pwc-archive/papers-with-abstracts huggingface dataset:
Learning to Hash for Indexing Big Data - A Survey
Authors: Jun Wang, Wei Liu, Sanjiv Kumar, Shih-Fu Chang
Date: 2015-09-17 00:00:00
Tasks: Survey

The explosive growth in big data has attracted much attention in designing
efficient indexing and search methods recently. In many critical applications
such as large-scale search and pattern matching, finding the nearest neighbors
to a query is a fundamental research problem. However, the straightforward
solution using exhaustive comparison is infeasible due to the prohibitive
computational complexity and memory requirement. In response, Approximate
Nearest Neighbor (ANN) search based on hashing techniques has become popular
due to its promising performance in both efficiency and accuracy. Prior
randomized hashing methods, e.g., Locality-Sensitive Hashing (LSH), explore
data-independent hash functions with random projections or permutations.
Although having elegant theoretic guarantees on the search quality in certain
metric spaces, performance of randomized hashing has been shown insufficient in
many real-world applications. As a remedy, new approaches incorporating
data-driven learning methods in development of advanced hash functions have
emerged. Such learning to hash methods exploit information such as data
distributions or class labels when optimizing the hash codes or functions.
Importantly, the learned hash codes are able to preserve the proximity of
neighboring data in the original feature spaces in the hash code spaces. The
goal of this paper is to provide readers with systematic understanding of
insights, pros and cons of the emerging techniques. We provide a comprehensive
survey of the learning to hash framework and representative techniques of
various types, including unsupervised, semi-supervised, and supervised. In
addition, we also summarize recent hashing approaches utilizing the deep
learning models. Finally, we discuss the future direction and trends of
research in this area.
Sparse Fisher's Linear Discriminant Analysis for Partially Labeled Data
Authors: Qiyi Lu, Xingye Qiao
Date: 2015-09-17 00:00:00
Tasks: Classification, General Classification

Classification is an important tool with many useful applications. Among the
many classification methods, Fisher's Linear Discriminant Analysis (LDA) is a
traditional model-based approach which makes use of the covariance information.
However, in the high-dimensional, low-sample size setting, LDA cannot be
directly deployed because the sample covariance is not invertible. While there
are modern methods designed to deal with high-dimensional data, they may not
fully use the covariance information as LDA does. Hence in some situations, it
is still desirable to use a model-based method such as LDA for classification.
This article exploits the potential of LDA in more complicated data settings.
In many real applications, it is costly to manually place labels on
observations; hence it is often that only a small portion of labeled data is
available while a large number of observations are left without a label. It is
a great challenge to obtain good classification performance through the labeled
data alone, especially when the dimension is greater than the size of the
labeled data. In order to overcome this issue, we propose a semi-supervised
sparse LDA classifier to take advantage of the seemingly useless unlabeled
data. They provide additional information which helps to boost the
classification performance in some situations. A direct estimation method is
used to reconstruct LDA and achieve the sparsity; meanwhile we employ the
difference-convex algorithm to handle the non-convex loss function associated
with the unlabeled data. Theoretical properties of the proposed classifier are
studied. Our simulated examples help to understand when and how the information
extracted from the unlabeled data can be useful. A real data example further
illustrates the usefulness of the proposed method.
Class Association Rules Mining based Rough Set Method
Authors: Thabet Slimani
Date: 2015-09-17 00:00:00

This paper investigates the mining of class association rules with rough set
approach. In data mining, an association occurs between two set of elements
when one element set happen together with another. A class association rule set
(CARs) is a subset of association rules with classes specified as their
consequences. We present an efficient algorithm for mining the finest class
rule set inspired form Apriori algorithm, where the support and confidence are
computed based on the elementary set of lower approximation included in the
property of rough set theory. Our proposed approach has been shown very
effective, where the rough set approach for class association discovery is much
simpler than the classic association method.
A Study Investigating Typical Concepts and Guidelines for Ontology Building
Authors: Thabet Slimani
Date: 2015-09-17 00:00:00

