pwc-archive/papers-with-abstracts huggingface dataset:
Interpretable Low-Rank Document Representations with Label-Dependent Sparsity Patterns
Authors: Ivan Ivek
Date: 2014-07-25 00:00:00
Tasks: Document Classification, General Classification

In context of document classification, where in a corpus of documents their
label tags are readily known, an opportunity lies in utilizing label
information to learn document representation spaces with better discriminative
properties. To this end, in this paper application of a Variational Bayesian
Supervised Nonnegative Matrix Factorization (supervised vbNMF) with
label-driven sparsity structure of coefficients is proposed for learning of
discriminative nonsubtractive latent semantic components occuring in TF-IDF
document representations. Constraints are such that the components pursued are
made to be frequently occuring in a small set of labels only, making it
possible to yield document representations with distinctive label-specific
sparse activation patterns. A simple measure of quality of this kind of
sparsity structure, dubbed inter-label sparsity, is introduced and
experimentally brought into tight connection with classification performance.
Representing a great practical convenience, inter-label sparsity is shown to be
easily controlled in supervised vbNMF by a single parameter.
Substitute Based SCODE Word Embeddings in Supervised NLP Tasks
Authors: Volkan Cirik, Deniz Yuret
Date: 2014-07-25 00:00:00
Tasks: Chunking, Dependency Parsing, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Word Embeddings

We analyze a word embedding method in supervised tasks. It maps words on a
sphere such that words co-occurring in similar contexts lie closely. The
similarity of contexts is measured by the distribution of substitutes that can
fill them. We compared word embeddings, including more recent representations,
in Named Entity Recognition (NER), Chunking, and Dependency Parsing. We examine
our framework in multilingual dependency parsing as well. The results show that
the proposed method achieves as good as or better results compared to the other
word embeddings in the tasks we investigate. It achieves state-of-the-art
results in multilingual dependency parsing. Word embeddings in 7 languages are
available for public use.
Curved Gabor Filters for Fingerprint Image Enhancement
Authors: Carsten Gottschlich
Date: 2011-04-21 00:00:00
Tasks: Image Enhancement

Gabor filters play an important role in many application areas for the
enhancement of various types of images and the extraction of Gabor features.
For the purpose of enhancing curved structures in noisy images, we introduce
curved Gabor filters which locally adapt their shape to the direction of flow.
These curved Gabor filters enable the choice of filter parameters which
increase the smoothing power without creating artifacts in the enhanced image.
In this paper, curved Gabor filters are applied to the curved ridge and valley
structure of low-quality fingerprint images. First, we combine two orientation
field estimation methods in order to obtain a more robust estimation for very
noisy images. Next, curved regions are constructed by following the respective
local orientation and they are used for estimating the local ridge frequency.
Lastly, curved Gabor filters are defined based on curved regions and they are
applied for the enhancement of low-quality fingerprint images. Experimental
results on the FVC2004 databases show improvements of this approach in
comparison to state-of-the-art enhancement methods.
Optimizing Auto-correlation for Fast Target Search in Large Search Space
Authors: Arif Mahmood, Ajmal Mian, Robyn Owens
Date: 2014-07-14 00:00:00
Tasks: Template Matching

