pwc-archive/papers-with-abstracts huggingface dataset:
Human Re-identification by Matching Compositional Template with Cluster Sampling
Authors: Yuanlu Xu, Liang Lin, Wei-Shi Zheng, Xiaobai Liu
Date: 2015-02-01 00:00:00
Tasks: Person Re-Identification

This paper aims at a newly raising task in visual surveillance:
re-identifying people at a distance by matching body information, given several
reference examples. Most of existing works solve this task by matching a
reference template with the target individual, but often suffer from large
human appearance variability (e.g. different poses/views, illumination) and
high false positives in matching caused by conjunctions, occlusions or
surrounding clutters. Addressing these problems, we construct a simple yet
expressive template from a few reference images of a certain individual, which
represents the body as an articulated assembly of compositional and alternative
parts, and propose an effective matching algorithm with cluster sampling. This
algorithm is designed within a candidacy graph whose vertices are matching
candidates (i.e. a pair of source and target body parts), and iterates in two
steps for convergence. (i) It generates possible partial matches based on
compatible and competitive relations among body parts. (ii) It confirms the
partial matches to generate a new matching solution, which is accepted by the
Markov Chain Monte Carlo (MCMC) mechanism. In the experiments, we demonstrate
the superior performance of our approach on three public databases compared to
existing methods.
Driver distraction detection and recognition using RGB-D sensor
Authors: Céline Craye, Fakhri Karray
Date: 2015-02-01 00:00:00

Driver inattention assessment has become a very active field in intelligent
transportation systems. Based on active sensor Kinect and computer vision
tools, we have built an efficient module for detecting driver distraction and
recognizing the type of distraction. Based on color and depth map data from the
Kinect, our system is composed of four sub-modules. We call them eye behavior
(detecting gaze and blinking), arm position (is the right arm up, down, right
of forward), head orientation, and facial expressions. Each module produces
relevant information for assessing driver inattention. They are merged together
later on using two different classification strategies: AdaBoost classifier and
Hidden Markov Model. Evaluation is done using a driving simulator and 8 drivers
of different gender, age and nationality for a total of more than 8 hours of
recording. Qualitative and quantitative results show strong and accurate
detection and recognition capacity (85% accuracy for the type of distraction
and 90% for distraction detection). Moreover, each module is obtained
independently and could be used for other types of inference, such as fatigue
detection, and could be implemented for real cars systems.
Injury risk prediction for traffic accidents in Porto Alegre/RS, Brazil
Authors: Christian S. Perone
Date: 2015-02-01 00:00:00
Tasks: BIG-bench Machine Learning

This study describes the experimental application of Machine Learning
techniques to build prediction models that can assess the injury risk
associated with traffic accidents. This work uses an freely available data set
of traffic accident records that took place in the city of Porto Alegre/RS
(Brazil) during the year of 2013. This study also provides an analysis of the
most important attributes of a traffic accident that could produce an outcome
of injury to the people involved in the accident.
Feature selection for classification with class-separability strategy and data envelopment analysis
Authors: Yishi Zhang, Chao Yang, Anrong Yang, Chan Xiong, Xingchi Zhou, Zigang Zhang
Date: 2014-05-06 00:00:00
Tasks: feature selection, General Classification

In this paper, a novel feature selection method is presented, which is based
on Class-Separability (CS) strategy and Data Envelopment Analysis (DEA). To
better capture the relationship between features and the class, class labels
are separated into individual variables and relevance and redundancy are
explicitly handled on each class label. Super-efficiency DEA is employed to
evaluate and rank features via their conditional dependence scores on all class
labels, and the feature with maximum super-efficiency score is then added in
the conditioning set for conditional dependence estimation in the next
iteration, in such a way as to iteratively select features and get the final
selected features. Eventually, experiments are conducted to evaluate the
effectiveness of proposed method comparing with four state-of-the-art methods
from the viewpoint of classification accuracy. Empirical results verify the
feasibility and the superiority of proposed feature selection method.
Feature Selection with Redundancy-complementariness Dispersion
Authors: Zhijun Chen, Chaozhong Wu, Yishi Zhang, Zhen Huang, Bin Ran, Ming Zhong, Nengchao Lyu
Date: 2015-02-01 00:00:00
Tasks: feature selection, General Classification