In semantic technologies, the shared common understanding of the structure of
information among artifacts (people or software agents) can be realized by
building an ontology. To do this, it is imperative for an ontology builder to
answer several questions: a) what are the main components of an ontology? b)
How an ontology look likes and how it works? c) Verify if it is required to
consider reusing existing ontologies or not? c) What is the complexity of the
ontology to be developed? d) What are the principles of ontology design and
development? e) How to evaluate an ontology? This paper answers all the key
questions above. The aim of this paper is to present a set of guiding
principles to help ontology developers and also inexperienced users to answer
such questions.
Facial Descriptors for Human Interaction Recognition In Still Images
Authors: Gokhan Tanisik, Cemil Zalluhoglu, Nazli Ikizler-Cinbis
Date: 2015-09-17 00:00:00
Tasks: Human Interaction Recognition

This paper presents a novel approach in a rarely studied area of computer
vision: Human interaction recognition in still images. We explore whether the
facial regions and their spatial configurations contribute to the recognition
of interactions. In this respect, our method involves extraction of several
visual features from the facial regions, as well as incorporation of scene
characteristics and deep features to the recognition. Extracted multiple
features are utilized within a discriminative learning framework for
recognizing interactions between people. Our designed facial descriptors are
based on the observation that relative positions, size and locations of the
faces are likely to be important for characterizing human interactions. Since
there is no available dataset in this relatively new domain, a comprehensive
new dataset which includes several images of human interactions is collected.
Our experimental results show that faces and scene characteristics contain
important information to recognize interactions between people.
Recurrent Spatial Transformer Networks
Authors: Søren Kaae Sønderby, Casper Kaae Sønderby, Lars Maaløe, Ole Winther
Date: 2015-09-17 00:00:00
Tasks: Attribute

We integrate the recently proposed spatial transformer network (SPN)
[Jaderberg et. al 2015] into a recurrent neural network (RNN) to form an
RNN-SPN model. We use the RNN-SPN to classify digits in cluttered MNIST
sequences. The proposed model achieves a single digit error of 1.5% compared to
2.9% for a convolutional networks and 2.0% for convolutional networks with SPN
layers. The SPN outputs a zoomed, rotated and skewed version of the input
image. We investigate different down-sampling factors (ratio of pixel in input
and output) for the SPN and show that the RNN-SPN model is able to down-sample
the input images without deteriorating performance. The down-sampling in
RNN-SPN can be thought of as adaptive down-sampling that minimizes the
information loss in the regions of interest. We attribute the superior
performance of the RNN-SPN to the fact that it can attend to a sequence of
regions of interest.
A Simulated Annealing Approach to Bayesian Inference
Authors: Carlo Albert
Date: 2015-09-17 00:00:00
Tasks: Bayesian Inference

A generic algorithm for the extraction of probabilistic (Bayesian)
information about model parameters from data is presented. The algorithm
propagates an ensemble of particles in the product space of model parameters
and outputs. Each particle update consists of a random jump in parameter space
followed by a simulation of a model output and a Metropolis
acceptance/rejection step based on a comparison of the simulated output to the
data. The distance of a particle to the data is interpreted as an energy and
the algorithm is reducing the associated temperature of the ensemble such that
entropy production is minimized. If this simulated annealing is not too fast
compared to the mixing speed in parameter space, the parameter marginal of the
ensemble approaches the Bayesian posterior distribution. Annealing is adaptive
and depends on certain extensive thermodynamic quantities that can easily be
measured throughout run-time. In the general case, we propose annealing with a
constant entropy production rate, which is optimal as long as annealing is not
too fast. For the practically relevant special case of no prior knowledge, we
derive an optimal fast annealing schedule with a non-constant entropy
production rate. The algorithm does not require the calculation of the density
of the model likelihood, which makes it interesting for Bayesian parameter
inference with stochastic models, whose likelihood functions are typically very
high dimensional integrals.
Humans Are Easily Fooled by Digital Images
Authors: Victor Schetinger, Manuel M. Oliveira, Roberto da Silva, Tiago J. Carvalho
Date: 2015-09-17 00:00:00

