pwc-archive/papers-with-abstracts huggingface dataset:
Learning Deep Representations By Distributed Random Samplings
Authors: Xiao-Lei Zhang
Date: 2013-12-16 00:00:00
Tasks: Clustering, Dimensionality Reduction

In this paper, we propose an extremely simple deep model for the unsupervised
nonlinear dimensionality reduction -- deep distributed random samplings, which
performs like a stack of unsupervised bootstrap aggregating. First, its network
structure is novel: each layer of the network is a group of mutually
independent $k$-centers clusterings. Second, its learning method is extremely
simple: the $k$ centers of each clustering are only $k$ randomly selected
examples from the training data; for small-scale data sets, the $k$ centers are
further randomly reconstructed by a simple cyclic-shift operation. Experimental
results on nonlinear dimensionality reduction show that the proposed method can
learn abstract representations on both large-scale and small-scale problems,
and meanwhile is much faster than deep neural networks on large-scale problems.
Rectifying Self Organizing Maps for Automatic Concept Learning from Web Images
Authors: Eren Golge, Pinar Duygulu
Date: 2013-12-16 00:00:00
Tasks: Clustering, Image Retrieval, Outlier Detection

We attack the problem of learning concepts automatically from noisy web image
search results. Going beyond low level attributes, such as colour and texture,
we explore weakly-labelled datasets for the learning of higher level concepts,
such as scene categories. The idea is based on discovering common
characteristics shared among subsets of images by posing a method that is able
to organise the data while eliminating irrelevant instances. We propose a novel
clustering and outlier detection method, namely Rectifying Self Organizing Maps
(RSOM). Given an image collection returned for a concept query, RSOM provides
clusters pruned from outliers. Each cluster is used to train a model
representing a different characteristics of the concept. The proposed method
outperforms the state-of-the-art studies on the task of learning low-level
concepts, and it is competitive in learning higher level concepts as well. It
is capable to work at large scale with no supervision through exploiting the
available sources.
Single-trial estimation of stimulus and spike-history effects on time-varying ensemble spiking activity of multiple neurons: a simulation study
Authors: Hideaki Shimazaki
Date: 2013-12-16 00:00:00

Neurons in cortical circuits exhibit coordinated spiking activity, and can
produce correlated synchronous spikes during behavior and cognition. We
recently developed a method for estimating the dynamics of correlated ensemble
activity by combining a model of simultaneous neuronal interactions (e.g., a
spin-glass model) with a state-space method (Shimazaki et al. 2012 PLoS Comput
Biol 8 e1002385). This method allows us to estimate stimulus-evoked dynamics of
neuronal interactions which is reproducible in repeated trials under identical
experimental conditions. However, the method may not be suitable for detecting
stimulus responses if the neuronal dynamics exhibits significant variability
across trials. In addition, the previous model does not include effects of past
spiking activity of the neurons on the current state of ensemble activity. In
this study, we develop a parametric method for simultaneously estimating the
stimulus and spike-history effects on the ensemble activity from single-trial
data even if the neurons exhibit dynamics that is largely unrelated to these
effects. For this goal, we model ensemble neuronal activity as a latent process
and include the stimulus and spike-history effects as exogenous inputs to the
latent process. We develop an expectation-maximization algorithm that
simultaneously achieves estimation of the latent process, stimulus responses,
and spike-history effects. The proposed method is useful to analyze an
interaction of internal cortical states and sensory evoked activity.
Teleoperation System Using Past Image Records Considering Narrow Communication Band
Authors: Noritaka Sato, Masataka Ito, Yoshifumi Morita, Fumitoshi Matsuno
Date: 2013-12-16 00:00:00
Tasks: Position

