pwc-archive/papers-with-abstracts huggingface dataset:
A Comparative Study of Reservoir Computing for Temporal Signal Processing
Authors: Alireza Goudarzi, Peter Banda, Matthew R. Lakin, Christof Teuscher, Darko Stefanovic
Date: 2014-01-10 00:00:00
Tasks: Memorization, Time Series, Time Series Analysis, Time Series Prediction

Reservoir computing (RC) is a novel approach to time series prediction using
recurrent neural networks. In RC, an input signal perturbs the intrinsic
dynamics of a medium called a reservoir. A readout layer is then trained to
reconstruct a target output from the reservoir's state. The multitude of RC
architectures and evaluation metrics poses a challenge to both practitioners
and theorists who study the task-solving performance and computational power of
RC. In addition, in contrast to traditional computation models, the reservoir
is a dynamical system in which computation and memory are inseparable, and
therefore hard to analyze. Here, we compare echo state networks (ESN), a
popular RC architecture, with tapped-delay lines (DL) and nonlinear
autoregressive exogenous (NARX) networks, which we use to model systems with
limited computation and limited memory respectively. We compare the performance
of the three systems while computing three common benchmark time series:
H{\'e}non Map, NARMA10, and NARMA20. We find that the role of the reservoir in
the reservoir computing paradigm goes beyond providing a memory of the past
inputs. The DL and the NARX network have higher memorization capability, but
fall short of the generalization power of the ESN.
Distinguishing noise from chaos: objective versus subjective criteria using Horizontal Visibility Graph
Authors: Martín Gómez Ravetti, Laura C. Carpi, Bruna Amin Gonçalves, Alejandro C. Frery, Osvaldo A. Rosso
Date: 2014-01-09 00:00:00
Tasks: Information Plane, Time Series, Time Series Analysis, valid

A recently proposed methodology called the Horizontal Visibility Graph (HVG)
[Luque {\it et al.}, Phys. Rev. E., 80, 046103 (2009)] that constitutes a
geometrical simplification of the well known Visibility Graph algorithm [Lacasa
{\it et al.\/}, Proc. Natl. Sci. U.S.A. 105, 4972 (2008)], has been used to
study the distinction between deterministic and stochastic components in time
series [L. Lacasa and R. Toral, Phys. Rev. E., 82, 036120 (2010)].
Specifically, the authors propose that the node degree distribution of these
processes follows an exponential functional of the form $P(\kappa)\sim
\exp(-\lambda~\kappa)$, in which $\kappa$ is the node degree and $\lambda$ is a
positive parameter able to distinguish between deterministic (chaotic) and
stochastic (uncorrelated and correlated) dynamics. In this work, we investigate
the characteristics of the node degree distributions constructed by using HVG,
for time series corresponding to $28$ chaotic maps and $3$ different stochastic
processes. We thoroughly study the methodology proposed by Lacasa and Toral
finding several cases for which their hypothesis is not valid. We propose a
methodology that uses the HVG together with Information Theory quantifiers. An
extensive and careful analysis of the node degree distributions obtained by
applying HVG allow us to conclude that the Fisher-Shannon information plane is
a remarkable tool able to graphically represent the different nature,
deterministic or stochastic, of the systems under study.
Emotional Responses in Artificial Agent-Based Systems: Reflexivity and Adaptation in Artificial Life
Authors: Carlos Pedro Gonçalves
Date: 2014-01-09 00:00:00
Tasks: Artificial Life

The current work addresses a virtual environment with self-replicating agents
whose decisions are based on a form of "somatic computation" (soma - body) in
which basic emotional responses, taken in parallelism to actual living
organisms, are introduced as a way to provide the agents with greater reflexive
abilities. The work provides a contribution to the field of Artificial
Intelligence (AI) and Artificial Life (ALife) in connection to a
neurobiology-based cognitive framework for artificial systems and virtual
environments' simulations. The performance of the agents capable of emotional
responses is compared with that of self-replicating automata, and the
implications of research on emotions and AI, in connection to both virtual
agents as well as robots, is addressed regarding possible future directions and
applications.
Statistical Analysis based Hypothesis Testing Method in Biological Knowledge Discovery
Authors: Md. Naseef-Ur-Rahman Chowdhury, Suvankar Paul, Kazi Zakia Sultana
Date: 2014-01-09 00:00:00
Tasks: Two-sample testing