Digital images are ubiquitous in our modern lives, with uses ranging from
social media to news, and even scientific papers. For this reason, it is
crucial evaluate how accurate people are when performing the task of identify
doctored images. In this paper, we performed an extensive user study evaluating
subjects capacity to detect fake images. After observing an image, users have
been asked if it had been altered or not. If the user answered the image has
been altered, he had to provide evidence in the form of a click on the image.
We collected 17,208 individual answers from 383 users, using 177 images
selected from public forensic databases. Different from other previously
studies, our method propose different ways to avoid lucky guess when evaluating
users answers. Our results indicate that people show inaccurate skills at
differentiating between altered and non-altered images, with an accuracy of
58%, and only identifying the modified images 46.5% of the time. We also track
user features such as age, answering time, confidence, providing deep analysis
of how such variables influence on the users' performance.
Decadal climate predictions using sequential learning algorithms
Authors: Ehud Strobach, Golan Bel
Date: 2015-09-17 00:00:00

Ensembles of climate models are commonly used to improve climate predictions
and assess the uncertainties associated with them. Weighting the models
according to their performances holds the promise of further improving their
predictions. Here, we use an ensemble of decadal climate predictions to
demonstrate the ability of sequential learning algorithms (SLAs) to reduce the
forecast errors and reduce the uncertainties. Three different SLAs are
considered, and their performances are compared with those of an equally
weighted ensemble, a linear regression and the climatology. Predictions of four
different variables--the surface temperature, the zonal and meridional wind,
and pressure--are considered. The spatial distributions of the performances are
presented, and the statistical significance of the improvements achieved by the
SLAs is tested. Based on the performances of the SLAs, we propose one to be
highly suitable for the improvement of decadal climate predictions.
Deep Multi-task Learning for Railway Track Inspection
Authors: Xavier Gibert, Vishal M. Patel, Rama Chellappa
Date: 2015-09-17 00:00:00
Tasks: Multi-Task Learning

Railroad tracks need to be periodically inspected and monitored to ensure
safe transportation. Automated track inspection using computer vision and
pattern recognition methods have recently shown the potential to improve safety
by allowing for more frequent inspections while reducing human errors.
Achieving full automation is still very challenging due to the number of
different possible failure modes as well as the broad range of image variations
that can potentially trigger false alarms. Also, the number of defective
components is very small, so not many training examples are available for the
machine to learn a robust anomaly detector. In this paper, we show that
detection performance can be improved by combining multiple detectors within a
multi-task learning framework. We show that this approach results in better
accuracy in detecting defects on railway ties and fasteners.
(Blue) Taxi Destination and Trip Time Prediction from Partial Trajectories
Authors: Hoang Thanh Lam, Ernesto Diaz-Aviles, Alessandra Pascale, Yiannis Gkoufas, Bei Chen
Date: 2015-09-17 00:00:00
Tasks: Ensemble Learning

Real-time estimation of destination and travel time for taxis is of great
importance for existing electronic dispatch systems. We present an approach
based on trip matching and ensemble learning, in which we leverage the patterns
observed in a dataset of roughly 1.7 million taxi journeys to predict the
corresponding final destination and travel time for ongoing taxi trips, as a
solution for the ECML/PKDD Discovery Challenge 2015 competition. The results of
our empirical evaluation show that our approach is effective and very robust,
which led our team -- BlueTaxi -- to the 3rd and 7th position of the final
rankings for the trip time and destination prediction tasks, respectively.
Given the fact that the final rankings were computed using a very small test
set (with only 320 trips) we believe that our approach is one of the most
robust solutions for the challenge based on the consistency of our good results
across the test sets.
Extraction of evidence tables from abstracts of randomized clinical trials using a maximum entropy classifier and global constraints
Authors: Antonio Trenta, Anthony Hunter, Sebastian Riedel
Date: 2015-09-17 00:00:00