Teleoperation is necessary when the robot is applied to real missions, for
example surveillance, search and rescue. We proposed teleoperation system using
past image records (SPIR). SPIR virtually generates the bird's-eye view image
by overlaying the CG model of the robot at the corresponding current position
on the background image which is captured from the camera mounted on the robot
at a past time. The problem for SPIR is that the communication bandwidth is
often narrow in some teleoperation tasks. In this case, the candidates of
background image of SPIR are few and the position of the robot is often
delayed. In this study, we propose zoom function for insufficiency of
candidates of the background image and additional interpolation lines for the
delay of the position data of the robot. To evaluate proposed system, an
outdoor experiments are carried out. The outdoor experiment is conducted on a
training course of a driving school.
Geospatial Narratives and their Spatio-Temporal Dynamics: Commonsense Reasoning for High-level Analyses in Geographic Information Systems
Authors: Mehul Bhatt, Jan Oliver Wallgruen
Date: 2013-07-09 00:00:00
Tasks: Data Integration

The modelling, analysis, and visualisation of dynamic geospatial phenomena
has been identified as a key developmental challenge for next-generation
Geographic Information Systems (GIS). In this context, the envisaged
paradigmatic extensions to contemporary foundational GIS technology raises
fundamental questions concerning the ontological, formal representational, and
(analytical) computational methods that would underlie their spatial
information theoretic underpinnings.
  We present the conceptual overview and architecture for the development of
high-level semantic and qualitative analytical capabilities for dynamic
geospatial domains. Building on formal methods in the areas of commonsense
reasoning, qualitative reasoning, spatial and temporal representation and
reasoning, reasoning about actions and change, and computational models of
narrative, we identify concrete theoretical and practical challenges that
accrue in the context of formal reasoning about `space, events, actions, and
change'. With this as a basis, and within the backdrop of an illustrated
scenario involving the spatio-temporal dynamics of urban narratives, we address
specific problems and solutions techniques chiefly involving `qualitative
abstraction', `data integration and spatial consistency', and `practical
geospatial abduction'. From a broad topical viewpoint, we propose that
next-generation dynamic GIS technology demands a transdisciplinary scientific
perspective that brings together Geography, Artificial Intelligence, and
Cognitive Science.
  Keywords: artificial intelligence; cognitive systems; human-computer
interaction; geographic information systems; spatio-temporal dynamics;
computational models of narrative; geospatial analysis; geospatial modelling;
ontology; qualitative spatial modelling and reasoning; spatial assistance
systems
Strategic Argumentation is NP-Complete
Authors: Guido Governatori, Francesco Olivieri, Simone Scannapieco, Antonino Rotolo, Matteo Cristani
Date: 2013-12-16 00:00:00

In this paper we study the complexity of strategic argumentation for dialogue
games. A dialogue game is a 2-player game where the parties play arguments. We
show how to model dialogue games in a skeptical, non-monotonic formalism, and
we show that the problem of deciding what move (set of rules) to play at each
turn is an NP-complete problem.
Probable convexity and its application to Correlated Topic Models
Authors: Khoat Than, Tu Bao Ho
Date: 2013-12-16 00:00:00
Tasks: Topic Models

Non-convex optimization problems often arise from probabilistic modeling,
such as estimation of posterior distributions. Non-convexity makes the problems
intractable, and poses various obstacles for us to design efficient algorithms.
In this work, we attack non-convexity by first introducing the concept of
\emph{probable convexity} for analyzing convexity of real functions in
practice. We then use the new concept to analyze an inference problem in the
\emph{Correlated Topic Model} (CTM) and related nonconjugate models. Contrary
to the existing belief of intractability, we show that this inference problem
is concave under certain conditions. One consequence of our analyses is a novel
algorithm for learning CTM which is significantly more scalable and qualitative
than existing methods. Finally, we highlight that stochastic gradient
algorithms might be a practical choice to resolve efficiently non-convex
problems. This finding might find beneficial in many contexts which are beyond
probabilistic modeling.
Vision-Guided Robot Hearing
Authors: Xavier Alameda-Pineda, Radu Horaud
Date: 2013-11-06 00:00:00
Tasks: speech-recognition, Speech Recognition

