a system that will search for relevant video using color
feature of video in response of user Query.
A Subband-Based SVM Front-End for Robust ASR
Authors: Jibran Yousafzai, Zoran Cvetkovic, Peter Sollich, Matthew Ager
Date: 2013-12-24 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), General Classification, speech-recognition, Speech Recognition

This work proposes a novel support vector machine (SVM) based robust
automatic speech recognition (ASR) front-end that operates on an ensemble of
the subband components of high-dimensional acoustic waveforms. The key issues
of selecting the appropriate SVM kernels for classification in frequency
subbands and the combination of individual subband classifiers using ensemble
methods are addressed. The proposed front-end is compared with state-of-the-art
ASR front-ends in terms of robustness to additive noise and linear filtering.
Experiments performed on the TIMIT phoneme classification task demonstrate the
benefits of the proposed subband based SVM front-end: it outperforms the
standard cepstral front-end in the presence of noise and linear filtering for
signal-to-noise ratio (SNR) below 12-dB. A combination of the proposed
front-end with a conventional front-end such as MFCC yields further
improvements over the individual front ends across the full range of noise
levels.
Bounded Recursive Self-Improvement
Authors: E. Nivel, K. R. Thórisson, B. R. Steunebrink, H. Dindo, G. Pezzulo, M. Rodriguez, C. Hernandez, D. Ognibene, J. Schmidhuber, R. Sanz, H. P. Helgason, A. Chella, G. K. Jonsson
Date: 2013-12-24 00:00:00
Tasks: Scheduling

We have designed a machine that becomes increasingly better at behaving in
underspecified circumstances, in a goal-directed way, on the job, by modeling
itself and its environment as experience accumulates. Based on principles of
autocatalysis, endogeny, and reflectivity, the work provides an architectural
blueprint for constructing systems with high levels of operational autonomy in
underspecified circumstances, starting from a small seed. Through value-driven
dynamic priority scheduling controlling the parallel execution of a vast number
of reasoning threads, the system achieves recursive self-improvement after it
leaves the lab, within the boundaries imposed by its designers. A prototype
system has been implemented and demonstrated to learn a complex real-world
task, real-time multimodal dialogue with humans, by on-line observation. Our
work presents solutions to several challenges that must be solved for achieving
artificial general intelligence.
Bounded Rational Decision-Making in Changing Environments
Authors: Jordi Grau-Moya, Daniel A. Braun
Date: 2013-12-24 00:00:00
Tasks: Decision Making

A perfectly rational decision-maker chooses the best action with the highest
utility gain from a set of possible actions. The optimality principles that
describe such decision processes do not take into account the computational
costs of finding the optimal action. Bounded rational decision-making addresses
this problem by specifically trading off information-processing costs and
expected utility. Interestingly, a similar trade-off between energy and entropy
arises when describing changes in thermodynamic systems. This similarity has
been recently used to describe bounded rational agents. Crucially, this
framework assumes that the environment does not change while the decision-maker
is computing the optimal policy. When this requirement is not fulfilled, the
decision-maker will suffer inefficiencies in utility, that arise because the
current policy is optimal for an environment in the past. Here we borrow
concepts from non-equilibrium thermodynamics to quantify these inefficiencies
and illustrate with simulations its relationship with computational resources.
Time-Series Classification Through Histograms of Symbolic Polynomials
Authors: Josif Grabocka, Martin Wistuba, Lars Schmidt-Thieme
Date: 2013-07-24 00:00:00
Tasks: Classification, Econometrics, General Classification, Time Series, Time Series Analysis, Time Series Classification

Time-series classification has attracted considerable research attention due
to the various domains where time-series data are observed, ranging from
medicine to econometrics. Traditionally, the focus of time-series
classification has been on short time-series data composed of a unique pattern
with intraclass pattern distortions and variations, while recently there have
been attempts to focus on longer series composed of various local patterns.
This study presents a novel method which can detect local patterns in long
time-series via fitting local polynomial functions of arbitrary degrees. The
coefficients of the polynomial functions are converted to symbolic words via
equivolume discretizations of the coefficients' distributions. The symbolic
polynomial words enable the detection of similar local patterns by assigning
the same words to similar polynomials. Moreover, a histogram of the frequencies
of the words is constructed from each time-series' bag of words. Each row of
the histogram enables a new representation for the series and symbolize the
existence of local patterns and their frequencies. Experimental evidence
demonstrates outstanding results of our method compared to the state-of-art
baselines, by exhibiting the best classification accuracies in all the datasets
and having statistically significant improvements in the absolute majority of
experiments.
Invariant Factorization Of Time-Series
Authors: Josif Grabocka, Lars Schmidt-Thieme
Date: 2013-12-23 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Classification

