pwc-archive/papers-with-abstracts huggingface dataset:
A First Empirical Study of Emphatic Temporal Difference Learning
Authors: Sina Ghiassian, Banafsheh Rafiee, Richard S. Sutton
Date: 2017-05-11 00:00:00

In this paper we present the first empirical study of the emphatic
temporal-difference learning algorithm (ETD), comparing it with conventional
temporal-difference learning, in particular, with linear TD(0), on on-policy
and off-policy variations of the Mountain Car problem. The initial motivation
for developing ETD was that it has good convergence properties under off-policy
training (Sutton, Mahmood and White 2016), but it is also a new algorithm for
the on-policy case. In both our on-policy and off-policy experiments, we found
that each method converged to a characteristic asymptotic level of error, with
ETD better than TD(0). TD(0) achieved a still lower error level temporarily
before falling back to its higher asymptote, whereas ETD never showed this kind
of "bounce". In the off-policy case (in which TD(0) is not guaranteed to
converge), ETD was significantly slower.
Iteratively-Reweighted Least-Squares Fitting of Support Vector Machines: A Majorization--Minimization Algorithm Approach
Authors: Hien D. Nguyen, Geoffrey J. McLachlan
Date: 2017-05-12 00:00:00

Support vector machines (SVMs) are an important tool in modern data analysis.
Traditionally, support vector machines have been fitted via quadratic
programming, either using purpose-built or off-the-shelf algorithms. We present
an alternative approach to SVM fitting via the majorization--minimization (MM)
paradigm. Algorithms that are derived via MM algorithm constructions can be
shown to monotonically decrease their objectives at each iteration, as well as
be globally convergent to stationary points. We demonstrate the construction of
iteratively-reweighted least-squares (IRLS) algorithms, via the MM paradigm,
for SVM risk minimization problems involving the hinge, least-square,
squared-hinge, and logistic losses, and 1-norm, 2-norm, and elastic net
penalizations. Successful implementations of our algorithms are presented via
some numerical examples.
Single Image Action Recognition by Predicting Space-Time Saliency
Authors: Marjaneh Safaei, Hassan Foroosh
Date: 2017-05-12 00:00:00
Tasks: Action Classification, Action Recognition, Domain Adaptation, General Classification, Optical Flow Estimation, Temporal Action Localization, Transfer Learning

We propose a novel approach based on deep Convolutional Neural Networks (CNN)
to recognize human actions in still images by predicting the future motion, and
detecting the shape and location of the salient parts of the image. We make the
following major contributions to this important area of research: (i) We use
the predicted future motion in the static image (Walker et al., 2015) as a
means of compensating for the missing temporal information, while using the
saliency map to represent the the spatial information in the form of location
and shape of what is predicted as significant. (ii) We cast action
classification in static images as a domain adaptation problem by transfer
learning. We first map the input static image to a new domain that we refer to
as the Predicted Optical Flow-Saliency Map domain (POF-SM), and then fine-tune
the layers of a deep CNN model trained on classifying the ImageNet dataset to
perform action classification in the POF-SM domain. (iii) We tested our method
on the popular Willow dataset. But unlike existing methods, we also tested on a
more realistic and challenging dataset of over 2M still images that we
collected and labeled by taking random frames from the UCF-101 video dataset.
We call our dataset the UCF Still Image dataset or UCFSI-101 in short. Our
results outperform the state of the art.
Exploiting variable associations to configure efficient local search algorithms in large-scale binary integer programs
Authors: Shunji Umetani
Date: 2016-04-28 00:00:00

We present a data mining approach for reducing the search space of local
search algorithms in a class of binary integer programs including the set
covering and partitioning problems. The quality of locally optimal solutions
typically improves if a larger neighborhood is used, while the computation time
of searching the neighborhood increases exponentially. To overcome this, we
extract variable associations from the instance to be solved in order to
identify promising pairs of flipping variables in the neighborhood search.
Based on this, we develop a 4-flip neighborhood local search algorithm that
incorporates an efficient incremental evaluation of solutions and an adaptive
control of penalty weights. Computational results show that the proposed method
improves the performance of the local search algorithm for large-scale set
covering and partitioning problems.
Clingcon: The Next Generation
Authors: Mutsunori Banbara, Benjamin Kaufmann, Max Ostrowski, Torsten Schaub
Date: 2017-05-12 00:00:00