Natural human-robot interaction in complex and unpredictable environments is
one of the main research lines in robotics. In typical real-world scenarios,
humans are at some distance from the robot and the acquired signals are
strongly impaired by noise, reverberations and other interfering sources. In
this context, the detection and localisation of speakers plays a key role since
it is the pillar on which several tasks (e.g.: speech recognition and speaker
tracking) rely. We address the problem of how to detect and localize people
that are both seen and heard by a humanoid robot. We introduce a hybrid
deterministic/probabilistic model. Indeed, the deterministic component allows
us to map the visual information into the auditory space. By means of the
probabilistic component, the visual features guide the grouping of the auditory
features in order to form AV objects. The proposed model and the associated
algorithm are implemented in real-time (17 FPS) using a stereoscopic camera
pair and two microphones embedded into the head of the humanoid robot NAO. We
performed experiments on (i) synthetic data, (ii) a publicly available data set
and (iii) data acquired using the robot. The results we obtained validate the
approach and encourage us to further investigate how vision can help robot
hearing.
Near-Optimal Bayesian Active Learning with Noisy Observations
Authors: Daniel Golovin, Andreas Krause, Debajyoti Ray
Date: 2010-10-15 00:00:00
Proceeding: NeurIPS 2010 12
Tasks: Active Learning, Experimental Design

We tackle the fundamental problem of Bayesian active learning with noise,
where we need to adaptively select from a number of expensive tests in order to
identify an unknown hypothesis sampled from a known prior distribution. In the
case of noise-free observations, a greedy algorithm called generalized binary
search (GBS) is known to perform near-optimally. We show that if the
observations are noisy, perhaps surprisingly, GBS can perform very poorly. We
develop EC2, a novel, greedy active learning algorithm and prove that it is
competitive with the optimal policy, thus obtaining the first competitiveness
guarantees for Bayesian active learning with noisy observations. Our bounds
rely on a recently discovered diminishing returns property called adaptive
submodularity, generalizing the classical notion of submodular set functions to
adaptive policies. Our results hold even if the tests have non-uniform cost and
their noise is correlated. We also propose EffECXtive, a particularly fast
approximation of EC2, and evaluate it on a Bayesian experimental design problem
involving human subjects, intended to tease apart competing economic theories
of how people make decisions under uncertainty.
Feature Graph Architectures
Authors: Richard Davis, Sanjay Chawla, Philip Leong
Date: 2013-12-15 00:00:00

In this article we propose feature graph architectures (FGA), which are deep
learning systems employing a structured initialisation and training method
based on a feature graph which facilitates improved generalisation performance
compared with a standard shallow architecture. The goal is to explore
alternative perspectives on the problem of deep network training. We evaluate
FGA performance for deep SVMs on some experimental datasets, and show how
generalisation and stability results may be derived for these models. We
describe the effect of permutations on the model accuracy, and give a criterion
for the optimal permutation in terms of feature correlations. The experimental
results show that the algorithm produces robust and significant test set
improvements over a standard shallow SVM training method for a range of
datasets. These gains are achieved with a moderate increase in time complexity.
A Framework for Genetic Algorithms Based on Hadoop
Authors: Filomena Ferrucci, M-Tahar Kechadi, Pasquale Salza, Federica Sarro
Date: 2013-11-30 00:00:00

Genetic Algorithms (GAs) are powerful metaheuristic techniques mostly used in
many real-world applications. The sequential execution of GAs requires
considerable computational power both in time and resources. Nevertheless, GAs
are naturally parallel and accessing a parallel platform such as Cloud is easy
and cheap. Apache Hadoop is one of the common services that can be used for
parallel applications. However, using Hadoop to develop a parallel version of
GAs is not simple without facing its inner workings. Even though some
sequential frameworks for GAs already exist, there is no framework supporting
the development of GA applications that can be executed in parallel. In this
paper is described a framework for parallel GAs on the Hadoop platform,
following the paradigm of MapReduce. The main purpose of this framework is to
allow the user to focus on the aspects of GA that are specific to the problem
to be addressed, being sure that this task is going to be correctly executed on
the Cloud with a good performance. The framework has been also exploited to
develop an application for Feature Subset Selection problem. A preliminary
analysis of the performance of the developed GA application has been performed
using three datasets and shown very promising performance.
Face Detection from still and Video Images using Unsupervised Cellular Automata with K means clustering algorithm
Authors: P. Kiran Sree, I. Ramesh Babu
Date: 2013-12-15 00:00:00
Tasks: Clustering, Face Detection, Position

