pwc-archive/papers-with-abstracts huggingface dataset:
Time series modeling by a regression approach based on a latent process
Authors: Faicel Chamroukhi, Allou Samé, Gérard Govaert, Patrice Aknin
Date: 2013-12-25 00:00:00
Tasks: global-optimization, regression, Time Series, Time Series Analysis

Time series are used in many domains including finance, engineering,
economics and bioinformatics generally to represent the change of a measurement
over time. Modeling techniques may then be used to give a synthetic
representation of such data. A new approach for time series modeling is
proposed in this paper. It consists of a regression model incorporating a
discrete hidden logistic process allowing for activating smoothly or abruptly
different polynomial regression models. The model parameters are estimated by
the maximum likelihood method performed by a dedicated Expectation Maximization
(EM) algorithm. The M step of the EM algorithm uses a multi-class Iterative
Reweighted Least-Squares (IRLS) algorithm to estimate the hidden process
parameters. To evaluate the proposed approach, an experimental study on
simulated data and real world data was performed using two alternative
approaches: a heteroskedastic piecewise regression model using a global
optimization algorithm based on dynamic programming, and a Hidden Markov
Regression Model whose parameters are estimated by the Baum-Welch algorithm.
Finally, in the context of the remote monitoring of components of the French
railway infrastructure, and more particularly the switch mechanism, the
proposed approach has been applied to modeling and classifying time series
representing the condition measurements acquired during switch operations.
A hidden process regression model for functional data description. Application to curve discrimination
Authors: Faicel Chamroukhi, Allou Samé, Gérard Govaert, Patrice Aknin
Date: 2013-12-25 00:00:00
Tasks: General Classification, regression

A new approach for functional data description is proposed in this paper. It
consists of a regression model with a discrete hidden logistic process which is
adapted for modeling curves with abrupt or smooth regime changes. The model
parameters are estimated in a maximum likelihood framework through a dedicated
Expectation Maximization (EM) algorithm. From the proposed generative model, a
curve discrimination rule is derived using the Maximum A Posteriori rule. The
proposed model is evaluated using simulated curves and real world curves
acquired during railway switch operations, by performing comparisons with the
piecewise regression approach in terms of curve modeling and classification.
Model-based clustering and segmentation of time series with changes in regime
Authors: Allou Samé, Faicel Chamroukhi, Gérard Govaert, Patrice Aknin
Date: 2013-12-25 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

Mixture model-based clustering, usually applied to multidimensional data, has
become a popular approach in many data analysis problems, both for its good
statistical properties and for the simplicity of implementation of the
Expectation-Maximization (EM) algorithm. Within the context of a railway
application, this paper introduces a novel mixture model for dealing with time
series that are subject to changes in regime. The proposed approach consists in
modeling each cluster by a regression model in which the polynomial
coefficients vary according to a discrete hidden process. In particular, this
approach makes use of logistic functions to model the (smooth or abrupt)
transitions between regimes. The model parameters are estimated by the maximum
likelihood method solved by an Expectation-Maximization algorithm. The proposed
approach can also be regarded as a clustering approach which operates by
finding groups of time series having common changes in regime. In addition to
providing a time series partition, it therefore provides a time series
segmentation. The problem of selecting the optimal numbers of clusters and
segments is solved by means of the Bayesian Information Criterion (BIC). The
proposed approach is shown to be efficient using a variety of simulated time
series and real-world time series of electrical power consumption from rail
switching operations.
Clustering for high-dimension, low-sample size data using distance vectors
Authors: Yoshikazu Terada
Date: 2013-12-12 00:00:00
Tasks: Clustering, Vocal Bursts Intensity Prediction

