pwc-archive/papers-with-abstracts huggingface dataset:
The state of play of ASC-Inclusion: An Integrated Internet-Based Environment for Social Inclusion of Children with Autism Spectrum Conditions
Authors: Björn Schuller, Erik Marchi, Simon Baron-Cohen, Helen O'Reilly, Delia Pigat, Peter Robinson, Ian Daves
Date: 2014-03-24 00:00:00

Individuals with Autism Spectrum Conditions (ASC) have marked difficulties
using verbal and non-verbal communication for social interaction. The running
ASC-Inclusion project aims to help children with ASC by allowing them to learn
how emotions can be expressed and recognised via playing games in a virtual
world. The platform includes analysis of users' gestures, facial, and vocal
expressions using standard microphone and web-cam or a depth sensor, training
through games, text communication with peers, animation, video and audio clips.
We present the state of play in realising such a serious game platform and
provide results for the different modalities.
Nonlinear Eigenproblems in Data Analysis - Balanced Graph Cuts and the RatioDCA-Prox
Authors: Leonardo Jost, Simon Setzer, Matthias Hein
Date: 2013-12-18 00:00:00

It has been recently shown that a large class of balanced graph cuts allows
for an exact relaxation into a nonlinear eigenproblem. We review briefly some
of these results and propose a family of algorithms to compute nonlinear
eigenvectors which encompasses previous work as special cases. We provide a
detailed analysis of the properties and the convergence behavior of these
algorithms and then discuss their application in the area of balanced graph
cuts.
Non-uniform Feature Sampling for Decision Tree Ensembles
Authors: Anastasios Kyrillidis, Anastasios Zouzias
Date: 2014-03-24 00:00:00
Tasks: feature selection, General Classification

We study the effectiveness of non-uniform randomized feature selection in
decision tree classification. We experimentally evaluate two feature selection
methodologies, based on information extracted from the provided dataset: $(i)$
\emph{leverage scores-based} and $(ii)$ \emph{norm-based} feature selection.
Experimental evaluation of the proposed feature selection techniques indicate
that such approaches might be more effective compared to naive uniform feature
selection and moreover having comparable performance to the random forest
algorithm [3]
Block Motion Based Dynamic Texture Analysis: A Review
Authors: Akhlaqur Rahman, Sumaira Tasnim
Date: 2014-03-24 00:00:00
Tasks: Texture Classification

Dynamic texture refers to image sequences of non-rigid objects that exhibit
some regularity in their movement. Videos of smoke, fire etc. fall under the
category of dynamic texture. Researchers have investigated different ways to
analyze dynamic textures since early nineties. Both appearance based (image
intensities) and motion based approaches are investigated. Motion based
approaches turn out to be more effective. A group of researchers have
investigated ways to utilize the motion vectors readily available with the
blocks in video codes like MGEG/H26X. In this paper we provide a review of the
dynamic texture analysis methods using block motion. Research into dynamic
texture analysis using block motion includes recognition, motion computation,
segmentation, and synthesis. We provide a comprehensive review of these
approaches.
MCL-3D: a database for stereoscopic image quality assessment using 2D-image-plus-depth source
Authors: Rui Song, Hyunsuk Ko, C. -C. Jay Kuo
Date: 2014-03-23 00:00:00
Tasks: Benchmarking, Image Quality Assessment, Stereoscopic image quality assessment

A new stereoscopic image quality assessment database rendered using the
2D-image-plus-depth source, called MCL-3D, is described and the performance
benchmarking of several known 2D and 3D image quality metrics using the MCL-3D
database is presented in this work. Nine image-plus-depth sources are first
selected, and a depth image-based rendering (DIBR) technique is used to render
stereoscopic image pairs. Distortions applied to either the texture image or
the depth image before stereoscopic image rendering include: Gaussian blur,
additive white noise, down-sampling blur, JPEG and JPEG-2000 (JP2K) compression
and transmission error. Furthermore, the distortion caused by imperfect
rendering is also examined. The MCL-3D database contains 693 stereoscopic image
pairs, where one third of them are of resolution 1024x728 and two thirds are of
resolution 1920x1080. The pair-wise comparison was adopted in the subjective
test for user friendliness, and the Mean Opinion Score (MOS) can be computed
accordingly. Finally, we evaluate the performance of several 2D and 3D image
quality metrics applied to MCL-3D. All texture images, depth images, rendered
image pairs in MCL-3D and their MOS values obtained in the subjective test are
available to the public (http://mcl.usc.edu/mcl-3d-database/) for future
research and development.
Analysis of Distributed Stochastic Dual Coordinate Ascent
Authors: Tianbao Yang, Shenghuo Zhu, Rong Jin, Yuanqing Lin
Date: 2013-12-04 00:00:00