We present the third generation of the constraint answer set system clingcon,
combining Answer Set Programming (ASP) with finite domain constraint processing
(CP). While its predecessors rely on a black-box approach to hybrid solving by
integrating the CP solver gecode, the new clingcon system pursues a lazy
approach using dedicated constraint propagators to extend propagation in the
underlying ASP solver clasp. No extension is needed for parsing and grounding
clingcon's hybrid modeling language since both can be accommodated by the new
generic theory handling capabilities of the ASP grounder gringo. As a whole,
clingcon 3 is thus an extension of the ASP system clingo 5, which itself relies
on the grounder gringo and the solver clasp. The new approach of clingcon
offers a seamless integration of CP propagation into ASP solving that benefits
from the whole spectrum of clasp's reasoning modes, including for instance
multi-shot solving and advanced optimization techniques. This is accomplished
by a lazy approach that unfolds the representation of constraints and adds it
to that of the logic program only when needed. Although the unfolding is
usually dictated by the constraint propagators during solving, it can already
be partially (or even totally) done during preprocessing. Moreover, clingcon's
constraint preprocessing and propagation incorporate several well established
CP techniques that greatly improve its performance. We demonstrate this via an
extensive empirical evaluation contrasting, first, the various techniques in
the context of CSP solving and, second, the new clingcon system with other
hybrid ASP systems. Under consideration in Theory and Practice of Logic
Programming (TPLP)
Autonomous and Connected Intersection Crossing Traffic Management using Discrete-Time Occupancies Trajectory
Authors: Qiang Lu, Kyoung-Dae Kim
Date: 2017-05-12 00:00:00
Tasks: Computational Efficiency, Management

In this paper, we address a problem of safe and efficient intersection
crossing traffic management of autonomous and connected ground traffic. Toward
this objective, we propose an algorithm that is called the Discrete-time
occupancies trajectory based Intersection traffic Coordination Algorithm
(DICA). We first prove that the basic DICA is deadlock free and also starvation
free. Then, we show that the basic DICA has a computational complexity of
$\mathcal{O}(n^2 L_m^3)$ where $n$ is the number of vehicles granted to cross
an intersection and $L_m$ is the maximum length of intersection crossing
routes.
  To improve the overall computational efficiency of the algorithm, the basic
DICA is enhanced by several computational approaches that are proposed in this
paper. The enhanced algorithm has the computational complexity of
$\mathcal{O}(n^2 L_m \log_2 L_m)$. The improved computational efficiency of the
enhanced algorithm is validated through simulation using an open source traffic
simulator, called the Simulation of Urban MObility (SUMO). The overall
throughput as well as the computational efficiency of the enhanced algorithm
are also compared with those of an optimized traffic light control.
Quality and Diversity Optimization: A Unifying Modular Framework
Authors: Antoine Cully, Yiannis Demiris
Date: 2017-05-12 00:00:00
Tasks: Diversity, Management

The optimization of functions to find the best solution according to one or
several objectives has a central role in many engineering and research fields.
Recently, a new family of optimization algorithms, named Quality-Diversity
optimization, has been introduced, and contrasts with classic algorithms.
Instead of searching for a single solution, Quality-Diversity algorithms are
searching for a large collection of both diverse and high-performing solutions.
The role of this collection is to cover the range of possible solution types as
much as possible, and to contain the best solution for each type. The
contribution of this paper is threefold. Firstly, we present a unifying
framework of Quality-Diversity optimization algorithms that covers the two main
algorithms of this family (Multi-dimensional Archive of Phenotypic Elites and
the Novelty Search with Local Competition), and that highlights the large
variety of variants that can be investigated within this family. Secondly, we
propose algorithms with a new selection mechanism for Quality-Diversity
algorithms that outperforms all the algorithms tested in this paper. Lastly, we
present a new collection management that overcomes the erosion issues observed
when using unstructured collections. These three contributions are supported by
extensive experimental comparisons of Quality-Diversity algorithms on three
different experimental scenarios.
Variance Based Moving K-Means Algorithm
Authors: Vibin Vijay, Raghunath Vp, Amarjot Singh, SN Omar
Date: 2017-04-07 00:00:00
Tasks: Clustering