Time-series classification is an important domain of machine learning and a
plethora of methods have been developed for the task. In comparison to existing
approaches, this study presents a novel method which decomposes a time-series
dataset into latent patterns and membership weights of local segments to those
patterns. The process is formalized as a constrained objective function and a
tailored stochastic coordinate descent optimization is applied. The time-series
are projected to a new feature representation consisting of the sums of the
membership weights, which captures frequencies of local patterns. Features from
various sliding window sizes are concatenated in order to encapsulate the
interaction of patterns from different sizes. Finally, a large-scale
experimental comparison against 6 state of the art baselines and 43 real life
datasets is conducted. The proposed method outperforms all the baselines with
statistically significant margins in terms of prediction accuracy.
A central limit theorem for scaled eigenvectors of random dot product graphs
Authors: Avanti Athreya, Vince Lyzinski, David J. Marchette, Carey E. Priebe, Daniel L. Sussman, Minh Tang
Date: 2013-05-31 00:00:00

We prove a central limit theorem for the components of the largest
eigenvectors of the adjacency matrix of a finite-dimensional random dot product
graph whose true latent positions are unknown. In particular, we follow the
methodology outlined in \citet{sussman2012universally} to construct consistent
estimates for the latent positions, and we show that the appropriately scaled
differences between the estimated and true latent positions converge to a
mixture of Gaussian random variables. As a corollary, we obtain a central limit
theorem for the first eigenvector of the adjacency matrix of an Erd\"os-Renyi
random graph.
Rounding Sum-of-Squares Relaxations
Authors: Boaz Barak, Jonathan Kelner, David Steurer
Date: 2013-12-23 00:00:00
Tasks: Open-Ended Question Answering

We present a general approach to rounding semidefinite programming
relaxations obtained by the Sum-of-Squares method (Lasserre hierarchy). Our
approach is based on using the connection between these relaxations and the
Sum-of-Squares proof system to transform a *combining algorithm* -- an
algorithm that maps a distribution over solutions into a (possibly weaker)
solution -- into a *rounding algorithm* that maps a solution of the relaxation
to a solution of the original problem.
  Using this approach, we obtain algorithms that yield improved results for
natural variants of three well-known problems:
  1) We give a quasipolynomial-time algorithm that approximates the maximum of
a low degree multivariate polynomial with non-negative coefficients over the
Euclidean unit sphere. Beyond being of interest in its own right, this is
related to an open question in quantum information theory, and our techniques
have already led to improved results in this area (Brand\~{a}o and Harrow, STOC
'13).
  2) We give a polynomial-time algorithm that, given a d dimensional subspace
of R^n that (almost) contains the characteristic function of a set of size n/k,
finds a vector $v$ in the subspace satisfying $|v|_4^4 > c(k/d^{1/3}) |v|_2^2$,
where $|v|_p = (E_i v_i^p)^{1/p}$. Aside from being a natural relaxation, this
is also motivated by a connection to the Small Set Expansion problem shown by
Barak et al. (STOC 2012) and our results yield a certain improvement for that
problem.
  3) We use this notion of L_4 vs. L_2 sparsity to obtain a polynomial-time
algorithm with substantially improved guarantees for recovering a planted
$\mu$-sparse vector v in a random d-dimensional subspace of R^n. If v has mu n
nonzero coordinates, we can recover it with high probability whenever $\mu <
O(\min(1,n/d^2))$, improving for $d < n^{2/3}$ prior methods which
intrinsically required $\mu < O(1/\sqrt(d))$.
Automated Coin Recognition System using ANN
Authors: Shatrughan Modi, Dr. Seema Bawa
Date: 2013-12-23 00:00:00