The correlation and interactions among different biological entities comprise
the biological system. Although already revealed interactions contribute to the
understanding of different existing systems, researchers face many questions
everyday regarding inter-relationships among entities. Their queries have
potential role in exploring new relations which may open up a new area of
investigation. In this paper, we introduce a text mining based method for
answering the biological queries in terms of statistical computation such that
researchers can come up with new knowledge discovery. It facilitates user to
submit their query in natural linguistic form which can be treated as
hypothesis. Our proposed approach analyzes the hypothesis and measures the
p-value of the hypothesis with respect to the existing literature. Based on the
measured value, the system either accepts or rejects the hypothesis from
statistical point of view. Moreover, even it does not find any direct
relationship among the entities of the hypothesis, it presents a network to
give an integral overview of all the entities through which the entities might
be related. This is also congenial for the researchers to widen their view and
thus think of new hypothesis for further investigation. It assists researcher
to get a quantitative evaluation of their assumptions such that they can reach
a logical conclusion and thus aids in relevant re-searches of biological
knowledge discovery. The system also provides the researchers a graphical
interactive interface to submit their hypothesis for assessment in a more
convenient way.
Gesture recognition based mouse events
Authors: Rachit Puri
Date: 2014-01-09 00:00:00
Tasks: Gesture Recognition

This paper presents the maneuver of mouse pointer and performs various mouse operations such as left click, right click, double click, drag etc using gestures recognition technique. Recognizing gestures is a complex task which involves many aspects such as motion modeling, motion analysis, pattern recognition and machine learning. Keeping all the essential factors in mind a system has been created which recognizes the movement of fingers and various patterns formed by them. Color caps have been used for fingers to distinguish it from the background color such as skin color. Thus recognizing the gestures various mouse events have been performed. The application has been created on MATLAB environment with operating system as windows 7.
Enhancement performance of road recognition system of autonomous robots in shadow scenario
Authors: Olusanya Y. Agunbiade, Tranos Zuva, Awosejo O. Johnson, Keneilwe Zuva
Date: 2014-01-09 00:00:00

Road region recognition is a main feature that is gaining increasing
attention from intellectuals because it helps autonomous vehicle to achieve a
successful navigation without accident. However, different techniques based on
camera sensor have been used by various researchers and outstanding results
have been achieved. Despite their success, environmental noise like shadow
leads to inaccurate recognition of road region which eventually leads to
accident for autonomous vehicle. In this research, we conducted an
investigation on shadow and its effects, optimized the road region recognition
system of autonomous vehicle by introducing an algorithm capable of detecting
and eliminating the effects of shadow. The experimental performance of our
system was tested and compared using the following schemes: Total Positive Rate
(TPR), False Negative Rate (FNR), Total Negative Rate (TNR), Error Rate (ERR)
and False Positive Rate (FPR). The performance result of the system improved on
road recognition in shadow scenario and this advancement has added tremendously
to successful navigation approaches for autonomous vehicle.
A logic for reasoning about ambiguity
Authors: Joseph Y. Halpern, Willemien Kets
Date: 2014-01-09 00:00:00

Standard models of multi-agent modal logic do not capture the fact that
information is often \emph{ambiguous}, and may be interpreted in different ways
by different agents. We propose a framework that can model this, and consider
different semantics that capture different assumptions about the agents'
beliefs regarding whether or not there is ambiguity. We examine the expressive
power of logics of ambiguity compared to logics that cannot model ambiguity,
with respect to the different semantics that we propose.
Brazilian License Plate Detection Using Histogram of Oriented Gradients and Sliding Windows
Authors: R. F. Prates, G. Cámara-Chávez, William R. Schwartz, D. Menotti
Date: 2014-01-09 00:00:00
Tasks: License Plate Detection