Clustering is a useful data exploratory method with its wide applicability in
multiple fields. However, data clustering greatly relies on initialization of
cluster centers that can result in large intra-cluster variance and dead
centers, therefore leading to sub-optimal solutions. This paper proposes a
novel variance based version of the conventional Moving K-Means (MKM) algorithm
called Variance Based Moving K-Means (VMKM) that can partition data into
optimal homogeneous clusters, irrespective of cluster initialization. The
algorithm utilizes a novel distance metric and a unique data element selection
criteria to transfer the selected elements between clusters to achieve low
intra-cluster variance and subsequently avoid dead centers. Quantitative and
qualitative comparison with various clustering techniques is performed on four
datasets selected from image processing, bioinformatics, remote sensing and the
stock market respectively. An extensive analysis highlights the superior
performance of the proposed method over other techniques.
A natural approach to studying schema processing
Authors: Jack McKay Fletcher, Thomas Wennekers
Date: 2017-05-12 00:00:00

The Building Block Hypothesis (BBH) states that adaptive systems combine good
partial solutions (so-called building blocks) to find increasingly better
solutions. It is thought that Genetic Algorithms (GAs) implement the BBH.
However, for GAs building blocks are semi-theoretical objects in that they are
thought only to be implicitly exploited via the selection and crossover
operations of a GA. In the current work, we discover a mathematical method to
identify the complete set of schemata present in a given population of a GA; as
such a natural way to study schema processing (and thus the BBH) is revealed.
We demonstrate how this approach can be used both theoretically and
experimentally. Theoretically, we show that the search space for good schemata
is a complete lattice and that each generation samples a complete sub-lattice
of this search space. In addition, we show that combining schemata can only
explore a subset of the search space. Experimentally, we compare how well
different crossover methods combine building blocks. We find that for most
crossover methods approximately 25-35% of building blocks in a generation
result from the combination of the previous generation's building blocks. We
also find that an increase in the combination of building blocks does not lead
to an increase in the efficiency of a GA. To complement this article, we
introduce an open source Python package called schematax, which allows one to
calculate the schemata present in a population using the methods described in
this article.
Detection of irregular QRS complexes using Hermite Transform and Support Vector Machine
Authors: Zoja Vulaj, Milos Brajovic, Andjela Draganic, Irena Orovic
Date: 2017-05-12 00:00:00
Tasks: General Classification

Computer based recognition and detection of abnormalities in ECG signals is
proposed. For this purpose, the Support Vector Machines (SVM) are combined with
the advantages of Hermite transform representation. SVM represent a special
type of classification techniques commonly used in medical applications.
Automatic classification of ECG could make the work of cardiologic departments
faster and more efficient. It would also reduce the number of false diagnosis
and, as a result, save lives. The working principle of the SVM is based on
translating the data into a high dimensional feature space and separating it
using a linear classificator. In order to provide an optimal representation for
SVM application, the Hermite transform domain is used. This domain is proved to
be suitable because of the similarity of the QRS complex with Hermite basis
functions. The maximal signal information is obtained using a small set of
features that are used for detection of irregular QRS complexes. The aim of the
paper is to show that these features can be employed for automatic ECG signal
analysis.
All the people around me: face discovery in egocentric photo-streams
Authors: Maedeh Aghaei, Mariella Dimiccoli, Petia Radeva
Date: 2017-03-06 00:00:00
Tasks: All, Clustering