In high-dimension, low-sample size (HDLSS) data, it is not always true that
closeness of two objects reflects a hidden cluster structure. We point out the
important fact that it is not the closeness, but the "values" of distance that
contain information of the cluster structure in high-dimensional space. Based
on this fact, we propose an efficient and simple clustering approach, called
distance vector clustering, for HDLSS data. Under the assumptions given in the
work of Hall et al. (2005), we show the proposed approach provides a true
cluster label under milder conditions when the dimension tends to infinity with
the sample size fixed. The effectiveness of the distance vector clustering
approach is illustrated through a numerical experiment and real data analysis.
Model-based functional mixture discriminant analysis with hidden process regression for curve classification
Authors: Faicel Chamroukhi, Hervé Glotin, Allou Samé
Date: 2013-12-25 00:00:00
Tasks: General Classification, regression

In this paper, we study the modeling and the classification of functional
data presenting regime changes over time. We propose a new model-based
functional mixture discriminant analysis approach based on a specific hidden
process regression model that governs the regime changes over time. Our
approach is particularly adapted to handle the problem of complex-shaped
classes of curves, where each class is potentially composed of several
sub-classes, and to deal with the regime changes within each homogeneous
sub-class. The proposed model explicitly integrates the heterogeneity of each
class of curves via a mixture model formulation, and the regime changes within
each sub-class through a hidden logistic process. Each class of complex-shaped
curves is modeled by a finite number of homogeneous clusters, each of them
being decomposed into several regimes. The model parameters of each class are
learned by maximizing the observed-data log-likelihood by using a dedicated
expectation-maximization (EM) algorithm. Comparisons are performed with
alternative curve classification approaches, including functional linear
discriminant analysis and functional mixture discriminant analysis with
polynomial regression mixtures and spline regression mixtures. Results obtained
on simulated data and real data show that the proposed approach outperforms the
alternative approaches in terms of discrimination, and significantly improves
the curves approximation.
An Unsupervised Approach for Automatic Activity Recognition based on Hidden Markov Model Regression
Authors: Dorra Trabelsi, Samer Mohammed, Faicel Chamroukhi, Latifa Oukhellou, Yacine Amirat
Date: 2013-12-25 00:00:00
Tasks: Activity Recognition, General Classification, Human Activity Recognition, regression, Segmentation, Time Series, Time Series Analysis

Using supervised machine learning approaches to recognize human activities
from on-body wearable accelerometers generally requires a large amount of
labelled data. When ground truth information is not available, too expensive,
time consuming or difficult to collect, one has to rely on unsupervised
approaches. This paper presents a new unsupervised approach for human activity
recognition from raw acceleration data measured using inertial wearable
sensors. The proposed method is based upon joint segmentation of
multidimensional time series using a Hidden Markov Model (HMM) in a multiple
regression context. The model is learned in an unsupervised framework using the
Expectation-Maximization (EM) algorithm where no activity labels are needed.
The proposed method takes into account the sequential appearance of the data.
It is therefore adapted for the temporal acceleration data to accurately detect
the activities. It allows both segmentation and classification of the human
activities. Experimental results are provided to demonstrate the efficiency of
the proposed approach with respect to standard supervised and unsupervised
classification approaches
Subjectivity Classification using Machine Learning Techniques for Mining Feature-Opinion Pairs from Web Opinion Sources
Authors: Ahmad Kamal
Date: 2013-12-25 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Opinion Mining

Due to flourish of the Web 2.0, web opinion sources are rapidly emerging
containing precious information useful for both customers and manufactures.
Recently, feature based opinion mining techniques are gaining momentum in which
customer reviews are processed automatically for mining product features and
user opinions expressed over them. However, customer reviews may contain both
opinionated and factual sentences. Distillations of factual contents improve
mining performance by preventing noisy and irrelevant extraction. In this
paper, combination of both supervised machine learning and rule-based
approaches are proposed for mining feasible feature-opinion pairs from
subjective review sentences. In the first phase of the proposed approach, a
supervised machine learning technique is applied for classifying subjective and
objective sentences from customer reviews. In the next phase, a rule based
method is implemented which applies linguistic and semantic analysis of texts
to mine feasible feature-opinion pairs from subjective sentences retained after
the first phase. The effectiveness of the proposed methods is established
through experimentation over customer reviews on different electronic products.
The Influence of Global Constraints on Similarity Measures for Time-Series Databases
Authors: Vladimir Kurbalija, Miloš Radovanović, Zoltan Geler, Mirjana Ivanović
Date: 2011-07-01 00:00:00
Tasks: Dynamic Time Warping, Time Series, Time Series Analysis