Feature selection has attracted significant attention in data mining and
machine learning in the past decades. Many existing feature selection methods
eliminate redundancy by measuring pairwise inter-correlation of features,
whereas the complementariness of features and higher inter-correlation among
more than two features are ignored. In this study, a modification item
concerning the complementariness of features is introduced in the evaluation
criterion of features. Additionally, in order to identify the interference
effect of already-selected False Positives (FPs), the
redundancy-complementariness dispersion is also taken into account to adjust
the measurement of pairwise inter-correlation of features. To illustrate the
effectiveness of proposed method, classification experiments are applied with
four frequently used classifiers on ten datasets. Classification results verify
the superiority of proposed method compared with five representative feature
selection methods.
Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning
Authors: Julien Mairal
Date: 2014-02-18 00:00:00
Tasks: BIG-bench Machine Learning

Majorization-minimization algorithms consist of successively minimizing a
sequence of upper bounds of the objective function. These upper bounds are
tight at the current estimate, and each iteration monotonically drives the
objective function downhill. Such a simple principle is widely applicable and
has been very popular in various scientific fields, especially in signal
processing and statistics. In this paper, we propose an incremental
majorization-minimization scheme for minimizing a large sum of continuous
functions, a problem of utmost importance in machine learning. We present
convergence guarantees for non-convex and convex optimization when the upper
bounds approximate the objective up to a smooth error; we call such upper
bounds "first-order surrogate functions". More precisely, we study asymptotic
stationary point guarantees for non-convex problems, and for convex ones, we
provide convergence rates for the expected objective function value. We apply
our scheme to composite optimization and obtain a new incremental proximal
gradient algorithm with linear convergence rate for strongly convex functions.
In our experiments, we show that our method is competitive with the state of
the art for solving machine learning problems such as logistic regression when
the number of training samples is large enough, and we demonstrate its
usefulness for sparse estimation with non-convex penalties.
Falling Rule Lists
Authors: Fulton Wang, Cynthia Rudin
Date: 2014-11-21 00:00:00
Tasks: General Classification

Falling rule lists are classification models consisting of an ordered list of
if-then rules, where (i) the order of rules determines which example should be
classified by each rule, and (ii) the estimated probability of success
decreases monotonically down the list. These kinds of rule lists are inspired
by healthcare applications where patients would be stratified into risk sets
and the highest at-risk patients should be considered first. We provide a
Bayesian framework for learning falling rule lists that does not rely on
traditional greedy decision tree learning methods.
Chemical Reaction Optimization for the Set Covering Problem
Authors: James J. Q. Yu, Albert Y. S. Lam, Victor O. K. Li
Date: 2015-02-01 00:00:00
Tasks: Combinatorial Optimization

The set covering problem (SCP) is one of the representative combinatorial
optimization problems, having many practical applications. This paper
investigates the development of an algorithm to solve SCP by employing chemical
reaction optimization (CRO), a general-purpose metaheuristic. It is tested on a
wide range of benchmark instances of SCP. The simulation results indicate that
this algorithm gives outstanding performance compared with other heuristics and
metaheuristics in solving SCP.
An Inter-molecular Adaptive Collision Scheme for Chemical Reaction Optimization
Authors: James J. Q. Yu, Victor O. K. Li, Albert Y. S. Lam
Date: 2015-02-01 00:00:00
Tasks: Evolutionary Algorithms, global-optimization