Given an unconstrained stream of images captured by a wearable photo-camera
(2fpm), we propose an unsupervised bottom-up approach for automatic clustering
appearing faces into the individual identities present in these data. The
problem is challenging since images are acquired under real world conditions;
hence the visible appearance of the people in the images undergoes intensive
variations. Our proposed pipeline consists of first arranging the photo-stream
into events, later, localizing the appearance of multiple people in them, and
finally, grouping various appearances of the same person across different
events. Experimental results performed on a dataset acquired by wearing a
photo-camera during one month, demonstrate the effectiveness of the proposed
approach for the considered purpose.
With Whom Do I Interact? Detecting Social Interactions in Egocentric Photo-streams
Authors: Maedeh Aghaei, Mariella Dimiccoli, Petia Radeva
Date: 2016-05-13 00:00:00
Tasks: Time Series, Time Series Analysis

Given a user wearing a low frame rate wearable camera during a day, this work
aims to automatically detect the moments when the user gets engaged into a
social interaction solely by reviewing the automatically captured photos by the
worn camera. The proposed method, inspired by the sociological concept of
F-formation, exploits distance and orientation of the appearing individuals
-with respect to the user- in the scene from a bird-view perspective. As a
result, the interaction pattern over the sequence can be understood as a
two-dimensional time series that corresponds to the temporal evolution of the
distance and orientation features over time. A Long-Short Term Memory-based
Recurrent Neural Network is then trained to classify each time series.
Experimental evaluation over a dataset of 30.000 images has shown promising
results on the proposed method for social interaction detection in egocentric
photo-streams.
Spatial-Temporal Recurrent Neural Network for Emotion Recognition
Authors: Tong Zhang, Wenming Zheng, Zhen Cui, Yuan Zong, Yang Li
Date: 2017-05-12 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Emotion Recognition

Emotion analysis is a crucial problem to endow artifact machines with real
intelligence in many large potential applications. As external appearances of
human emotions, electroencephalogram (EEG) signals and video face signals are
widely used to track and analyze human's affective information. According to
their common characteristics of spatial-temporal volumes, in this paper we
propose a novel deep learning framework named spatial-temporal recurrent neural
network (STRNN) to unify the learning of two different signal sources into a
spatial-temporal dependency model. In STRNN, to capture those spatially
cooccurrent variations of human emotions, a multi-directional recurrent neural
network (RNN) layer is employed to capture longrange contextual cues by
traversing the spatial region of each time slice from multiple angles. Then a
bi-directional temporal RNN layer is further used to learn discriminative
temporal dependencies from the sequences concatenating spatial features of each
time slice produced from the spatial RNN layer. To further select those salient
regions of emotion representation, we impose sparse projection onto those
hidden states of spatial and temporal domains, which actually also increases
the model discriminant ability because of this global consideration.
Consequently, such a two-layer RNN model builds spatial dependencies as well as
temporal dependencies of the input signals. Experimental results on the public
emotion datasets of EEG and facial expression demonstrate the proposed STRNN
method is more competitive over those state-of-the-art methods.
External Prior Guided Internal Prior Learning for Real-World Noisy Image Denoising
Authors: Jun Xu, Lei Zhang, David Zhang
Date: 2017-05-12 00:00:00
Tasks: Denoising, Image Denoising

Most of existing image denoising methods learn image priors from either
external data or the noisy image itself to remove noise. However, priors
learned from external data may not be adaptive to the image to be denoised,
while priors learned from the given noisy image may not be accurate due to the
interference of corrupted noise. Meanwhile, the noise in real-world noisy
images is very complex, which is hard to be described by simple distributions
such as Gaussian distribution, making real-world noisy image denoising a very
challenging problem. We propose to exploit the information in both external
data and the given noisy image, and develop an external prior guided internal
prior learning method for real-world noisy image denoising. We first learn
external priors from an independent set of clean natural images. With the aid
of learned external priors, we then learn internal priors from the given noisy
image to refine the prior model. The external and internal priors are
formulated as a set of orthogonal dictionaries to efficiently reconstruct the
desired image. Extensive experiments are performed on several real-world noisy
image datasets. The proposed method demonstrates highly competitive denoising
performance, outperforming state-of-the-art denoising methods including those
designed for real-world noisy images.
User Bias Removal in Review Score Prediction
Authors: Rahul Wadbude, Vivek Gupta, Dheeraj Mekala, Harish Karnick
Date: 2016-12-20 00:00:00
Tasks: Prediction, Recommendation Systems

