pwc-archive/papers-with-abstracts huggingface dataset:
Speech Enhancement Using Pitch Detection Approach For Noisy Environment
Authors: Rashmi Makhijani, Urmila Shrawankar, V. M. Thakare
Date: 2013-05-09 00:00:00
Tasks: Speech Enhancement, speech-recognition, Speech Recognition

Acoustical mismatch among training and testing phases degrades outstandingly
speech recognition results. This problem has limited the development of
real-world nonspecific applications, as testing conditions are highly variant
or even unpredictable during the training process. Therefore the background
noise has to be removed from the noisy speech signal to increase the signal
intelligibility and to reduce the listener fatigue. Enhancement techniques
applied, as pre-processing stages; to the systems remarkably improve
recognition results. In this paper, a novel approach is used to enhance the
perceived quality of the speech signal when the additive noise cannot be
directly controlled. Instead of controlling the background noise, we propose to
reinforce the speech signal so that it can be heard more clearly in noisy
environments. The subjective evaluation shows that the proposed method improves
perceptual quality of speech in various noisy environments. As in some cases
speaking may be more convenient than typing, even for rapid typists: many
mathematical symbols are missing from the keyboard but can be easily spoken and
recognized. Therefore, the proposed system can be used in an application
designed for mathematical symbol recognition (especially symbols not available
on the keyboard) in schools.
Inferring Team Strengths Using a Discrete Markov Random Field
Authors: John Zech, Frank Wood
Date: 2013-05-09 00:00:00

We propose an original model for inferring team strengths using a Markov
Random Field, which can be used to generate historical estimates of the
offensive and defensive strengths of a team over time. This model was designed
to be applied to sports such as soccer or hockey, in which contest outcomes
take value in a limited discrete space. We perform inference using a
combination of Expectation Maximization and Loopy Belief Propagation. The
challenges of working with a non-convex optimization problem and a
high-dimensional parameter space are discussed. The performance of the model is
demonstrated on professional soccer data from the English Premier League.
On the universality of cognitive tests
Authors: David L. Dowe, Jose Hernandez-Orallo
Date: 2013-05-09 00:00:00
Tasks: Diversity

The analysis of the adaptive behaviour of many different kinds of systems
such as humans, animals and machines, requires more general ways of assessing
their cognitive abilities. This need is strengthened by increasingly more tasks
being analysed for and completed by a wider diversity of systems, including
swarms and hybrids. The notion of universal test has recently emerged in the
context of machine intelligence evaluation as a way to define and use the same
cognitive test for a variety of systems, using some principled tasks and
adapting the interface to each particular subject. However, how far can
universal tests be taken? This paper analyses this question in terms of
subjects, environments, space-time resolution, rewards and interfaces. This
leads to a number of findings, insights and caveats, according to several
levels where universal tests may be progressively more difficult to conceive,
implement and administer. One of the most significant contributions is given by
the realisation that more universal tests are defined as maximisations of less
universal tests for a variety of configurations. This means that universal
tests must be necessarily adaptive.
An Improved Three-Weight Message-Passing Algorithm
Authors: Nate Derbinsky, José Bento, Veit Elser, Jonathan S. Yedidia
Date: 2013-05-08 00:00:00

We describe how the powerful "Divide and Concur" algorithm for constraint
satisfaction can be derived as a special case of a message-passing version of
the Alternating Direction Method of Multipliers (ADMM) algorithm for convex
optimization, and introduce an improved message-passing algorithm based on
ADMM/DC by introducing three distinct weights for messages, with "certain" and
"no opinion" weights, as well as the standard weight used in ADMM/DC. The
"certain" messages allow our improved algorithm to implement constraint
propagation as a special case, while the "no opinion" messages speed
convergence for some problems by making the algorithm focus only on active
constraints. We describe how our three-weight version of ADMM/DC can give
greatly improved performance for non-convex problems such as circle packing and
solving large Sudoku puzzles, while retaining the exact performance of ADMM for
convex problems. We also describe the advantages of our algorithm compared to
other message-passing algorithms based upon belief propagation.
Kernelized Bayesian Matrix Factorization
Authors: Mehmet Gönen, Suleiman A. Khan, Samuel Kaski
Date: 2012-11-06 00:00:00
Tasks: Recommendation Systems