A time series consists of a series of values or events obtained over repeated
measurements in time. Analysis of time series represents and important tool in
many application areas, such as stock market analysis, process and quality
control, observation of natural phenomena, medical treatments, etc. A vital
component in many types of time-series analysis is the choice of an appropriate
distance/similarity measure. Numerous measures have been proposed to date, with
the most successful ones based on dynamic programming. Being of quadratic time
complexity, however, global constraints are often employed to limit the search
space in the matrix during the dynamic programming procedure, in order to speed
up computation. Furthermore, it has been reported that such constrained
measures can also achieve better accuracy. In this paper, we investigate two
representative time-series distance/similarity measures based on dynamic
programming, Dynamic Time Warping (DTW) and Longest Common Subsequence (LCS),
and the effects of global constraints on them. Through extensive experiments on
a large number of time-series data sets, we demonstrate how global constrains
can significantly reduce the computation time of DTW and LCS. We also show
that, if the constraint parameter is tight enough (less than 10-15% of
time-series length), the constrained measure becomes significantly different
from its unconstrained counterpart, in the sense of producing qualitatively
different 1-nearest neighbor graphs. This observation explains the potential
for accuracy gains when using constrained measures, highlighting the need for
careful tuning of constraint parameters in order to achieve a good trade-off
between speed and accuracy.
Joint segmentation of multivariate time series with hidden process regression for human activity recognition
Authors: Faicel Chamroukhi, Samer Mohammed, Dorra Trabelsi, Latifa Oukhellou, Yacine Amirat
Date: 2013-12-25 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, regression, Time Series, Time Series Analysis

The problem of human activity recognition is central for understanding and
predicting the human behavior, in particular in a prospective of assistive
services to humans, such as health monitoring, well being, security, etc. There
is therefore a growing need to build accurate models which can take into
account the variability of the human activities over time (dynamic models)
rather than static ones which can have some limitations in such a dynamic
context. In this paper, the problem of activity recognition is analyzed through
the segmentation of the multidimensional time series of the acceleration data
measured in the 3-d space using body-worn accelerometers. The proposed model
for automatic temporal segmentation is a specific statistical latent process
model which assumes that the observed acceleration sequence is governed by
sequence of hidden (unobserved) activities. More specifically, the proposed
approach is based on a specific multiple regression model incorporating a
hidden discrete logistic process which governs the switching from one activity
to another over time. The model is learned in an unsupervised context by
maximizing the observed-data log-likelihood via a dedicated
expectation-maximization (EM) algorithm. We applied it on a real-world
automatic human activity recognition problem and its performance was assessed
by performing comparisons with alternative approaches, including well-known
supervised static classifiers and the standard hidden Markov model (HMM). The
obtained results are very encouraging and show that the proposed approach is
quite competitive even it works in an entirely unsupervised way and does not
requires a feature extraction preprocessing step.
Refinement Modal Logic
Authors: Laura Bozzelli, Hans van Ditmarsch, Tim French, James Hales, Sophie Pinchinat
Date: 2012-02-16 00:00:00
Tasks: All