Due to the increasingly need for automatic traffic monitoring, vehicle
license plate detection is of high interest to perform automatic toll
collection, traffic law enforcement, parking lot access control, among others.
In this paper, a sliding window approach based on Histogram of Oriented
Gradients (HOG) features is used for Brazilian license plate detection. This
approach consists in scanning the whole image in a multiscale fashion such that
the license plate is located precisely. The main contribution of this work
consists in a deep study of the best setup for HOG descriptors on the detection
of Brazilian license plates, in which HOG have never been applied before. We
also demonstrate the reliability of this method ensured by a recall higher than
98% (with a precision higher than 78%) in a publicly available data set.
RNADE: The real-valued neural autoregressive density-estimator
Authors: Benigno Uria, Iain Murray, Hugo Larochelle
Date: 2013-06-02 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: Density Estimation

We introduce RNADE, a new model for joint density estimation of real-valued
vectors. Our model calculates the density of a datapoint as the product of
one-dimensional conditionals modeled using mixture density networks with shared
parameters. RNADE learns a distributed representation of the data, while having
a tractable expression for the calculation of densities. A tractable likelihood
allows direct comparison with other methods and training by standard
gradient-based optimizers. We compare the performance of RNADE on several
datasets of heterogeneous and perceptual data, finding it outperforms mixture
models in all but one case.
Hand-guided 3D surface acquisition by combining simple light sectioning with real-time algorithms
Authors: Oliver Arold, Svenja Ettl, Florian Willomitzer, Gerd Häusler
Date: 2014-01-09 00:00:00
Tasks: Object

Precise 3D measurements of rigid surfaces are desired in many fields of
application like quality control or surgery. Often, views from all around the
object have to be acquired for a full 3D description of the object surface. We
present a sensor principle called "Flying Triangulation" which avoids an
elaborate "stop-and-go" procedure. It combines a low-cost classical
light-section sensor with an algorithmic pipeline. A hand-guided sensor
captures a continuous movie of 3D views while being moved around the object.
The views are automatically aligned and the acquired 3D model is displayed in
real time. In contrast to most existing sensors no bandwidth is wasted for
spatial or temporal encoding of the projected lines. Nor is an expensive color
camera necessary for 3D acquisition. The achievable measurement uncertainty and
lateral resolution of the generated 3D data is merely limited by physics. An
alternating projection of vertical and horizontal lines guarantees the
existence of corresponding points in successive 3D views. This enables a
precise registration without surface interpolation. For registration, a variant
of the iterative closest point algorithm - adapted to the specific nature of
our 3D views - is introduced. Furthermore, data reduction and smoothing without
losing lateral resolution as well as the acquisition and mapping of a color
texture is presented. The precision and applicability of the sensor is
demonstrated by simulation and measurement results.
Radial basis function process neural network training based on generalized frechet distance and GA-SA hybrid strategy
Authors: Bing Wang, Yao-hua Meng, Xiao-hong Yu
Date: 2014-01-09 00:00:00
Tasks: global-optimization

For learning problem of Radial Basis Function Process Neural Network
(RBF-PNN), an optimization training method based on GA combined with SA is
proposed in this paper. Through building generalized Fr\'echet distance to
measure similarity between time-varying function samples, the learning problem
of radial basis centre functions and connection weights is converted into the
training on corresponding discrete sequence coefficients. Network training
objective function is constructed according to the least square error
criterion, and global optimization solving of network parameters is implemented
in feasible solution space by use of global optimization feature of GA and
probabilistic jumping property of SA . The experiment results illustrate that
the training algorithm improves the network training efficiency and stability.
A PSO and Pattern Search based Memetic Algorithm for SVMs Parameters Optimization
Authors: Yukun Bao, Zhongyi Hu, Tao Xiong
Date: 2014-01-09 00:00:00

Addressing the issue of SVMs parameters optimization, this study proposes an
efficient memetic algorithm based on Particle Swarm Optimization algorithm
(PSO) and Pattern Search (PS). In the proposed memetic algorithm, PSO is
responsible for exploration of the search space and the detection of the
potential regions with optimum solutions, while pattern search (PS) is used to
produce an effective exploitation on the potential regions obtained by PSO.
Moreover, a novel probabilistic selection strategy is proposed to select the
appropriate individuals among the current population to undergo local
refinement, keeping a well balance between exploration and exploitation.
Experimental results confirm that the local refinement with PS and our proposed
selection strategy are effective, and finally demonstrate effectiveness and
robustness of the proposed PSO-PS based MA for SVMs parameters optimization.
Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting
Authors: Tao Xiong, Yukun Bao, Zhongyi Hu
Date: 2014-01-09 00:00:00
Tasks: Time Series, Time Series Analysis