In remote sensing image-blurring is induced by many sources such as
atmospheric scatter, optical aberration, spatial and temporal sensor
integration. The natural blurring can be exploited to speed up target search by
fast template matching. In this paper, we synthetically induce additional
non-uniform blurring to further increase the speed of the matching process. To
avoid loss of accuracy, the amount of synthetic blurring is varied spatially
over the image according to the underlying content. We extend transitive
algorithm for fast template matching by incorporating controlled image blur. To
this end we propose an Efficient Group Size (EGS) algorithm which minimizes the
number of similarity computations for a particular search image. A larger
efficient group size guarantees less computations and more speedup. EGS
algorithm is used as a component in our proposed Optimizing auto-correlation
(OptA) algorithm. In OptA a search image is iteratively non-uniformly blurred
while ensuring no accuracy degradation at any image location. In each iteration
efficient group size and overall computations are estimated by using the
proposed EGS algorithm. The OptA algorithm stops when the number of
computations cannot be further decreased without accuracy degradation. The
proposed algorithm is compared with six existing state of the art exhaustive
accuracy techniques using correlation coefficient as the similarity measure.
Experiments on satellite and aerial image datasets demonstrate the
effectiveness of the proposed algorithm.
Enhancing the Accuracy of Biometric Feature Extraction Fusion Using Gabor Filter and Mahalanobis Distance Algorithm
Authors: Ayodeji S. Makinde, Yaw Nkansah-Gyekye, Loserian S. Laizer
Date: 2014-07-24 00:00:00
Tasks: Dimensionality Reduction, Image Enhancement

Biometric recognition systems have advanced significantly in the last decade
and their use in specific applications will increase in the near future. The
ability to conduct meaningful comparisons and assessments will be crucial to
successful deployment and increasing biometric adoption. The best modality used
as unimodal biometric systems are unable to fully address the problem of higher
recognition rate. Multimodal biometric systems are able to mitigate some of the
limitations encountered in unimodal biometric systems, such as
non-universality, distinctiveness, non-acceptability, noisy sensor data, spoof
attacks, and performance. More reliable recognition accuracy and performance
are achievable as different modalities were being combined together and
different algorithms or techniques were being used. The work presented in this
paper focuses on a bimodal biometric system using face and fingerprint. An
image enhancement technique (histogram equalization) is used to enhance the
face and fingerprint images. Salient features of the face and fingerprint were
extracted using the Gabor filter technique. A dimensionality reduction
technique was carried out on both images extracted features using a principal
component analysis technique. A feature level fusion algorithm (Mahalanobis
distance technique) is used to combine each unimodal feature together. The
performance of the proposed approach is validated and is effective.
Trainable and Dynamic Computing: Error Backpropagation through Physical Media
Authors: Michiel Hermans, Michaël Burm, Joni Dambre, Peter Bienstman
Date: 2014-07-24 00:00:00
Tasks: speech-recognition, Speech Recognition

Machine learning algorithms, and more in particular neural networks, arguably
experience a revolution in terms of performance. Currently, the best systems we
have for speech recognition, computer vision and similar problems are based on
neural networks, trained using the half-century old backpropagation algorithm.
Despite the fact that neural networks are a form of analog computers, they are
still implemented digitally for reasons of convenience and availability. In
this paper we demonstrate how we can design physical linear dynamic systems
with non-linear feedback as a generic platform for dynamic, neuro-inspired
analog computing. We show that a crucial advantage of this setup is that the
error backpropagation can be performed physically as well, which greatly speeds
up the optimisation process. As we show in this paper, using one experimentally
validated and one conceptual example, such systems may be the key to providing
a relatively straightforward mechanism for constructing highly scalable, fully
dynamic analog computers.
Ubic: Bridging the gap between digital cryptography and the physical world
Authors: Mark Simkin, Dominique Schroeder, Andreas Bulling, Mario Fritz
Date: 2014-03-06 00:00:00

Advances in computing technology increasingly blur the boundary between the
digital domain and the physical world. Although the research community has
developed a large number of cryptographic primitives and has demonstrated their
usability in all-digital communication, many of them have not yet made their
way into the real world due to usability aspects. We aim to make another step
towards a tighter integration of digital cryptography into real world
interactions. We describe Ubic, a framework that allows users to bridge the gap
between digital cryptography and the physical world. Ubic relies on
head-mounted displays, like Google Glass, resource-friendly computer vision
techniques as well as mathematically sound cryptographic primitives to provide
users with better security and privacy guarantees. The framework covers key
cryptographic primitives, such as secure identification, document verification
using a novel secure physical document format, as well as content hiding. To
make a contribution of practical value, we focused on making Ubic as simple,
easily deployable, and user friendly as possible.
Convolutional Neural Associative Memories: Massive Capacity with Noise Tolerance
Authors: Amin Karbasi, Amir Hesam Salavati, Amin Shokrollahi
Date: 2014-07-24 00:00:00
Tasks: Retrieval