We extend kernelized matrix factorization with a fully Bayesian treatment and
with an ability to work with multiple side information sources expressed as
different kernels. Kernel functions have been introduced to matrix
factorization to integrate side information about the rows and columns (e.g.,
objects and users in recommender systems), which is necessary for making
out-of-matrix (i.e., cold start) predictions. We discuss specifically bipartite
graph inference, where the output matrix is binary, but extensions to more
general matrices are straightforward. We extend the state of the art in two key
aspects: (i) A fully conjugate probabilistic formulation of the kernelized
matrix factorization problem enables an efficient variational approximation,
whereas fully Bayesian treatments are not computationally feasible in the
earlier approaches. (ii) Multiple side information sources are included,
treated as different kernels in multiple kernel learning that additionally
reveals which side information sources are informative. Our method outperforms
alternatives in predicting drug-protein interactions on two data sets. We then
show that our framework can also be used for solving multilabel learning
problems by considering samples and labels as the two domains where matrix
factorization operates on. Our algorithm obtains the lowest Hamming loss values
on 10 out of 14 multilabel classification data sets compared to five
state-of-the-art multilabel learning algorithms.
The Dynamically Extended Mind -- A Minimal Modeling Case Study
Authors: Tom Froese, Carlos Gershenson, David A. Rosenblueth
Date: 2013-05-08 00:00:00

The extended mind hypothesis has stimulated much interest in cognitive
science. However, its core claim, i.e. that the process of cognition can extend
beyond the brain via the body and into the environment, has been heavily
criticized. A prominent critique of this claim holds that when some part of the
world is coupled to a cognitive system this does not necessarily entail that
the part is also constitutive of that cognitive system. This critique is known
as the "coupling-constitution fallacy". In this paper we respond to this
reductionist challenge by using an evolutionary robotics approach to create a
minimal model of two acoustically coupled agents. We demonstrate how the
interaction process as a whole has properties that cannot be reduced to the
contributions of the isolated agents. We also show that the neural dynamics of
the coupled agents has formal properties that are inherently impossible for
those neural networks in isolation. By keeping the complexity of the model to
an absolute minimum, we are able to illustrate how the coupling-constitution
fallacy is in fact based on an inadequate understanding of the constitutive
role of nonlinear interactions in dynamical systems theory.
Semantic-based Anomalous Pattern Discovery in Moving Object Trajectories
Authors: Elena Camossi, Paola Villa, Luca Mazzola
Date: 2013-05-08 00:00:00
Tasks: Object, Retrieval

In this work, we investigate a novel semantic approach for pattern discovery
in trajectories that, relying on ontologies, enhances object movement
information with event semantics. The approach can be applied to the detection
of movement patterns and behaviors whenever the semantics of events occurring
along the trajectory is, explicitly or implicitly, available. In particular, we
tested it against an exacting case scenario in maritime surveillance, i.e., the
discovery of suspicious container transportations.
  The methodology we have developed entails the formalization of the
application domain through a domain ontology, extending the Moving Object
Ontology (MOO) described in this paper. Afterwards, movement patterns have to
be formalized, either as Description Logic (DL) axioms or queries, enabling the
retrieval of the trajectories that follow the patterns.
  In our experimental evaluation, we have considered a real world dataset of 18
Million of container events describing the deed undertaken in a port to
accomplish the shipping (e.g., loading on a vessel, export operation).
Leveraging events, we have reconstructed almost 300 thousand container
trajectories referring to 50 thousand containers travelling along three years.
We have formalized the anomalous itinerary patterns as DL axioms, testing
different ontology APIs and DL reasoners to retrieve the suspicious
transportations.
  Our experiments demonstrate that the approach is feasible and efficient. In
particular, the joint use of Pellet and SPARQL-DL enables to detect the
trajectories following a given pattern in a reasonable time with big size
datasets.
Consistency of Online Random Forests
Authors: Misha Denil, David Matheson, Nando de Freitas
Date: 2013-02-20 00:00:00