In this paper we present {\em refinement modal logic}. A refinement is like a
bisimulation, except that from the three relational requirements only `atoms'
and `back' need to be satisfied. Our logic contains a new operator 'all' in
addition to the standard modalities 'box' for each agent. The operator 'all'
acts as a quantifier over the set of all refinements of a given model. As a
variation on a bisimulation quantifier, this refinement operator or refinement
quantifier 'all' can be seen as quantifying over a variable not occurring in
the formula bound by it. The logic combines the simplicity of multi-agent modal
logic with some powers of monadic second-order quantification. We present a
sound and complete axiomatization of multi-agent refinement modal logic. We
also present an extension of the logic to the modal mu-calculus, and an
axiomatization for the single-agent version of this logic. Examples and
applications are also discussed: to software verification and design (the set
of agents can also be seen as a set of actions), and to dynamic epistemic
logic. We further give detailed results on the complexity of satisfiability,
and on succinctness.
Description Logics based Formalization of Wh-Queries
Authors: Sourish Dasgupta, Rupali KaPatel, Ankur Padia, Kushal Shah
Date: 2013-12-25 00:00:00
Tasks: Information Retrieval, Question Answering, Retrieval, Translation

The problem of Natural Language Query Formalization (NLQF) is to translate a
given user query in natural language (NL) into a formal language so that the
semantic interpretation has equivalence with the NL interpretation.
Formalization of NL queries enables logic based reasoning during information
retrieval, database query, question-answering, etc. Formalization also helps in
Web query normalization and indexing, query intent analysis, etc. In this paper
we are proposing a Description Logics based formal methodology for wh-query
intent (also called desire) identification and corresponding formal
translation. We evaluated the scalability of our proposed formalism using
Microsoft Encarta 98 query dataset and OWL-S TC v.4.0 dataset.
Analysis of Optimization Techniques to Improve User Response Time of Web Applications and Their Implementation for MOODLE
Authors: Priyanka Manchanda
Date: 2013-09-27 00:00:00
Tasks: Management

Analysis of seven optimization techniques grouped under three categories
(hardware, back-end, and front-end) is done to study the reduction in average
user response time for Modular Object Oriented Dynamic Learning Environment
(Moodle), a Learning Management System which is scripted in PHP5, runs on
Apache web server and utilizes MySQL database software. Before the
implementation of these techniques, performance analysis of Moodle is performed
for varying number of concurrent users. The results obtained for each
optimization technique are then reported in a tabular format. The maximum
reduction in end user response time was achieved for hardware optimization
which requires Moodle server and database to be installed on solid state disk.
Outlier robust system identification: a Bayesian kernel-based approach
Authors: Giulio Bottegal, Aleksandr Y. Aravkin, Hakan Hjalmarsson, Gianluigi Pillonetto
Date: 2013-12-21 00:00:00

In this paper, we propose an outlier-robust regularized kernel-based method
for linear system identification. The unknown impulse response is modeled as a
zero-mean Gaussian process whose covariance (kernel) is given by the recently
proposed stable spline kernel, which encodes information on regularity and
exponential stability. To build robustness to outliers, we model the
measurement noise as realizations of independent Laplacian random variables.
The identification problem is cast in a Bayesian framework, and solved by a new
Markov Chain Monte Carlo (MCMC) scheme. In particular, exploiting the
representation of the Laplacian random variables as scale mixtures of
Gaussians, we design a Gibbs sampler which quickly converges to the target
distribution. Numerical simulations show a substantial improvement in the
accuracy of the estimates over state-of-the-art kernel-based methods.
Deep learning for class-generic object detection
Authors: Brody Huval, Adam Coates, Andrew Ng
Date: 2013-12-24 00:00:00
Tasks: Deep Learning, Object, object-detection, Object Detection

We investigate the use of deep neural networks for the novel task of class
generic object detection. We show that neural networks originally designed for
image recognition can be trained to detect objects within images, regardless of
their class, including objects for which no bounding box labels have been
provided. In addition, we show that bounding box labels yield a 1% performance
increase on the ImageNet recognition challenge.
Exploiting Social Tags for Cross-Domain Collaborative Filtering
Authors: Yue Shi, Martha Larson, Alan Hanjalic
Date: 2013-02-20 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems, TAG, Transfer Learning