In \citep{Yangnips13}, the author presented distributed stochastic dual
coordinate ascent (DisDCA) algorithms for solving large-scale regularized loss
minimization. Extraordinary performances have been observed and reported for
the well-motivated updates, as referred to the practical updates, compared to
the naive updates. However, no serious analysis has been provided to understand
the updates and therefore the convergence rates. In the paper, we bridge the
gap by providing a theoretical analysis of the convergence rates of the
practical DisDCA algorithm. Our analysis helped by empirical studies has shown
that it could yield an exponential speed-up in the convergence by increasing
the number of dual updates at each iteration. This result justifies the
superior performances of the practical DisDCA as compared to the naive variant.
As a byproduct, our analysis also reveals the convergence behavior of the
one-communication DisDCA.
Human brain distinctiveness based on EEG spectral coherence connectivity
Authors: Daria La Rocca, Patrizio Campisi, Balazs Vegso, Peter Cserti, Gyorgy Kozmann, Fabio Babiloni, Fabrizio De Vico Fallani
Date: 2014-03-23 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Functional Connectivity

The use of EEG biometrics, for the purpose of automatic people recognition,
has received increasing attention in the recent years. Most of current analysis
rely on the extraction of features characterizing the activity of single brain
regions, like power-spectrum estimates, thus neglecting possible temporal
dependencies between the generated EEG signals. However, important
physiological information can be extracted from the way different brain regions
are functionally coupled. In this study, we propose a novel approach that fuses
spectral coherencebased connectivity between different brain regions as a
possibly viable biometric feature. The proposed approach is tested on a large
dataset of subjects (N=108) during eyes-closed (EC) and eyes-open (EO) resting
state conditions. The obtained recognition performances show that using brain
connectivity leads to higher distinctiveness with respect to power-spectrum
measurements, in both the experimental conditions. Notably, a 100% recognition
accuracy is obtained in EC and EO when integrating functional connectivity
between regions in the frontal lobe, while a lower 97.41% is obtained in EC
(96.26% in EO) when fusing power spectrum information from centro-parietal
regions. Taken together, these results suggest that functional connectivity
patterns represent effective features for improving EEG-based biometric
systems.
D-CFPR: D numbers extended consistent fuzzy preference relations
Authors: Xinyang Deng, Felix T. S. Chan, Rehan Sadiq, Sankaran Mahadevan, Yong Deng
Date: 2014-03-23 00:00:00
Tasks: Decision Making, Relation

How to express an expert's or a decision maker's preference for alternatives
is an open issue. Consistent fuzzy preference relation (CFPR) is with big
advantages to handle this problem due to it can be construed via a smaller
number of pairwise comparisons and satisfies additive transitivity property.
However, the CFPR is incapable of dealing with the cases involving uncertain
and incomplete information. In this paper, a D numbers extended consistent
fuzzy preference relation (D-CFPR) is proposed to overcome the weakness. The
D-CFPR extends the classical CFPR by using a new model of expressing uncertain
information called D numbers. The D-CFPR inherits the merits of classical CFPR
and can be totally reduced to the classical CFPR. This study can be integrated
into our previous study about D-AHP (D numbers extended AHP) model to provide a
systematic solution for multi-criteria decision making (MCDM).
Two Timescale Convergent Q-learning for Sleep--Scheduling in Wireless Sensor Networks
Authors: Prashanth L. A., Abhranil Chatterjee, Shalabh Bhatnagar
Date: 2013-12-27 00:00:00
Tasks: feature selection, Intrusion Detection, Q-Learning, Scheduling