Highly accurate interval forecasting of a stock price index is fundamental to
successfully making a profit when making investment decisions, by providing a
range of values rather than a point estimate. In this study, we investigate the
possibility of forecasting an interval-valued stock price index series over
short and long horizons using multi-output support vector regression (MSVR).
Furthermore, this study proposes a firefly algorithm (FA)-based approach, built
on the established MSVR, for determining the parameters of MSVR (abbreviated as
FA-MSVR). Three globally traded broad market indices are used to compare the
performance of the proposed FA-MSVR method with selected counterparts. The
quantitative and comprehensive assessments are performed on the basis of
statistical criteria, economic criteria, and computational cost. In terms of
statistical criteria, we compare the out-of-sample forecasting using
goodness-of-forecast measures and testing approaches. In terms of economic
criteria, we assess the relative forecast performance with a simple trading
strategy. The results obtained in this study indicate that the proposed FA-MSVR
method is a promising alternative for forecasting interval-valued financial
time series.
A Parameterized Complexity Analysis of Bi-level Optimisation with Evolutionary Algorithms
Authors: Dogan Corus, Per Kristian Lehre, Frank Neumann, Mojgan Pourhassan
Date: 2014-01-09 00:00:00
Tasks: Evolutionary Algorithms

Bi-level optimisation problems have gained increasing interest in the field
of combinatorial optimisation in recent years. With this paper, we start the
runtime analysis of evolutionary algorithms for bi-level optimisation problems.
We examine two NP-hard problems, the generalised minimum spanning tree problem
(GMST), and the generalised travelling salesman problem (GTSP) in the context
of parameterised complexity.
  For the generalised minimum spanning tree problem, we analyse the two
approaches presented by Hu and Raidl (2012) with respect to the number of
clusters that distinguish each other by the chosen representation of possible
solutions. Our results show that a (1+1) EA working with the spanning nodes
representation is not a fixed-parameter evolutionary algorithm for the problem,
whereas the global structure representation enables to solve the problem in
fixed-parameter time. We present hard instances for each approach and show that
the two approaches are highly complementary by proving that they solve each
other's hard instances very efficiently.
  For the generalised travelling salesman problem, we analyse the problem with
respect to the number of clusters in the problem instance. Our results show
that a (1+1) EA working with the global structure representation is a
fixed-parameter evolutionary algorithm for the problem.
Efficient unimodality test in clustering by signature testing
Authors: Mahdi Shahbaba, Soosan Beheshti
Date: 2014-01-09 00:00:00
Tasks: Clustering

This paper provides a new unimodality test with application in hierarchical
clustering methods. The proposed method denoted by signature test (Sigtest),
transforms the data based on its statistics. The transformed data has much
smaller variation compared to the original data and can be evaluated in a
simple proposed unimodality test. Compared with the existing unimodality tests,
Sigtest is more accurate in detecting the overlapped clusters and has a much
less computational complexity. Simulation results demonstrate the efficiency of
this statistic test for both real and synthetic data sets.
Fighting Sample Degeneracy and Impoverishment in Particle Filters: A Review of Intelligent Approaches
Authors: Tiancheng Li, Shudong Sun, Tariq P. Sattar, Juan M. Corchado
Date: 2013-08-12 00:00:00
Tasks: Clustering