Optimization techniques are frequently applied in science and engineering
research and development. Evolutionary algorithms, as a kind of general-purpose
metaheuristic, have been shown to be very effective in solving a wide range of
optimization problems. A recently proposed chemical-reaction-inspired
metaheuristic, Chemical Reaction Optimization (CRO), has been applied to solve
many global optimization problems. However, the functionality of the
inter-molecular ineffective collision operator in the canonical CRO design
overlaps that of the on-wall ineffective collision operator, which can
potential impair the overall performance. In this paper we propose a new
inter-molecular ineffective collision operator for CRO for global optimization.
To fully utilize our newly proposed operator, we also design a scheme to adapt
the algorithm to optimization problems with different search space
characteristics. We analyze the performance of our proposed algorithm with a
number of widely used benchmark functions. The simulation results indicate that
the new algorithm has superior performance over the canonical CRO.
Optimal V2G Scheduling of Electric Vehicles and Unit Commitment using Chemical Reaction Optimization
Authors: James J. Q. Yu, Victor O. K. Li, Albert Y. S. Lam
Date: 2015-02-01 00:00:00
Tasks: Scheduling

An electric vehicle (EV) may be used as energy storage which allows the
bi-directional electricity flow between the vehicle's battery and the electric
power grid. In order to flatten the load profile of the electricity system, EV
scheduling has become a hot research topic in recent years. In this paper, we
propose a new formulation of the joint scheduling of EV and Unit Commitment
(UC), called EVUC. Our formulation considers the characteristics of EVs while
optimizing the system total running cost. We employ Chemical Reaction
Optimization (CRO), a general-purpose optimization algorithm to solve this
problem and the simulation results on a widely used set of instances indicate
that CRO can effectively optimize this problem.
Sensor Deployment for Air Pollution Monitoring Using Public Transportation System
Authors: James J. Q. Yu, Victor O. K. Li, Albert Y. S. Lam
Date: 2015-02-01 00:00:00

Air pollution monitoring is a very popular research topic and many monitoring
systems have been developed. In this paper, we formulate the Bus Sensor
Deployment Problem (BSDP) to select the bus routes on which sensors are
deployed, and we use Chemical Reaction Optimization (CRO) to solve BSDP. CRO is
a recently proposed metaheuristic designed to solve a wide range of
optimization problems. Using the real world data, namely Hong Kong Island bus
route data, we perform a series of simulations and the results show that CRO is
capable of solving this optimization problem efficiently.
Real-Coded Chemical Reaction Optimization with Different Perturbation Functions
Authors: James J. Q. Yu, Albert Y. S. Lam, Victor O. K. Li
Date: 2015-02-01 00:00:00

Chemical Reaction Optimization (CRO) is a powerful metaheuristic which mimics
the interactions of molecules in chemical reactions to search for the global
optimum. The perturbation function greatly influences the performance of CRO on
solving different continuous problems. In this paper, we study four different
probability distributions, namely, the Gaussian distribution, the Cauchy
distribution, the exponential distribution, and a modified Rayleigh
distribution, for the perturbation function of CRO. Different distributions
have different impacts on the solutions. The distributions are tested by a set
of well-known benchmark functions and simulation results show that problems
with different characteristics have different preference on the distribution
function. Our study gives guidelines to design CRO for different types of
optimization problems.
Evolutionary Artificial Neural Network Based on Chemical Reaction Optimization
Authors: James J. Q. Yu, Albert Y. S. Lam, Victor O. K. Li
Date: 2015-02-01 00:00:00
Tasks: Evolutionary Algorithms, global-optimization

Evolutionary algorithms (EAs) are very popular tools to design and evolve
artificial neural networks (ANNs), especially to train them. These methods have
advantages over the conventional backpropagation (BP) method because of their
low computational requirement when searching in a large solution space. In this
paper, we employ Chemical Reaction Optimization (CRO), a newly developed global
optimization method, to replace BP in training neural networks. CRO is a
population-based metaheuristics mimicking the transition of molecules and their
interactions in a chemical reaction. Simulation results show that CRO
outperforms many EA strategies commonly used to train neural networks.
Pose and Shape Estimation with Discriminatively Learned Parts
Authors: Menglong Zhu, Xiaowei Zhou, Kostas Daniilidis
Date: 2015-02-01 00:00:00