In this paper, we consider an intrusion detection application for Wireless
Sensor Networks (WSNs). We study the problem of scheduling the sleep times of
the individual sensors to maximize the network lifetime while keeping the
tracking error to a minimum. We formulate this problem as a
partially-observable Markov decision process (POMDP) with continuous
state-action spaces, in a manner similar to (Fuemmeler and Veeravalli [2008]).
However, unlike their formulation, we consider infinite horizon discounted and
average cost objectives as performance criteria. For each criterion, we propose
a convergent on-policy Q-learning algorithm that operates on two timescales,
while employing function approximation to handle the curse of dimensionality
associated with the underlying POMDP. Our proposed algorithm incorporates a
policy gradient update using a one-simulation simultaneous perturbation
stochastic approximation (SPSA) estimate on the faster timescale, while the
Q-value parameter (arising from a linear function approximation for the
Q-values) is updated in an on-policy temporal difference (TD) algorithm-like
fashion on the slower timescale. The feature selection scheme employed in each
of our algorithms manages the energy and tracking components in a manner that
assists the search for the optimal sleep-scheduling policy. For the sake of
comparison, in both discounted and average settings, we also develop a function
approximation analogue of the Q-learning algorithm. This algorithm, unlike the
two-timescale variant, does not possess theoretical convergence guarantees.
Finally, we also adapt our algorithms to include a stochastic iterative
estimation scheme for the intruder's mobility model. Our simulation results on
a 2-dimensional network setting suggest that our algorithms result in better
tracking accuracy at the cost of only a few additional sensors, in comparison
to a recent prior work.
SmartAnnotator: An Interactive Tool for Annotating RGBD Indoor Images
Authors: Yu-Shiang Wong, Hung-Kuo Chu, Niloy J. Mitra
Date: 2014-03-23 00:00:00
Tasks: Image Manipulation

RGBD images with high quality annotations in the form of geometric (i.e.,
segmentation) and structural (i.e., how do the segments are mutually related in
3D) information provide valuable priors to a large number of scene and image
manipulation applications. While it is now simple to acquire RGBD images,
annotating them, automatically or manually, remains challenging especially in
cluttered noisy environments. We present SmartAnnotator, an interactive system
to facilitate annotating RGBD images. The system performs the tedious tasks of
grouping pixels, creating potential abstracted cuboids, inferring object
interactions in 3D, and comes up with various hypotheses. The user simply has
to flip through a list of suggestions for segment labels, finalize a selection,
and the system updates the remaining hypotheses. As objects are finalized, the
process speeds up with fewer ambiguities to resolve. Further, as more scenes
are annotated, the system makes better suggestions based on structural and
geometric priors learns from the previous annotation sessions. We test our
system on a large number of database scenes and report significant improvements
over naive low-level annotation tools.
Cortex simulation system proposal using distributed computer network environments
Authors: Boris Tomas
Date: 2014-03-22 00:00:00

In the dawn of computer science and the eve of neuroscience we participate in
rebirth of neuroscience due to new technology that allows us to deeply and
precisely explore whole new world that dwells in our brains.
Neuronal Synchrony in Complex-Valued Deep Networks
Authors: David P. Reichert, Thomas Serre
Date: 2013-12-20 00:00:00

Deep learning has recently led to great successes in tasks such as image
recognition (e.g Krizhevsky et al., 2012). However, deep networks are still
outmatched by the power and versatility of the brain, perhaps in part due to
the richer neuronal computations available to cortical circuits. The challenge
is to identify which neuronal mechanisms are relevant, and to find suitable
abstractions to model them. Here, we show how aspects of spike timing, long
hypothesized to play a crucial role in cortical information processing, could
be incorporated into deep networks to build richer, versatile representations.
  We introduce a neural network formulation based on complex-valued neuronal
units that is not only biologically meaningful but also amenable to a variety
of deep learning frameworks. Here, units are attributed both a firing rate and
a phase, the latter indicating properties of spike timing. We show how this
formulation qualitatively captures several aspects thought to be related to
neuronal synchrony, including gating of information processing and dynamic
binding of distributed object representations. Focusing on the latter, we
demonstrate the potential of the approach in several simple experiments. Thus,
neuronal synchrony could be a flexible mechanism that fulfills multiple
functional roles in deep networks.
Firefly Monte Carlo: Exact MCMC with Subsets of Data
Authors: Dougal Maclaurin, Ryan P. Adams
Date: 2014-03-22 00:00:00
Tasks: Bayesian Inference