The task of a neural associative memory is to retrieve a set of previously
memorized patterns from their noisy versions using a network of neurons. An
ideal network should have the ability to 1) learn a set of patterns as they
arrive, 2) retrieve the correct patterns from noisy queries, and 3) maximize
the pattern retrieval capacity while maintaining the reliability in responding
to queries. The majority of work on neural associative memories has focused on
designing networks capable of memorizing any set of randomly chosen patterns at
the expense of limiting the retrieval capacity. In this paper, we show that if
we target memorizing only those patterns that have inherent redundancy (i.e.,
belong to a subspace), we can obtain all the aforementioned properties. This is
in sharp contrast with the previous work that could only improve one or two
aspects at the expense of the third. More specifically, we propose framework
based on a convolutional neural network along with an iterative algorithm that
learns the redundancy among the patterns. The resulting network has a retrieval
capacity that is exponential in the size of the network. Moreover, the
asymptotic error correction performance of our network is linear in the size of
the patterns. We then ex- tend our approach to deal with patterns lie
approximately in a subspace. This extension allows us to memorize datasets
containing natural patterns (e.g., images). Finally, we report experimental
results on both synthetic and real datasets to support our claims.
Nonlinear Quality of Life Index
Authors: A. Zinovyev, A. N. Gorban
Date: 2010-08-24 00:00:00

We present details of the analysis of the nonlinear quality of life index for
171 countries. This index is based on four indicators: GDP per capita by
Purchasing Power Parities, Life expectancy at birth, Infant mortality rate, and
Tuberculosis incidence. We analyze the structure of the data in order to find
the optimal and independent on expert's opinion way to map several numerical
indicators from a multidimensional space onto the one-dimensional space of the
quality of life. In the 4D space we found a principal curve that goes "through
the middle" of the dataset and project the data points on this curve. The order
along this principal curve gives us the ranking of countries. Projection onto
the principal curve provides a solution to the classical problem of
unsupervised ranking of objects. It allows us to find the independent on
expert's opinion way to project several numerical indicators from a
multidimensional space onto the one-dimensional space of the index values. This
projection is, in some sense, optimal and preserves as much information as
possible. For computation we used ViDaExpert, a tool for visualization and
analysis of multidimensional vectorial data (arXiv:1406.5550).
New Method for Optimization of License Plate Recognition system with Use of Edge Detection and Connected Component
Authors: Reza Azad, Hamid Reza Shayegh
Date: 2014-07-24 00:00:00
Tasks: Edge Detection, License Plate Recognition, Management

License Plate recognition plays an important role on the traffic monitoring
and parking management systems. In this paper, a fast and real time method has
been proposed which has an appropriate application to find tilt and poor
quality plates. In the proposed method, at the beginning, the image is
converted into binary mode using adaptive threshold. Then, by using some edge
detection and morphology operations, plate number location has been specified.
Finally, if the plat has tilt, its tilt is removed away. This method has been
tested on another paper data set that has different images of the background,
considering distance, and angel of view so that the correct extraction rate of
plate reached at 98.66%.
Novel and Tuneable Method for Skin Detection Based on Hybrid Color Space and Color Statistical Features
Authors: Reza Azad, Hamid Reza Shayegh
Date: 2014-07-24 00:00:00
Tasks: Face Detection