Coins are integral part of our day to day life. We use coins everywhere like
grocery store, banks, buses, trains etc. So it becomes a basic need that coins
can be sorted and counted automatically. For this it is necessary that coins
can be recognized automatically. In this paper we have developed an ANN
(Artificial Neural Network) based Automated Coin Recognition System for the
recognition of Indian Coins of denomination Rs. 1, 2, 5 and 10 with rotation
invariance. We have taken images from both sides of coin. So this system is
capable of recognizing coins from both sides. Features are extracted from
images using techniques of Hough Transformation, Pattern Averaging etc. Then,
the extracted features are passed as input to a trained Neural Network. 97.74%
recognition rate has been achieved during the experiments i.e. only 2.26% miss
recognition, which is quite encouraging.
A comprehensive review of firefly algorithms
Authors: Iztok Fister, Iztok Fister Jr., Xin-She Yang, Janez Brest
Date: 2013-12-23 00:00:00

The firefly algorithm has become an increasingly important tool of Swarm
Intelligence that has been applied in almost all areas of optimization, as well
as engineering practice. Many problems from various areas have been
successfully solved using the firefly algorithm and its variants. In order to
use the algorithm to solve diverse problems, the original firefly algorithm
needs to be modified or hybridized. This paper carries out a comprehensive
review of this living and evolving discipline of Swarm Intelligence, in order
to show that the firefly algorithm could be applied to every problem arising in
practice. On the other hand, it encourages new researchers and algorithm
developers to use this simple and yet very efficient algorithm for problem
solving. It often guarantees that the obtained results will meet the
expectations.
Using Latent Binary Variables for Online Reconstruction of Large Scale Systems
Authors: Victorin Martin, Jean-Marc Lasgouttes, Cyril Furtlehner
Date: 2013-12-23 00:00:00

We propose a probabilistic graphical model realizing a minimal encoding of
real variables dependencies based on possibly incomplete observation and an
empirical cumulative distribution function per variable. The target application
is a large scale partially observed system, like e.g. a traffic network, where
a small proportion of real valued variables are observed, and the other
variables have to be predicted. Our design objective is therefore to have good
scalability in a real-time setting. Instead of attempting to encode the
dependencies of the system directly in the description space, we propose a way
to encode them in a latent space of binary variables, reflecting a rough
perception of the observable (congested/non-congested for a traffic road). The
method relies in part on message passing algorithms, i.e. belief propagation,
but the core of the work concerns the definition of meaningful latent variables
associated to the variables of interest and their pairwise dependencies.
Numerical experiments demonstrate the applicability of the method in practice.
Image Processing based Systems and Techniques for the Recognition of Ancient and Modern Coins
Authors: Shatrughan Modi, Dr. Seema Bawa
Date: 2013-12-23 00:00:00

Coins are frequently used in everyday life at various places like in banks,
grocery stores, supermarkets, automated weighing machines, vending machines
etc. So, there is a basic need to automate the counting and sorting of coins.
For this machines need to recognize the coins very fast and accurately, as
further transaction processing depends on this recognition. Three types of
systems are available in the market: Mechanical method based systems,
Electromagnetic method based systems and Image processing based systems. This
paper presents an overview of available systems and techniques based on image
processing to recognize ancient and modern coins.
Predictive User Modeling with Actionable Attributes
Authors: Indre Zliobaite, Mykola Pechenizkiy
Date: 2013-12-23 00:00:00
Tasks: Attribute, Marketing