Markov chain Monte Carlo (MCMC) is a popular and successful general-purpose
tool for Bayesian inference. However, MCMC cannot be practically applied to
large data sets because of the prohibitive cost of evaluating every likelihood
term at every iteration. Here we present Firefly Monte Carlo (FlyMC) an
auxiliary variable MCMC algorithm that only queries the likelihoods of a
potentially small subset of the data at each iteration yet simulates from the
exact posterior distribution, in contrast to recent proposals that are
approximate even in the asymptotic limit. FlyMC is compatible with a wide
variety of modern MCMC algorithms, and only requires a lower bound on the
per-datum likelihood factors. In experiments, we find that FlyMC generates
samples from the posterior more than an order of magnitude faster than regular
MCMC, opening up MCMC methods to larger datasets than were previously
considered feasible.
Tractable Epistemic Reasoning with Functional Fluents, Static Causal Laws and Postdiction
Authors: Manfred Eppe
Date: 2014-03-01 00:00:00
Tasks: Epistemic Reasoning

We present an epistemic action theory for tractable epistemic reasoning as an extension to the h-approximation (HPX) theory. In contrast to existing tractable approaches, the theory supports functional fluents and postdictive reasoning with static causal laws. We argue that this combination is particularly synergistic because it allows one not only to perform direct postdiction about the conditions of actions, but also indirect postdiction about the conditions of static causal laws. We show that despite the richer expressiveness, the temporal projection problem remains tractable (polynomial), and therefore the planning problem remains in NP. We present the operational semantics of our theory as well as its formulation as Answer Set Programming.
k-Sparse Autoencoders
Authors: Alireza Makhzani, Brendan Frey
Date: 2013-12-19 00:00:00
Tasks: Classification, Denoising, General Classification

Recently, it has been observed that when representations are learnt in a way
that encourages sparsity, improved performance is obtained on classification
tasks. These methods involve combinations of activation functions, sampling
steps and different kinds of penalties. To investigate the effectiveness of
sparsity by itself, we propose the k-sparse autoencoder, which is an
autoencoder with linear activation function, where in hidden layers only the k
highest activities are kept. When applied to the MNIST and NORB datasets, we
find that this method achieves better classification results than denoising
autoencoders, networks trained with dropout, and RBMs. k-sparse autoencoders
are simple to train and the encoding stage is very fast, making them
well-suited to large problem sizes, where conventional sparse coding algorithms
cannot be applied.
CUR Algorithm with Incomplete Matrix Observation
Authors: Rong Jin, Shenghuo Zhu
Date: 2014-03-22 00:00:00
Tasks: Matrix Completion

CUR matrix decomposition is a randomized algorithm that can efficiently
compute the low rank approximation for a given rectangle matrix. One limitation
with the existing CUR algorithms is that they require an access to the full
matrix A for computing U. In this work, we aim to alleviate this limitation. In
particular, we assume that besides having an access to randomly sampled d rows
and d columns from A, we only observe a subset of randomly sampled entries from
A. Our goal is to develop a low rank approximation algorithm, similar to CUR,
based on (i) randomly sampled rows and columns from A, and (ii) randomly
sampled entries from A. The proposed algorithm is able to perfectly recover the
target matrix A with only O(rn log n) number of observed entries. In addition,
instead of having to solve an optimization problem involved trace norm
regularization, the proposed algorithm only needs to solve a standard
regression problem. Finally, unlike most matrix completion theories that hold
only when the target matrix is of low rank, we show a strong guarantee for the
proposed algorithm even when the target matrix is not low rank.
Bayesian Optimization with Unknown Constraints
Authors: Michael A. Gelbart, Jasper Snoek, Ryan P. Adams
Date: 2014-03-22 00:00:00
Tasks: Bayesian Optimization, global-optimization