Skin detection is one of the most important and primary stages in some of
image processing applications such as face detection and human tracking. So
far, many approaches are proposed to done this case. Near all of these methods
have tried to find best match intensity distribution with skin pixels based on
popular color spaces such as RGB, CMYK or YCbCr. Results show these methods
cannot provide an accurate approach for every kinds of skin. In this paper, an
approach is proposed to solve this problem using statistical features
technique. This approach is including two stages. In the first one, from pure
skin statistical features were extracted and at the second stage, the skin
pixels are detected using HSV and YCbCr color spaces. In the result part, the
proposed approach is applied on FEI database and the accuracy rate reached
99.25 + 0.2. Further proposed method is applied on complex background database
and accuracy rate obtained 95.40+0.31%. The proposed approach can be used for
all kinds of skin using train stage which is the main advantages of it. Low
noise sensitivity and low computational complexity are some of other
advantages.
Real-Time and Efficient Method for Accuracy Enhancement of Edge Based License Plate Recognition System
Authors: Reza Azad, Babak Azad, Hamid Reza Shayegh
Date: 2014-07-24 00:00:00
Tasks: Edge Detection, License Plate Recognition, Management

License Plate Recognition plays an important role on the traffic monitoring
and parking management. Administration and restriction of those transportation
tools for their better service becomes very essential. In this paper, a fast
and real time method has an appropriate application to find plates that the
plat has tilt and the picture quality is poor. In the proposed method, at the
beginning, the image is converted into binary mode with use of adaptive
threshold. And with use of edge detection and morphology operation, plate
number location has been specified and if the plat has tilt; its tilt is
removed away. Then its characters are distinguished using image processing
techniques. Finally, K Nearest Neighbour (KNN) classifier was used for
character recognition. This method has been tested on available data set that
has different images of the background, considering distance, and angel of view
so that the correct extraction rate of plate reached at 98% and character
recognition rate achieved at 99.12%. Further we tested our character
recognition stage on Persian vehicle data set and we achieved 99% correct
recognition rate.
Pattern-wave model of brain. Mechanisms of information processing, memory organization
Authors: Alexey Redozubov
Date: 2014-06-26 00:00:00

The structure of the axon-dendrite connections of neurons of the brain
creates a rich spatial structure in which provided various combinations of
signals surrounding neurons. Structure of dendritic trees and shape of
dendritic spines allow repeatedly increase combinatorial component through
cross synapses influence neighboring neurons. In this paper it is shown that
the diffuse spreading of neurotransmitters allows neurons to detect and
remember significant set of environmental activity patterns. As a core element
fixation described extrasynaptic metabotropic receptive clusters. The described
mechanism leads to the appearance of wave processes, based on the propagation
of the front-line areas of spontaneous activity. In the proposed model, any
compact pattern of neural activity is seen as a source emitting a diverging
wave endogenous spikes. It is shown that the spike pattern of the wave front is
strictly unique and uniquely defined pattern that started the wave. The
propagation of waves with a unique pattern allows anywhere in nature undergoing
brain wave patterns there to judge the whole brain processes information. In
these assumptions naturally described mechanism of projection information
between regions of the cortex. Performed computer simulations show the high
effectiveness of such information model.
Sequential Changepoint Approach for Online Community Detection
Authors: David Marangoni-Simonsen, Yao Xie
Date: 2014-07-22 00:00:00
Tasks: Community Detection, Online Community Detection

We present new algorithms for detecting the emergence of a community in large
networks from sequential observations. The networks are modeled using
Erdos-Renyi random graphs with edges forming between nodes in the community
with higher probability. Based on statistical changepoint detection
methodology, we develop three algorithms: the Exhaustive Search (ES), the
mixture, and the Hierarchical Mixture (H-Mix) methods. Performance of these
methods is evaluated by the average run length (ARL), which captures the
frequency of false alarms, and the detection delay. Numerical comparisons show
that the ES method performs the best; however, it is exponentially complex. The
mixture method is polynomially complex by exploiting the fact that the size of
the community is typically small in a large network. However, it may react to a
group of active edges that do not form a community. This issue is resolved by
the H-Mix method, which is based on a dendrogram decomposition of the network.
We present an asymptotic analytical expression for ARL of the mixture method
when the threshold is large. Numerical simulation verifies that our
approximation is accurate even in the non-asymptotic regime. Hence, it can be
used to determine a desired threshold efficiently. Finally, numerical examples
show that the mixture and the H-Mix methods can both detect a community quickly
with a lower complexity than the ES method.
Learning Structured Outputs from Partial Labels using Forest Ensemble
Authors: Truyen Tran, Dinh Phung, Svetha Venkatesh
Date: 2014-07-24 00:00:00