As a testament to their success, the theory of random forests has long been
outpaced by their application in practice. In this paper, we take a step
towards narrowing this gap by providing a consistency result for online random
forests.
Gaussian process regression as a predictive model for Quality-of-Service in Web service systems
Authors: Jakub M. Tomczak, Jerzy Swiatek, Krzysztof Latawiec
Date: 2012-07-30 00:00:00
Tasks: General Classification, regression

In this paper, we present the Gaussian process regression as the predictive
model for Quality-of-Service (QoS) attributes in Web service systems. The goal
is to predict performance of the execution system expressed as QoS attributes
given existing execution system, service repository, and inputs, e.g., streams
of requests. In order to evaluate the performance of Gaussian process
regression the simulation environment was developed. Two quality indexes were
used, namely, Mean Absolute Error and Mean Squared Error. The results obtained
within the experiment show that the Gaussian process performed the best with
linear kernel and statistically significantly better comparing to
Classification and Regression Trees (CART) method.
Standard Fingerprint Databases: Manual Minutiae Labeling and Matcher Performance Analyses
Authors: Mehmet Kayaoglu, Berkay Topcu, Umut Uludag
Date: 2013-05-07 00:00:00
Tasks: fingerprint verification

Fingerprint verification and identification algorithms based on minutiae
features are used in many biometric systems today (e.g., governmental e-ID
programs, border control, AFIS, personal authentication for portable devices).
Researchers in industry/academia are now able to utilize many publicly
available fingerprint databases (e.g., Fingerprint Verification Competition
(FVC) & NIST databases) to compare/evaluate their feature extraction and/or
matching algorithm performances against those of others. The results from these
evaluations are typically utilized by decision makers responsible for
implementing the cited biometric systems, in selecting/tuning specific sensors,
feature extractors and matchers. In this study, for a subset of the cited
public fingerprint databases, we report fingerprint minutiae matching results,
which are based on (i) minutiae extracted automatically from fingerprint
images, and (ii) minutiae extracted manually by human subjects. By doing so, we
are able to (i) quantitatively judge the performance differences between these
two cases, (ii) elaborate on performance upper bounds of minutiae matching,
utilizing what can be termed as "ground truth" minutiae features, (iii) analyze
minutiae matching performance, without coupling it with the minutiae extraction
performance beforehand. Further, as we will freely distribute the minutiae
templates, originating from this manual labeling study, in a standard minutiae
template exchange format (ISO 19794-2), we believe that other researchers in
the biometrics community will be able to utilize the associated results &
templates to create their own evaluations pertaining to their fingerprint
minutiae extractors/matchers.
Speech: A Challenge to Digital Signal Processing Technology for Human-to-Computer Interaction
Authors: Urmila Shrawankar, Anjali Mahajan
Date: 2013-05-08 00:00:00
Tasks: Speech Synthesis, Speech-to-Text, text-to-speech, Text to Speech

This software project based paper is for a vision of the near future in which
computer interaction is characterized by natural face-to-face conversations
with lifelike characters that speak, emote, and gesture. The first step is
speech. The dream of a true virtual reality, a complete human-computer
interaction system will not come true unless we try to give some perception to
machine and make it perceive the outside world as humans communicate with each
other. This software project is under development for listening and replying
machine (Computer) through speech. The Speech interface is developed to convert
speech input into some parametric form (Speech-to-Text) for further processing
and the results, text output to speech synthesis (Text-to-Speech)
Class Imbalance Problem in Data Mining Review
Authors: Rushi Longadge, Snehalata Dongre
Date: 2013-05-08 00:00:00
Tasks: Classification, feature selection, General Classification