Recent work on Bayesian optimization has shown its effectiveness in global
optimization of difficult black-box objective functions. Many real-world
optimization problems of interest also have constraints which are unknown a
priori. In this paper, we study Bayesian optimization for constrained problems
in the general case that noise may be present in the constraint functions, and
the objective and constraints may be evaluated independently. We provide
motivating practical examples, and present a general framework to solve such
problems. We demonstrate the effectiveness of our approach on optimizing the
performance of online latent Dirichlet allocation subject to topic sparsity
constraints, tuning a neural network given test-time memory constraints, and
optimizing Hamiltonian Monte Carlo to achieve maximal effectiveness in a fixed
time, subject to passing standard convergence diagnostics.
Forecasting Popularity of Videos using Social Media
Authors: Jie Xu, Mihaela van der Schaar, Jiangchuan Liu, Haitao Li
Date: 2014-03-22 00:00:00
Tasks: Prediction

This paper presents a systematic online prediction method (Social-Forecast)
that is capable to accurately forecast the popularity of videos promoted by
social media. Social-Forecast explicitly considers the dynamically changing and
evolving propagation patterns of videos in social media when making popularity
forecasts, thereby being situation and context aware. Social-Forecast aims to
maximize the forecast reward, which is defined as a tradeoff between the
popularity prediction accuracy and the timeliness with which a prediction is
issued. The forecasting is performed online and requires no training phase or a
priori knowledge. We analytically bound the prediction performance loss of
Social-Forecast as compared to that obtained by an omniscient oracle and prove
that the bound is sublinear in the number of video arrivals, thereby
guaranteeing its short-term performance as well as its asymptotic convergence
to the optimal performance. In addition, we conduct extensive experiments using
real-world data traces collected from the videos shared in RenRen, one of the
largest online social networks in China. These experiments show that our
proposed method outperforms existing view-based approaches for popularity
prediction (which are not context-aware) by more than 30% in terms of
prediction rewards.
A Lemma Based Evaluator for Semitic Language Text Summarization Systems
Authors: Tarek El-Shishtawy, Fatma El-Ghannam
Date: 2014-03-22 00:00:00
Tasks: LEMMA, Text Matching, Text Summarization

Matching texts in highly inflected languages such as Arabic by simple
stemming strategy is unlikely to perform well. In this paper, we present a
strategy for automatic text matching technique for for inflectional languages,
using Arabic as the test case. The system is an extension of ROUGE test in
which texts are matched on token's lemma level. The experimental results show
an enhancement of detecting similarities between different sentences having
same semantics but written in different lexical forms..
Zero-Shot Learning by Convex Combination of Semantic Embeddings
Authors: Mohammad Norouzi, Tomas Mikolov, Samy Bengio, Yoram Singer, Jonathon Shlens, Andrea Frome, Greg S. Corrado, Jeffrey Dean
Date: 2013-12-19 00:00:00
Tasks: Multi-label zero-shot learning, Zero-Shot Learning

Several recent publications have proposed methods for mapping images into
continuous semantic embedding spaces. In some cases the embedding space is
trained jointly with the image transformation. In other cases the semantic
embedding space is established by an independent natural language processing
task, and then the image transformation into that space is learned in a second
stage. Proponents of these image embedding systems have stressed their
advantages over the traditional \nway{} classification framing of image
understanding, particularly in terms of the promise for zero-shot learning --
the ability to correctly annotate images of previously unseen object
categories. In this paper, we propose a simple method for constructing an image
embedding system from any existing \nway{} image classifier and a semantic word
embedding model, which contains the $\n$ class labels in its vocabulary. Our
method maps images into the semantic embedding space via convex combination of
the class label embedding vectors, and requires no additional training. We show
that this simple and direct method confers many of the advantages associated
with more complex image embedding schemes, and indeed outperforms state of the
art methods on the ImageNet zero-shot learning task.
Continuous Optimization for Fields of Experts Denoising Works
Authors: Petter Strandmark, Sameer Agarwal
Date: 2014-03-21 00:00:00
Tasks: Denoising, Image Denoising

Several recent papers use image denoising with a Fields of Experts prior to
benchmark discrete optimization methods. We show that a non-linear least
squares solver significantly outperforms all known discrete methods on this
problem.