Systematic use of the published results of randomized clinical trials is
increasingly important in evidence-based medicine. In order to collate and
analyze the results from potentially numerous trials, evidence tables are used
to represent trials concerning a set of interventions of interest. An evidence
table has columns for the patient group, for each of the interventions being
compared, for the criterion for the comparison (e.g. proportion who survived
after 5 years from treatment), and for each of the results. Currently, it is a
labour-intensive activity to read each published paper and extract the
information for each field in an evidence table. There have been some NLP
studies investigating how some of the features from papers can be extracted, or
at least the relevant sentences identified. However, there is a lack of an NLP
system for the systematic extraction of each item of information required for
an evidence table. We address this need by a combination of a maximum entropy
classifier, and integer linear programming. We use the later to handle
constraints on what is an acceptable classification of the features to be
extracted. With experimental results, we demonstrate substantial advantages in
using global constraints (such as the features describing the patient group,
and the interventions, must occur before the features describing the results of
the comparison).
Improved Residual Vector Quantization for High-dimensional Approximate Nearest Neighbor Search
Authors: Shicong Liu, Hongtao Lu, Junru Shao
Date: 2015-09-17 00:00:00
Tasks: Clustering, Quantization, Vocal Bursts Intensity Prediction

Quantization methods have been introduced to perform large scale approximate
nearest search tasks. Residual Vector Quantization (RVQ) is one of the
effective quantization methods. RVQ uses a multi-stage codebook learning scheme
to lower the quantization error stage by stage. However, there are two major
limitations for RVQ when applied to on high-dimensional approximate nearest
neighbor search: 1. The performance gain diminishes quickly with added stages.
2. Encoding a vector with RVQ is actually NP-hard. In this paper, we propose an
improved residual vector quantization (IRVQ) method, our IRVQ learns codebook
with a hybrid method of subspace clustering and warm-started k-means on each
stage to prevent performance gain from dropping, and uses a multi-path encoding
scheme to encode a vector with lower distortion. Experimental results on the
benchmark datasets show that our method gives substantially improves RVQ and
delivers better performance compared to the state-of-the-art.
HCLAE: High Capacity Locally Aggregating Encodings for Approximate Nearest Neighbor Search
Authors: Shicong Liu, Junru Shao, Hongtao Lu
Date: 2015-09-17 00:00:00
Tasks: Quantization, Vocal Bursts Intensity Prediction

Vector quantization-based approaches are successful to solve Approximate
Nearest Neighbor (ANN) problems which are critical to many applications. The
idea is to generate effective encodings to allow fast distance approximation.
We propose quantization-based methods should partition the data space finely
and exhibit locality of the dataset to allow efficient non-exhaustive search.
In this paper, we introduce the concept of High Capacity Locality Aggregating
Encodings (HCLAE) to this end, and propose Dictionary Annealing (DA) to learn
HCLAE by a simulated annealing procedure. The quantization error is lower than
other state-of-the-art. The algorithms of DA can be easily extended to an
online learning scheme, allowing effective handle of large scale data. Further,
we propose Aggregating-Tree (A-Tree), a non-exhaustive search method using
HCLAE to perform efficient ANN-Search. A-Tree achieves magnitudes of speed-up
on ANN-Search tasks, compared to the state-of-the-art.
Efficient Task Collaboration with Execution Uncertainty
Authors: Dengji Zhao, Sarvapali D. Ramchurn, Nicholas R. Jennings
Date: 2015-09-17 00:00:00

We study a general task allocation problem, involving multiple agents that
collaboratively accomplish tasks and where agents may fail to successfully
complete the tasks assigned to them (known as execution uncertainty). The goal
is to choose an allocation that maximises social welfare while taking their
execution uncertainty into account. We show that this can be achieved by using
the post-execution verification (PEV)-based mechanism if and only if agents'
valuations satisfy a multilinearity condition. We then consider a more complex
setting where an agent's execution uncertainty is not completely predictable by
the agent alone but aggregated from all agents' private opinions (known as
trust). We show that PEV-based mechanism with trust is still truthfully
implementable if and only if the trust aggregation is multilinear.
Taming the ReLU with Parallel Dither in a Deep Neural Network
Authors: Andrew J. R. Simpson
Date: 2015-09-17 00:00:00