Learning structured outputs with general structures is computationally
challenging, except for tree-structured models. Thus we propose an efficient
boosting-based algorithm AdaBoost.MRF for this task. The idea is based on the
realization that a graph is a superimposition of trees. Different from most
existing work, our algorithm can handle partial labelling, and thus is
particularly attractive in practice where reliable labels are often sparsely
observed. In addition, our method works exclusively on trees and thus is
guaranteed to converge. We apply the AdaBoost.MRF algorithm to an indoor video
surveillance scenario, where activities are modelled at multiple levels.
Performance evaluation of wavelet scattering network in image texture classification in various color spaces
Authors: Jiasong Wu, Longyu Jiang, Xu Han, Lotfi Senhadji, Huazhong Shu
Date: 2014-07-24 00:00:00
Tasks: Classification, General Classification, Texture Classification

Texture plays an important role in many image analysis applications. In this
paper, we give a performance evaluation of color texture classification by
performing wavelet scattering network in various color spaces. Experimental
results on the KTH_TIPS_COL database show that opponent RGB based wavelet
scattering network outperforms other color spaces. Therefore, when dealing with
the problem of color texture classification, opponent RGB based wavelet
scattering network is recommended.
Parallel MCMC with Generalized Elliptical Slice Sampling
Authors: Robert Nishihara, Iain Murray, Ryan P. Adams
Date: 2012-10-28 00:00:00

Probabilistic models are conceptually powerful tools for finding structure in
data, but their practical effectiveness is often limited by our ability to
perform inference in them. Exact inference is frequently intractable, so
approximate inference is often performed using Markov chain Monte Carlo (MCMC).
To achieve the best possible results from MCMC, we want to efficiently simulate
many steps of a rapidly mixing Markov chain which leaves the target
distribution invariant. Of particular interest in this regard is how to take
advantage of multi-core computing to speed up MCMC-based inference, both to
improve mixing and to distribute the computational load. In this paper, we
present a parallelizable Markov chain Monte Carlo algorithm for efficiently
sampling from continuous probability distributions that can take advantage of
hundreds of cores. This method shares information between parallel Markov
chains to build a scale-mixture of Gaussians approximation to the density
function of the target distribution. We combine this approximation with a
recent method known as elliptical slice sampling to create a Markov chain with
no step-size parameters that can mix rapidly without requiring gradient or
curvature computations.
Clustering Partially Observed Graphs via Convex Optimization
Authors: Yudong Chen, Ali Jalali, Sujay Sanghavi, Huan Xu
Date: 2011-04-25 00:00:00
Tasks: Clustering, Stochastic Block Model

This paper considers the problem of clustering a partially observed
unweighted graph---i.e., one where for some node pairs we know there is an edge
between them, for some others we know there is no edge, and for the remaining
we do not know whether or not there is an edge. We want to organize the nodes
into disjoint clusters so that there is relatively dense (observed)
connectivity within clusters, and sparse across clusters.
  We take a novel yet natural approach to this problem, by focusing on finding
the clustering that minimizes the number of "disagreements"---i.e., the sum of
the number of (observed) missing edges within clusters, and (observed) present
edges across clusters. Our algorithm uses convex optimization; its basis is a
reduction of disagreement minimization to the problem of recovering an
(unknown) low-rank matrix and an (unknown) sparse matrix from their partially
observed sum. We evaluate the performance of our algorithm on the classical
Planted Partition/Stochastic Block Model. Our main theorem provides sufficient
conditions for the success of our algorithm as a function of the minimum
cluster size, edge density and observation probability; in particular, the
results characterize the tradeoff between the observation probability and the
edge density gap. When there are a constant number of clusters of equal size,
our results are optimal up to logarithmic factors.