Different machine learning techniques have been proposed and used for
modeling individual and group user needs, interests and preferences. In the
traditional predictive modeling instances are described by observable
variables, called attributes. The goal is to learn a model for predicting the
target variable for unseen instances. For example, for marketing purposes a
company consider profiling a new user based on her observed web browsing
behavior, referral keywords or other relevant information. In many real world
applications the values of some attributes are not only observable, but can be
actively decided by a decision maker. Furthermore, in some of such applications
the decision maker is interested not only to generate accurate predictions, but
to maximize the probability of the desired outcome. For example, a direct
marketing manager can choose which type of a special offer to send to a client
(actionable attribute), hoping that the right choice will result in a positive
response with a higher probability. We study how to learn to choose the value
of an actionable attribute in order to maximize the probability of a desired
outcome in predictive modeling. We emphasize that not all instances are equally
sensitive to changes in actions. Accurate choice of an action is critical for
those instances, which are on the borderline (e.g. users who do not have a
strong opinion one way or the other). We formulate three supervised learning
approaches for learning to select the value of an actionable attribute at an
instance level. We also introduce a focused training procedure which puts more
emphasis on the situations where varying the action is the most likely to take
the effect. The proof of concept experimental validation on two real-world case
studies in web analytics and e-learning domains highlights the potential of the
proposed approaches.
The SP theory of intelligence: benefits and applications
Authors: J. Gerard Wolff
Date: 2013-06-13 00:00:00
Tasks: Descriptive, Medical Diagnosis

This article describes existing and expected benefits of the "SP theory of
intelligence", and some potential applications. The theory aims to simplify and
integrate ideas across artificial intelligence, mainstream computing, and human
perception and cognition, with information compression as a unifying theme. It
combines conceptual simplicity with descriptive and explanatory power across
several areas of computing and cognition. In the "SP machine" -- an expression
of the SP theory which is currently realized in the form of a computer model --
there is potential for an overall simplification of computing systems,
including software. The SP theory promises deeper insights and better solutions
in several areas of application including, most notably, unsupervised learning,
natural language processing, autonomous robots, computer vision, intelligent
databases, software engineering, information compression, medical diagnosis and
big data. There is also potential in areas such as the semantic web,
bioinformatics, structuring of documents, the detection of computer viruses,
data fusion, new kinds of computer, and the development of scientific theories.
The theory promises seamless integration of structures and functions within and
between different areas of application. The potential value, worldwide, of
these benefits and applications is at least $190 billion each year. Further
development would be facilitated by the creation of a high-parallel,
open-source version of the SP machine, available to researchers everywhere.
A Survey on Eye-Gaze Tracking Techniques
Authors: H. R. Chennamma, Xiaohui Yuan
Date: 2013-12-22 00:00:00
Tasks: Survey

Study of eye-movement is being employed in Human Computer Interaction (HCI)
research. Eye - gaze tracking is one of the most challenging problems in the
area of computer vision. The goal of this paper is to present a review of
latest research in this continued growth of remote eye-gaze tracking. This
overview includes the basic definitions and terminologies, recent advances in
the field and finally the need of future development in the field.
An Efficient Edge Detection Technique by Two Dimensional Rectangular Cellular Automata
Authors: Jahangir Mohammed, Deepak Ranjan Nayak
Date: 2013-12-22 00:00:00
Tasks: Edge Detection, Vocal Bursts Valence Prediction

This paper proposes a new pattern of two dimensional cellular automata linear
rules that are used for efficient edge detection of an image. Since cellular
automata is inherently parallel in nature, it has produced desired output
within a unit time interval. We have observed four linear rules among 512 total
linear rules of a rectangular cellular automata in adiabatic or reflexive
boundary condition that produces an optimal result. These four rules are
directly applied once to the images and produced edge detected output. We
compare our results with the existing edge detection algorithms and found that
our results shows better edge detection with an enhancement of edges.
Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning
Authors: François Denis, Mattias Gybels, Amaury Habrard
Date: 2013-12-21 00:00:00

Learning probabilistic models over strings is an important issue for many
applications. Spectral methods propose elegant solutions to the problem of
inferring weighted automata from finite samples of variable-length strings
drawn from an unknown target distribution. These methods rely on a singular
value decomposition of a matrix $H_S$, called the Hankel matrix, that records
the frequencies of (some of) the observed strings. The accuracy of the learned
distribution depends both on the quantity of information embedded in $H_S$ and
on the distance between $H_S$ and its mean $H_r$. Existing concentration bounds
seem to indicate that the concentration over $H_r$ gets looser with the size of
$H_r$, suggesting to make a trade-off between the quantity of used information
and the size of $H_r$. We propose new dimension-free concentration bounds for
several variants of Hankel matrices. Experiments demonstrate that these bounds
are tight and that they significantly improve existing bounds. These results
suggest that the concentration rate of the Hankel matrix around its mean does
not constitute an argument for limiting its size.