One of the most challenging problems in recommender systems based on the
collaborative filtering (CF) concept is data sparseness, i.e., limited user
preference data is available for making recommendations. Cross-domain
collaborative filtering (CDCF) has been studied as an effective mechanism to
alleviate data sparseness of one domain using the knowledge about user
preferences from other domains. A key question to be answered in the context of
CDCF is what common characteristics can be deployed to link different domains
for effective knowledge transfer. In this paper, we assess the usefulness of
user-contributed (social) tags in this respect. We do so by means of the
Generalized Tag-induced Cross-domain Collaborative Filtering (GTagCDCF)
approach that we propose in this paper and that we developed based on the
general collective matrix factorization framework. Assessment is done by a
series of experiments, using publicly available CF datasets that represent
three cross-domain cases, i.e., two two-domain cases and one three-domain case.
A comparative analysis on two-domain cases involving GTagCDCF and several
state-of-the-art CDCF approaches indicates the increased benefit of using
social tags as representatives of explicit links between domains for CDCF as
compared to the implicit links deployed by the existing CDCF methods. In
addition, we show that users from different domains can already benefit from
GTagCDCF if they only share a few common tags. Finally, we use the three-domain
case to validate the robustness of GTagCDCF with respect to the scale of
datasets and the varying number of domains.
Greedy Column Subset Selection for Large-scale Data Sets
Authors: Ahmed K. Farahat, Ahmed Elgohary, Ali Ghodsi, Mohamed S. Kamel
Date: 2013-12-24 00:00:00

In today's information systems, the availability of massive amounts of data
necessitates the development of fast and accurate algorithms to summarize these
data and represent them in a succinct format. One crucial problem in big data
analytics is the selection of representative instances from large and
massively-distributed data, which is formally known as the Column Subset
Selection (CSS) problem. The solution to this problem enables data analysts to
understand the insights of the data and explore its hidden structure. The
selected instances can also be used for data preprocessing tasks such as
learning a low-dimensional embedding of the data points or computing a low-rank
approximation of the corresponding matrix. This paper presents a fast and
accurate greedy algorithm for large-scale column subset selection. The
algorithm minimizes an objective function which measures the reconstruction
error of the data matrix based on the subset of selected columns. The paper
first presents a centralized greedy algorithm for column subset selection which
depends on a novel recursive formula for calculating the reconstruction error
of the data matrix. The paper then presents a MapReduce algorithm which selects
a few representative columns from a matrix whose columns are massively
distributed across several commodity machines. The algorithm first learns a
concise representation of all columns using random projection, and it then
solves a generalized column subset selection problem at each machine in which a
subset of columns are selected from the sub-matrix on that machine such that
the reconstruction error of the concise representation is minimized. The paper
demonstrates the effectiveness and efficiency of the proposed algorithm through
an empirical evaluation on benchmark data sets.
3D Interest Point Detection via Discriminative Learning
Authors: Leizer Teran, Philippos Mordohai
Date: 2013-12-24 00:00:00
Tasks: Binary Classification, Interest Point Detection

The task of detecting the interest points in 3D meshes has typically been
handled by geometric methods. These methods, while greatly describing human
preference, can be ill-equipped for handling the variety and subjectivity in
human responses. Different tasks have different requirements for interest point
detection; some tasks may necessitate high precision while other tasks may
require high recall. Sometimes points with high curvature may be desirable,
while in other cases high curvature may be an indication of noise. Geometric
methods lack the required flexibility to adapt to such changes. As a
consequence, interest point detection seems to be well suited for machine
learning methods that can be trained to match the criteria applied on the
annotated training data. In this paper, we formulate interest point detection
as a supervised binary classification problem using a random forest as our
classifier. Among other challenges, we are faced with an imbalanced learning
problem due to the substantial difference in the priors between interest and
non-interest points. We address this by re-sampling the training set. We
validate the accuracy of our method and compare our results to those of five
state of the art methods on a new, standard benchmark.