In last few years there are major changes and evolution has been done on
classification of data. As the application area of technology is increases the
size of data also increases. Classification of data becomes difficult because
of unbounded size and imbalance nature of data. Class imbalance problem become
greatest issue in data mining. Imbalance problem occur where one of the two
classes having more sample than other classes. The most of algorithm are more
focusing on classification of major sample while ignoring or misclassifying
minority sample. The minority samples are those that rarely occur but very
important. There are different methods available for classification of
imbalance data set which is divided into three main categories, the algorithmic
approach, data-preprocessing approach and feature selection approach. Each of
this technique has their own advantages and disadvantages. In this paper
systematic study of each approach is define which gives the right direction for
research in class imbalance problem.
The Extended Parameter Filter
Authors: Yusuf Erol, Lei LI, Bharath Ramsundar, Stuart J. Russell
Date: 2013-05-08 00:00:00

The parameters of temporal models, such as dynamic Bayesian networks, may be
modelled in a Bayesian context as static or atemporal variables that influence
transition probabilities at every time step. Particle filters fail for models
that include such variables, while methods that use Gibbs sampling of parameter
variables may incur a per-sample cost that grows linearly with the length of
the observation sequence. Storvik devised a method for incremental computation
of exact sufficient statistics that, for some cases, reduces the per-sample
cost to a constant. In this paper, we demonstrate a connection between
Storvik's filter and a Kalman filter in parameter space and establish more
general conditions under which Storvik's filter works. Drawing on an analogy to
the extended Kalman filter, we develop and analyze, both theoretically and
experimentally, a Taylor approximation to the parameter posterior that allows
Storvik's method to be applied to a broader class of models. Our experiments on
both synthetic examples and real applications show improvement over existing
methods.
Unsatisfiable Cores for Constraint Programming
Authors: Nicholas Downing, Thibaut Feydy, Peter J. Stuckey
Date: 2013-05-08 00:00:00

Constraint Programming (CP) solvers typically tackle optimization problems by
repeatedly finding solutions to a problem while placing tighter and tighter
bounds on the solution cost. This approach is somewhat naive, especially for
soft-constraint optimization problems in which the soft constraints are mostly
satisfied. Unsatisfiable-core approaches to solving soft constraint problems in
Boolean Satisfiability (e.g. MAXSAT) force all soft constraints to hold
initially. When solving fails they return an unsatisfiable core, as a set of
soft constraints that cannot hold simultaneously. Using this information the
problem is relaxed to allow certain soft constraint(s) to be violated and
solving continues. Since Lazy Clause Generation (LCG) solvers can also return
unsatisfiable cores we can adapt the MAXSAT unsatisfiable core approach to CP.
We implement the original MAXSAT unsatisfiable core solving algorithms WPM1,
MSU3 in a state-of-the-art LCG solver and show that there exist problems which
benefit from this hybrid approach.
Automatic Segmentation of Fluorescence Lifetime Microscopy Images of Cells Using Multi-Resolution Community Detection
Authors: Dandan Hu, Pinaki Sarder, Peter Ronhovde, Sandra Orthaus, Samuel Achilefu, Zohar Nussinov
Date: 2012-08-23 00:00:00
Tasks: Clustering, Community Detection, Image Segmentation, Segmentation, Semantic Segmentation

We have developed an automatic method for segmenting fluorescence lifetime
(FLT) imaging microscopy (FLIM) images of cells inspired by a multi-resolution
community detection (MCD) based network segmentation method. The image
processing problem is framed as identifying segments with respective average
FLTs against a background in FLIM images. The proposed method segments a FLIM
image for a given resolution of the network composed using image pixels as the
nodes and similarity between the pixels as the edges. In the resulting
segmentation, low network resolution leads to larger segments and high network
resolution leads to smaller segments. Further, the mean-square error (MSE) in
estimating the FLT segments in a FLIM image using the proposed method was found
to be consistently decreasing with increasing resolution of the corresponding
network. The proposed MCD method outperformed a popular spectral clustering
based method in performing FLIM image segmentation. The spectral segmentation
method introduced noisy segments in its output at high resolution. It was
unable to offer a consistent decrease in MSE with increasing resolution.
Minimizing inter-subject variability in fNIRS based Brain Computer Interfaces via multiple-kernel support vector learning
Authors: Berdakh Abibullaev, Jinung An, Seung-Hyun Lee, Sang-Hyeon Jin, Jeon-Il Moon
Date: 2012-09-25 00:00:00
Tasks: Brain Computer Interface