We introduce a new approach for estimating the 3D pose and the 3D shape of an
object from a single image. Given a training set of view exemplars, we learn
and select appearance-based discriminative parts which are mapped onto the 3D
model from the training set through a facil- ity location optimization. The
training set of 3D models is summarized into a sparse set of shapes from which
we can generalize by linear combination. Given a test picture, we detect
hypotheses for each part. The main challenge is to select from these hypotheses
and compute the 3D pose and shape coefficients at the same time. To achieve
this, we optimize a function that minimizes simultaneously the geometric
reprojection error as well as the appearance matching of the parts. We apply
the alternating direction method of multipliers (ADMM) to minimize the
resulting convex function. We evaluate our approach on the Fine Grained 3D Car
dataset with superior performance in shape and pose errors. Our main and novel
contribution is the simultaneous solution for part localization, 3D pose and
shape by maximizing both geometric and appearance compatibility.
Tracing the boundaries of materials in transparent vessels using computer vision
Authors: Sagi Eppel
Date: 2015-01-20 00:00:00

Visual recognition of material boundaries in transparent vessels is valuable
for numerous applications. Such recognition is essential for estimation of
fill-level, volume and phase-boundaries as well as for tracking of such
chemical processes as precipitation, crystallization, condensation, evaporation
and phase-separation. The problem of material boundary recognition in images is
particularly complex for materials with non-flat surfaces, i.e., solids,
powders and viscous fluids, in which the material interfaces have unpredictable
shapes. This work demonstrates a general method for finding the boundaries of
materials inside transparent containers in images. The method uses an image of
the transparent vessel containing the material and the boundary of the vessel
in this image. The recognition is based on the assumption that the material
boundary appears in the image in the form of a curve (with various constraints)
whose endpoints are both positioned on the vessel contour. The probability that
a curve matches the material boundary in the image is evaluated using a cost
function based on some image properties along this curve. Several image
properties were examined as indicators for the material boundary. The optimal
boundary curve was found using Dijkstra's algorithm. The method was
successfully examined for recognition of various types of phase-boundaries,
including liquid-air, solid-air and solid-liquid interfaces, as well as for
various types of glassware containers from everyday life and the chemistry
laboratory (i.e., bottles, beakers, flasks, jars, columns, vials and
separation-funnels). In addition, the method can be easily extended to
materials carried on top of carrier vessels (i.e., plates, spoons, spatulas).
An evaluation framework for event detection using a morphological model of acoustic scenes
Authors: Mathieu Lagrange, Grégoire Lafay, Mathias Rossignol, Emmanouil Benetos, Axel Roebel
Date: 2015-01-31 00:00:00
Tasks: Event Detection

This paper introduces a model of environmental acoustic scenes which adopts a
morphological approach by ab-stracting temporal structures of acoustic scenes.
To demonstrate its potential, this model is employed to evaluate the
performance of a large set of acoustic events detection systems. This model
allows us to explicitly control key morphological aspects of the acoustic scene
and isolate their impact on the performance of the system under evaluation.
Thus, more information can be gained on the behavior of evaluated systems,
providing guidance for further improvements. The proposed model is validated
using submitted systems from the IEEE DCASE Challenge; results indicate that
the proposed scheme is able to successfully build datasets useful for
evaluating some aspects the performance of event detection systems, more
particularly their robustness to new listening conditions and the increasing
level of background sounds.
Sparse Dueling Bandits
Authors: Kevin Jamieson, Sumeet Katariya, Atul Deshpande, Robert Nowak
Date: 2015-01-31 00:00:00