Pattern recognition problem rely upon the features inherent in the pattern of images. Face detection and recognition is one of the challenging research areas in the field of computer vision. In this paper, we present a method to identify skin pixels from still and video images using skin color. Face regions are identified from this skin pixel region. Facial features such as eyes, nose and mouth are then located. Faces are recognized from color images using an RBF based neural network. Unsupervised Cellular Automata with K means clustering algorithm is used to locate different facial elements. Orientation is corrected by using eyes. Parameters like inter eye distance, nose length, mouth position, Discrete Cosine Transform (DCT) coefficients etc. are computed and used for a Radial Basis Function (RBF) based neural network. This approach reliably works for face sequence with orientation in head, expressions etc.
Autonomous Quantum Perceptron Neural Network
Authors: Alaa Sagheer, Mohammed Zidan
Date: 2013-12-15 00:00:00

Recently, with the rapid development of technology, there are a lot of
applications require to achieve low-cost learning. However the computational
power of classical artificial neural networks, they are not capable to provide
low-cost learning. In contrast, quantum neural networks may be representing a
good computational alternate to classical neural network approaches, based on
the computational power of quantum bit (qubit) over the classical bit. In this
paper we present a new computational approach to the quantum perceptron neural
network can achieve learning in low-cost computation. The proposed approach has
only one neuron can construct self-adaptive activation operators capable to
accomplish the learning process in a limited number of iterations and, thereby,
reduce the overall computational cost. The proposed approach is capable to
construct its own set of activation operators to be applied widely in both
quantum and classical applications to overcome the linearity limitation of
classical perceptron. The computational power of the proposed approach is
illustrated via solving variety of problems where promising and comparable
results are given.
An introduction to synchronous self-learning Pareto strategy
Authors: Ahmad Mozaffari, Alireza Fathi
Date: 2013-12-15 00:00:00
Tasks: Evolutionary Algorithms, Self-Learning

In last decades optimization and control of complex systems that possessed
various conflicted objectives simultaneously attracted an incremental interest
of scientists. This is because of the vast applications of these systems in
various fields of real life engineering phenomena that are generally multi
modal, non convex and multi criterion. Hence, many researchers utilized
versatile intelligent models such as Pareto based techniques, game theory
(cooperative and non cooperative games), neuro evolutionary systems, fuzzy
logic and advanced neural networks for handling these types of problems. In
this paper a novel method called Synchronous Self Learning Pareto Strategy
Algorithm (SSLPSA) is presented which utilizes Evolutionary Computing (EC),
Swarm Intelligence (SI) techniques and adaptive Classical Self Organizing Map
(CSOM) simultaneously incorporating with a data shuffling behavior.
Evolutionary Algorithms (EA) which attempt to simulate the phenomenon of
natural evolution are powerful numerical optimization algorithms that reach an
approximate global maximum of a complex multi variable function over a wide
search space and swarm base technique can improved the intensity and the
robustness in EA. CSOM is a neural network capable of learning and can improve
the quality of obtained optimal Pareto front. To prove the efficient
performance of proposed algorithm, authors utilized some well known benchmark
test functions. Obtained results indicate that the cited method is best suit in
the case of vector optimization.
A robust Iris recognition method on adverse conditions
Authors: Maryam Soltanali Khalili, Hamed Sadjedi
Date: 2013-12-15 00:00:00
Tasks: feature selection, Iris Recognition