Rectified Linear Units (ReLU) seem to have displaced traditional 'smooth'
nonlinearities as activation-function-du-jour in many - but not all - deep
neural network (DNN) applications. However, nobody seems to know why. In this
article, we argue that ReLU are useful because they are ideal demodulators -
this helps them perform fast abstract learning. However, this fast learning
comes at the expense of serious nonlinear distortion products - decoy features.
We show that Parallel Dither acts to suppress the decoy features, preventing
overfitting and leaving the true features cleanly demodulated for rapid,
reliable learning.
Fast Sequence Component Analysis for Attack Detection in Synchrophasor Networks
Authors: Jordan Landford, Rich Meier, Richard Barella, Xinghui Zhao, Eduardo Cotilla-Sanchez, Robert B. Bass, Scott Wallace
Date: 2015-09-17 00:00:00

Modern power systems have begun integrating synchrophasor technologies into
part of daily operations. Given the amount of solutions offered and the
maturity rate of application development it is not a matter of "if" but a
matter of "when" in regards to these technologies becoming ubiquitous in
control centers around the world. While the benefits are numerous, the
functionality of operator-level applications can easily be nullified by
injection of deceptive data signals disguised as genuine measurements. Such
deceptive action is a common precursor to nefarious, often malicious activity.
A correlation coefficient characterization and machine learning methodology are
proposed to detect and identify injection of spoofed data signals. The proposed
method utilizes statistical relationships intrinsic to power system parameters,
which are quantified and presented. Several spoofing schemes have been
developed to qualitatively and quantitatively demonstrate detection
capabilities.
Exact simultaneous recovery of locations and structure from known orientations and corrupted point correspondences
Authors: Paul Hand, Choongbum Lee, Vladislav Voroninski
Date: 2015-09-16 00:00:00
Tasks: Translation

Let $t_1,\ldots,t_{n_l} \in \mathbb{R}^d$ and $p_1,\ldots,p_{n_s} \in
\mathbb{R}^d$ and consider the bipartite location recovery problem: given a
subset of pairwise direction observations $\{(t_i - p_j) / \|t_i -
p_j\|_2\}_{i,j \in [n_l] \times [n_s]}$, where a constant fraction of these
observations are arbitrarily corrupted, find $\{t_i\}_{i \in [n_ll]}$ and
$\{p_j\}_{j \in [n_s]}$ up to a global translation and scale. We study the
recently introduced ShapeFit algorithm as a method for solving this bipartite
location recovery problem. In this case, ShapeFit consists of a simple convex
program over $d(n_l + n_s)$ real variables. We prove that this program recovers
a set of $n_l+n_s$ i.i.d. Gaussian locations exactly and with high probability
if the observations are given by a bipartite Erd\H{o}s-R\'{e}nyi graph, $d$ is
large enough, and provided that at most a constant fraction of observations
involving any particular location are adversarially corrupted. This recovery
theorem is based on a set of deterministic conditions that we prove are
sufficient for exact recovery. Finally, we propose a modified pipeline for the
Structure for Motion problem, based on this bipartite location recovery
problem.
Overcomplete Dictionary Learning with Jacobi Atom Updates
Authors: Paul Irofti, Bogdan Dumitrescu
Date: 2015-09-16 00:00:00
Tasks: Dictionary Learning

Dictionary learning for sparse representations is traditionally approached
with sequential atom updates, in which an optimized atom is used immediately
for the optimization of the next atoms. We propose instead a Jacobi version, in
which groups of atoms are updated independently, in parallel. Extensive
numerical evidence for sparse image representation shows that the parallel
algorithms, especially when all atoms are updated simultaneously, give better
dictionaries than their sequential counterparts.