During the last two decades there has been a growing interest in Particle
Filtering (PF). However, PF suffers from two long-standing problems that are
referred to as sample degeneracy and impoverishment. We are investigating
methods that are particularly efficient at Particle Distribution Optimization
(PDO) to fight sample degeneracy and impoverishment, with an emphasis on
intelligence choices. These methods benefit from such methods as Markov Chain
Monte Carlo methods, Mean-shift algorithms, artificial intelligence algorithms
(e.g., Particle Swarm Optimization, Genetic Algorithm and Ant Colony
Optimization), machine learning approaches (e.g., clustering, splitting and
merging) and their hybrids, forming a coherent standpoint to enhance the
particle filter. The working mechanism, interrelationship, pros and cons of
these approaches are provided. In addition, Approaches that are effective for
dealing with high-dimensionality are reviewed. While improving the filter
performance in terms of accuracy, robustness and convergence, it is noted that
advanced techniques employed in PF often causes additional computational
requirement that will in turn sacrifice improvement obtained in real life
filtering. This fact, hidden in pure simulations, deserves the attention of the
users and designers of new filters.
Image reconstruction from few views by L0-norm optimization
Authors: Yuli Sun, Jinxu Tao
Date: 2014-01-09 00:00:00
Tasks: CT Reconstruction, Image Reconstruction

The L1-norm of the gradient-magnitude images (GMI), which is the well-known
total variation (TV) model, is widely used as regularization in the few views
CT reconstruction. As the L1-norm TV regularization is tending to uniformly
penalize the image gradient and the low-contrast structures are sometimes over
smoothed, we proposed a new algorithm based on the L0-norm of the GMI to deal
with the few views problem. To rise to the challenges introduced by the L0-norm
DGT, the algorithm uses a pseudo-inverse transform of DGT and adapts an
iterative hard thresholding (IHT) algorithm, whose convergence and effective
efficiency have been theoretically proven. The simulation indicates that the
algorithm proposed in this paper can obviously improve the reconstruction
quality.
Robust Large Scale Non-negative Matrix Factorization using Proximal Point Algorithm
Authors: Jason Gejie Liu, Shuchin Aeron
Date: 2014-01-08 00:00:00

A robust algorithm for non-negative matrix factorization (NMF) is presented
in this paper with the purpose of dealing with large-scale data, where the
separability assumption is satisfied. In particular, we modify the Linear
Programming (LP) algorithm of [9] by introducing a reduced set of constraints
for exact NMF. In contrast to the previous approaches, the proposed algorithm
does not require the knowledge of factorization rank (extreme rays [3] or
topics [7]). Furthermore, motivated by a similar problem arising in the context
of metabolic network analysis [13], we consider an entirely different regime
where the number of extreme rays or topics can be much larger than the
dimension of the data vectors. The performance of the algorithm for different
synthetic data sets are provided.
Learning Multilingual Word Representations using a Bag-of-Words Autoencoder
Authors: Stanislas Lauly, Alex Boulanger, Hugo Larochelle
Date: 2014-01-08 00:00:00
Tasks: Document Classification, General Classification, Sentence, Translation, Word Embeddings

Recent work on learning multilingual word representations usually relies on
the use of word-level alignements (e.g. infered with the help of GIZA++)
between translated sentences, in order to align the word embeddings in
different languages. In this workshop paper, we investigate an autoencoder
model for learning multilingual word representations that does without such
word-level alignements. The autoencoder is trained to reconstruct the
bag-of-word representation of given sentence from an encoded representation
extracted from its translation. We evaluate our approach on a multilingual
document classification task, where labeled data is available only for one
language (e.g. English) while classification must be performed in a different
language (e.g. French). In our experiments, we observe that our method compares
favorably with a previously proposed method that exploits word-level alignments
to learn word representations.
Large Scale Visual Recommendations From Street Fashion Images
Authors: Vignesh Jagadeesh, Robinson Piramuthu, Anurag Bhardwaj, Wei Di, Neel Sundaresan
Date: 2014-01-08 00:00:00
Tasks: Recommendation Systems, Retrieval

We describe a completely automated large scale visual recommendation system
for fashion. Our focus is to efficiently harness the availability of large
quantities of online fashion images and their rich meta-data. Specifically, we
propose four data driven models in the form of Complementary Nearest Neighbor
Consensus, Gaussian Mixture Models, Texture Agnostic Retrieval and Markov Chain
LDA for solving this problem. We analyze relative merits and pitfalls of these
algorithms through extensive experimentation on a large-scale data set and
baseline them against existing ideas from color science. We also illustrate key
fashion insights learned through these experiments and show how they can be
employed to design better recommendation systems. Finally, we also outline a
large-scale annotated data set of fashion images (Fashion-136K) that can be
exploited for future vision research.