As a stable biometric system, iris has recently attracted great attention
among the researchers. However, research is still needed to provide appropriate
solutions to ensure the resistance of the system against error factors. The
present study has tried to apply a mask to the image so that the unexpected
factors affecting the location of the iris can be removed. So, pupil
localization will be faster and robust. Then to locate the exact location of
the iris, a simple stage of boundary displacement due to the Canny edge
detector has been applied. Then, with searching left and right IRIS edge point,
outer radios of IRIS will be detect. Through the process of extracting the iris
features, it has been sought to obtain the distinctive iris texture features by
using a discrete stationary wavelets transform 2-D (DSWT2). Using DSWT2 tool
and symlet 4 wavelet, distinctive features are extracted. To reduce the
computational cost, the features obtained from the application of the wavelet
have been investigated and a feature selection procedure, using similarity
criteria, has been implemented. Finally, the iris matching has been performed
using a semi-correlation criterion. The accuracy of the proposed method for
localization on CASIA-v1, CASIA-v3 is 99.73%, 98.24% and 97.04%, respectively.
The accuracy of the feature extraction proposed method for CASIA3 iris images
database is 97.82%, which confirms the efficiency of the proposed method.
A MapReduce based distributed SVM algorithm for binary classification
Authors: Ferhat Özgür Çatak, Mehmet Erdal Balaban
Date: 2013-12-15 00:00:00
Tasks: Binary Classification, Classification, Cloud Computing, General Classification, Learning Theory

Although Support Vector Machine (SVM) algorithm has a high generalization
property to classify for unseen examples after training phase and it has small
loss value, the algorithm is not suitable for real-life classification and
regression problems. SVMs cannot solve hundreds of thousands examples in
training dataset. In previous studies on distributed machine learning
algorithms, SVM is trained over a costly and preconfigured computer
environment. In this research, we present a MapReduce based distributed
parallel SVM training algorithm for binary classification problems. This work
shows how to distribute optimization problem over cloud computing systems with
MapReduce technique. In the second step of this work, we used statistical
learning theory to find the predictive hypothesis that minimize our empirical
risks from hypothesis spaces that created with reduce function of MapReduce.
The results of this research are important for training of big datasets for SVM
algorithm based classification problems. We provided that iterative training of
split dataset with MapReduce technique; accuracy of the classifier function
will converge to global optimal classifier function's accuracy in finite
iteration size. The algorithm performance was measured on samples from letter
recognition and pen-based recognition of handwritten digits dataset.
Domain adaptation for sequence labeling using hidden Markov models
Authors: Edouard Grave, Guillaume Obozinski, Francis Bach
Date: 2013-12-14 00:00:00
Tasks: Domain Adaptation, Part-Of-Speech Tagging

Most natural language processing systems based on machine learning are not
robust to domain shift. For example, a state-of-the-art syntactic dependency
parser trained on Wall Street Journal sentences has an absolute drop in
performance of more than ten points when tested on textual data from the Web.
An efficient solution to make these methods more robust to domain shift is to
first learn a word representation using large amounts of unlabeled data from
both domains, and then use this representation as features in a supervised
learning algorithm. In this paper, we propose to use hidden Markov models to
learn word representations for part-of-speech tagging. In particular, we study
the influence of using data from the source, the target or both domains to
learn the representation and the different ways to represent words using an
HMM.
A natural-inspired optimization machine based on the annual migration of salmons in nature
Authors: Ahmad Mozaffari, Alireza Fathi
Date: 2013-12-14 00:00:00
Tasks: Fault Detection

Bio inspiration is a branch of artificial simulation science that shows
pervasive contributions to variety of engineering fields such as automate
pattern recognition, systematic fault detection and applied optimization. In
this paper, a new metaheuristic optimizing algorithm that is the simulation of
The Great Salmon Run(TGSR) is developed. The obtained results imply on the
acceptable performance of implemented method in optimization of complex non
convex, multi dimensional and multi-modal problems. To prove the superiority of
TGSR in both robustness and quality, it is also compared with most of the well
known proposed optimizing techniques such as Simulated Annealing (SA), Parallel
Migrating Genetic Algorithm (PMGA), Differential Evolutionary Algorithm (DEA),
Particle Swarm Optimization (PSO), Bee Algorithm (BA), Artificial Bee Colony
(ABC), Firefly Algorithm (FA) and Cuckoo Search (CS). The obtained results
confirm the acceptable performance of the proposed method in both robustness
and quality for different bench-mark optimizing problems and also prove the
authors claim.