The dueling bandit problem is a variation of the classical multi-armed bandit
in which the allowable actions are noisy comparisons between pairs of arms.
This paper focuses on a new approach for finding the "best" arm according to
the Borda criterion using noisy comparisons. We prove that in the absence of
structural assumptions, the sample complexity of this problem is proportional
to the sum of the inverse squared gaps between the Borda scores of each
suboptimal arm and the best arm. We explore this dependence further and
consider structural constraints on the pairwise comparison matrix (a particular
form of sparsity natural to this problem) that can significantly reduce the
sample complexity. This motivates a new algorithm called Successive Elimination
with Comparison Sparsity (SECS) that exploits sparsity to find the Borda winner
using fewer samples than standard algorithms. We also evaluate the new
algorithm experimentally with synthetic and real data. The results show that
the sparsity model and the new algorithm can provide significant improvements
over standard approaches.
The Search for Computational Intelligence
Authors: Joseph Corneli, Ewen Maclean
Date: 2015-01-31 00:00:00

We define and explore in simulation several rules for the local evolution of
generative rules for 1D and 2D cellular automata. Our implementation uses
strategies from conceptual blending. We discuss potential applications to
modelling social dynamics.
Optimized Projection for Sparse Representation Based Classification
Authors: Can-Yi Lu, De-Shuang Huang
Date: 2015-01-31 00:00:00
Tasks: Classification, Dimensionality Reduction, Face Recognition, General Classification, Sparse Representation-based Classification

Dimensionality reduction (DR) methods have been commonly used as a principled
way to understand the high-dimensional data such as facial images. In this
paper, we propose a new supervised DR method called Optimized Projection for
Sparse Representation based Classification (OP-SRC), which is based on the
recent face recognition method, Sparse Representation based Classification
(SRC). SRC seeks a sparse linear combination on all the training data for a
given query image, and make the decision by the minimal reconstruction
residual. OP-SRC is designed on the decision rule of SRC, it aims to reduce the
within-class reconstruction residual and simultaneously increase the
between-class reconstruction residual on the training data. The projections are
optimized and match well with the mechanism of SRC. Therefore, SRC performs
well in the OP-SRC transformed space. The feasibility and effectiveness of the
proposed method is verified on the Yale, ORL and UMIST databases with promising
results.
Twitter Hash Tag Recommendation
Authors: Roman Dovgopol, Matt Nohelty
Date: 2015-01-31 00:00:00
Tasks: General Classification, TAG

The rise in popularity of microblogging services like Twitter has led to
increased use of content annotation strategies like the hashtag. Hashtags
provide users with a tagging mechanism to help organize, group, and create
visibility for their posts. This is a simple idea but can be challenging for
the user in practice which leads to infrequent usage. In this paper, we will
investigate various methods of recommending hashtags as new posts are created
to encourage more widespread adoption and usage. Hashtag recommendation comes
with numerous challenges including processing huge volumes of streaming data
and content which is small and noisy. We will investigate preprocessing methods
to reduce noise in the data and determine an effective method of hashtag
recommendation based on the popular classification algorithms.
Deep learning of fMRI big data: a novel approach to subject-transfer decoding
Authors: Sotetsu Koyamada, Yumi Shikauchi, Ken Nakae, Masanori Koyama, Shin Ishii
Date: 2015-01-31 00:00:00
Tasks: Brain Decoding, Decoder, Subject Transfer

As a technology to read brain states from measurable brain activities, brain
decoding are widely applied in industries and medical sciences. In spite of
high demands in these applications for a universal decoder that can be applied
to all individuals simultaneously, large variation in brain activities across
individuals has limited the scope of many studies to the development of
individual-specific decoders. In this study, we used deep neural network (DNN),
a nonlinear hierarchical model, to construct a subject-transfer decoder. Our
decoder is the first successful DNN-based subject-transfer decoder. When
applied to a large-scale functional magnetic resonance imaging (fMRI) database,
our DNN-based decoder achieved higher decoding accuracy than other baseline
methods, including support vector machine (SVM). In order to analyze the
knowledge acquired by this decoder, we applied principal sensitivity analysis
(PSA) to the decoder and visualized the discriminative features that are common
to all subjects in the dataset. Our PSA successfully visualized the
subject-independent features contributing to the subject-transferability of the
trained decoder.