Brain signal variability in the measurements obtained from different subjects
during different sessions significantly deteriorates the accuracy of most
brain-computer interface (BCI) systems. Moreover these variabilities, also
known as inter-subject or inter-session variabilities, require lengthy
calibration sessions before the BCI system can be used. Furthermore, the
calibration session has to be repeated for each subject independently and
before use of the BCI due to the inter-session variability. In this study, we
present an algorithm in order to minimize the above-mentioned variabilities and
to overcome the time-consuming and usually error-prone calibration time. Our
algorithm is based on linear programming support-vector machines and their
extensions to a multiple kernel learning framework. We tackle the inter-subject
or -session variability in the feature spaces of the classifiers. This is done
by incorporating each subject- or session-specific feature spaces into much
richer feature spaces with a set of optimal decision boundaries. Each decision
boundary represents the subject- or a session specific spatio-temporal
variabilities of neural signals. Consequently, a single classifier with
multiple feature spaces will generalize well to new unseen test patterns even
without the calibration steps. We demonstrate that classifiers maintain good
performances even under the presence of a large degree of BCI variability. The
present study analyzes BCI variability related to oxy-hemoglobin neural signals
measured using a functional near-infrared spectroscopy.
High Level Pattern Classification via Tourist Walks in Networks
Authors: Thiago Christiano Silva, Liang Zhao
Date: 2013-05-07 00:00:00
Tasks: Classification, General Classification, Vocal Bursts Intensity Prediction

Complex networks refer to large-scale graphs with nontrivial connection
patterns. The salient and interesting features that the complex network study
offer in comparison to graph theory are the emphasis on the dynamical
properties of the networks and the ability of inherently uncovering pattern
formation of the vertices. In this paper, we present a hybrid data
classification technique combining a low level and a high level classifier. The
low level term can be equipped with any traditional classification techniques,
which realize the classification task considering only physical features (e.g.,
geometrical or statistical features) of the input data. On the other hand, the
high level term has the ability of detecting data patterns with semantic
meanings. In this way, the classification is realized by means of the
extraction of the underlying network's features constructed from the input
data. As a result, the high level classification process measures the
compliance of the test instances with the pattern formation of the training
data. Out of various high level perspectives that can be utilized to capture
semantic meaning, we utilize the dynamical features that are generated from a
tourist walker in a networked environment. Specifically, a weighted combination
of transient and cycle lengths generated by the tourist walk is employed for
that end. Interestingly, our study shows that the proposed technique is able to
further improve the already optimized performance of traditional classification
techniques.
Image Similarity Using Sparse Representation and Compression Distance
Authors: Tanaya Guha, Rabab K. Ward
Date: 2012-06-12 00:00:00
Tasks: Clustering, Image Clustering, Retrieval

A new line of research uses compression methods to measure the similarity
between signals. Two signals are considered similar if one can be compressed
significantly when the information of the other is known. The existing
compression-based similarity methods, although successful in the discrete one
dimensional domain, do not work well in the context of images. This paper
proposes a sparse representation-based approach to encode the information
content of an image using information from the other image, and uses the
compactness (sparsity) of the representation as a measure of its
compressibility (how much can the image be compressed) with respect to the
other image. The more sparse the representation of an image, the better it can
be compressed and the more it is similar to the other image. The efficacy of
the proposed measure is demonstrated through the high accuracies achieved in
image clustering, retrieval and classification.