Review score prediction of text reviews has recently gained a lot of
attention in recommendation systems. A major problem in models for review score
prediction is the presence of noise due to user-bias in review scores. We
propose two simple statistical methods to remove such noise and improve review
score prediction. Compared to other methods that use multiple classifiers, one
for each user, our model uses a single global classifier to predict review
scores. We empirically evaluate our methods on two major categories
(\textit{Electronics} and \textit{Movies and TV}) of the SNAP published Amazon
e-Commerce Reviews data-set and Amazon \textit{Fine Food} reviews data-set. We
obtain improved review score prediction for three commonly used text feature
representations.
SCDV : Sparse Composite Document Vectors using soft clustering over distributional representations
Authors: Dheeraj Mekala, Vivek Gupta, Bhargavi Paranjape, Harish Karnick
Date: 2016-12-20 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Clustering, Information Retrieval, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Retrieval, Text Classification

We present a feature vector formation technique for documents - Sparse
Composite Document Vector (SCDV) - which overcomes several shortcomings of the
current distributional paragraph vector representations that are widely used
for text representation. In SCDV, word embedding's are clustered to capture
multiple semantic contexts in which words occur. They are then chained together
to form document topic-vectors that can express complex, multi-topic documents.
Through extensive experiments on multi-class and multi-label classification
tasks, we outperform the previous state-of-the-art method, NTSG (Liu et al.,
2015a). We also show that SCDV embedding's perform well on heterogeneous tasks
like Topic Coherence, context-sensitive Learning and Information Retrieval.
Moreover, we achieve significant reduction in training and prediction times
compared to other representation methods. SCDV achieves best of both worlds -
better performance with lower time and space complexity.
TraX: The visual Tracking eXchange Protocol and Library
Authors: Luka Čehovin
Date: 2017-05-12 00:00:00
Tasks: Visual Tracking

In this paper we address the problem of developing on-line visual tracking
algorithms. We present a specialized communication protocol that serves as a
bridge between a tracker implementation and utilizing application. It decouples
development of algorithms and application, encouraging re-usability. The
primary use case is algorithm evaluation where the protocol facilitates more
complex evaluation scenarios that are used nowadays thus pushing forward the
field of visual tracking. We present a reference implementation of the protocol
that makes it easy to use in several popular programming languages and discuss
where the protocol is already used and some usage scenarios that we envision
for the future.
Learning to Refine Object Contours with a Top-Down Fully Convolutional Encoder-Decoder Network
Authors: Yahui Liu, Jian Yao, Li Li, Xiaohu Lu, Jing Han
Date: 2017-05-12 00:00:00
Tasks: Contour Detection, Decoder

We develop a novel deep contour detection algorithm with a top-down fully
convolutional encoder-decoder network. Our proposed method, named TD-CEDN,
solves two important issues in this low-level vision problem: (1) learning
multi-scale and multi-level features; and (2) applying an effective top-down
refined approach in the networks. TD-CEDN performs the pixel-wise prediction by
means of leveraging features at all layers of the net. Unlike skip connections
and previous encoder-decoder methods, we first learn a coarse feature map after
the encoder stage in a feedforward pass, and then refine this feature map in a
top-down strategy during the decoder stage utilizing features at successively
lower layers. Therefore, the deconvolutional process is conducted stepwise,
which is guided by Deeply-Supervision Net providing the integrated direct
supervision. The above proposed technologies lead to a more precise and clearer
prediction. Our proposed algorithm achieved the state-of-the-art on the BSDS500
dataset (ODS F-score of 0.788), the PASCAL VOC2012 dataset (ODS F-score of
0.588), and and the NYU Depth dataset (ODS F-score of 0.735).