pwc-archive/papers-with-abstracts huggingface dataset:
The Radon cumulative distribution transform and its application to image classification
Authors: Soheil Kolouri, Se Rim Park, Gustavo K. Rohde
Date: 2015-11-10 00:00:00
Tasks: General Classification, image-classification, Image Classification

Invertible image representation methods (transforms) are routinely employed
as low-level image processing operations based on which feature extraction and
recognition algorithms are developed. Most transforms in current use (e.g.
Fourier, Wavelet, etc.) are linear transforms, and, by themselves, are unable
to substantially simplify the representation of image classes for
classification. Here we describe a nonlinear, invertible, low-level image
processing transform based on combining the well known Radon transform for
image data, and the 1D Cumulative Distribution Transform proposed earlier. We
describe a few of the properties of this new transform, and with both
theoretical and experimental results show that it can often render certain
problems linearly separable in transform space.
Sliced Wasserstein Kernels for Probability Distributions
Authors: Soheil Kolouri, Yang Zou, Gustavo K. Rohde
Date: 2015-11-10 00:00:00
Proceeding: CVPR 2016 6
Tasks: BIG-bench Machine Learning

Optimal transport distances, otherwise known as Wasserstein distances, have
recently drawn ample attention in computer vision and machine learning as a
powerful discrepancy measure for probability distributions. The recent
developments on alternative formulations of the optimal transport have allowed
for faster solutions to the problem and has revamped its practical applications
in machine learning. In this paper, we exploit the widely used kernel methods
and provide a family of provably positive definite kernels based on the Sliced
Wasserstein distance and demonstrate the benefits of these kernels in a variety
of learning tasks. Our work provides a new perspective on the application of
optimal transport flavored distances through kernel methods in machine learning
tasks.
Fame for sale: efficient detection of fake Twitter followers
Authors: Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, Maurizio Tesconi
Date: 2015-09-14 00:00:00
Tasks: Spam detection

$\textit{Fake followers}$ are those Twitter accounts specifically created to
inflate the number of followers of a target account. Fake followers are
dangerous for the social platform and beyond, since they may alter concepts
like popularity and influence in the Twittersphere - hence impacting on
economy, politics, and society. In this paper, we contribute along different
dimensions. First, we review some of the most relevant existing features and
rules (proposed by Academia and Media) for anomalous Twitter accounts
detection. Second, we create a baseline dataset of verified human and fake
follower accounts. Such baseline dataset is publicly available to the
scientific community. Then, we exploit the baseline dataset to train a set of
machine-learning classifiers built over the reviewed rules and features. Our
results show that most of the rules proposed by Media provide unsatisfactory
performance in revealing fake followers, while features proposed in the past by
Academia for spam detection provide good results. Building on the most
promising features, we revise the classifiers both in terms of reduction of
overfitting and cost for gathering the data needed to compute the features. The
final result is a novel $\textit{Class A}$ classifier, general enough to thwart
overfitting, lightweight thanks to the usage of the less costly features, and
still able to correctly classify more than 95% of the accounts of the original
training set. We ultimately perform an information fusion-based sensitivity
analysis, to assess the global sensitivity of each of the features employed by
the classifier. The findings reported in this paper, other than being supported
by a thorough experimental methodology and interesting on their own, also pave
the way for further investigation on the novel issue of fake Twitter followers.
Dynamic Belief Fusion for Object Detection
Authors: Hyungtae Lee, Heesung Kwon, Ryan M. Robinson, William D. Nothwang, Amar M. Marathe
Date: 2015-11-10 00:00:00
Tasks: Object, object-detection, Object Detection

A novel approach for the fusion of heterogeneous object detection methods is
proposed. In order to effectively integrate the outputs of multiple detectors,
the level of ambiguity in each individual detection score is estimated using
the precision/recall relationship of the corresponding detector. The main
contribution of the proposed work is a novel fusion method, called Dynamic
Belief Fusion (DBF), which dynamically assigns probabilities to hypotheses
(target, non-target, intermediate state (target or non-target)) based on
confidence levels in the detection results conditioned on the prior performance
of individual detectors. In DBF, a joint basic probability assignment,
optimally fusing information from all detectors, is determined by the
Dempster's combination rule, and is easily reduced to a single fused detection
score. Experiments on ARL and PASCAL VOC 07 datasets demonstrate that the
detection accuracy of DBF is considerably greater than conventional fusion
approaches as well as individual detectors used for the fusion.
Kernel Methods for Accurate UWB-Based Ranging with Reduced Complexity
Authors: Vladimir Savic, Erik G. Larsson, Javier Ferrer-Coll, Peter Stenumgaard
Date: 2015-11-10 00:00:00
Tasks: GPR

Accurate and robust positioning in multipath environments can enable many
applications, such as search-and-rescue and asset tracking. For this problem,
ultra-wideband (UWB) technology can provide the most accurate range estimates,
which are required for range-based positioning. However, UWB still faces a
problem with non-line-of-sight (NLOS) measurements, in which the range
estimates based on time-of-arrival (TOA) will typically be positively biased.
There are many techniques that address this problem, mainly based on NLOS
identification and NLOS error mitigation algorithms. However, these techniques
do not exploit all available information in the UWB channel impulse response.
Kernel-based machine learning methods, such as Gaussian Process Regression
(GPR), are able to make use of all information, but they may be too complex in
their original form. In this paper, we propose novel ranging methods based on
kernel principal component analysis (kPCA), in which the selected channel
parameters are projected onto a nonlinear orthogonal high-dimensional space,
and a subset of these projections is then used as an input for ranging. We
evaluate the proposed methods using real UWB measurements obtained in a
basement tunnel, and found that one of the proposed methods is able to
outperform state-of-the-art, even if little training samples are available.
USFD: Twitter NER with Drift Compensation and Linked Data
Authors: Leon Derczynski, Isabelle Augenstein, Kalina Bontcheva
Date: 2015-11-10 00:00:00
Proceeding: WS 2015 7
Tasks: Clustering, NER

This paper describes a pilot NER system for Twitter, comprising the USFD
system entry to the W-NUT 2015 NER shared task. The goal is to correctly label
entities in a tweet dataset, using an inventory of ten types. We employ
structured learning, drawing on gazetteers taken from Linked Data, and on
unsupervised clustering features, and attempting to compensate for stylistic
and topic drift - a key challenge in social media text. Our result is
competitive; we provide an analysis of the components of our methodology, and
an examination of the target dataset in the context of this task.
The CTU Prague Relational Learning Repository
Authors: Jan Motl, Oliver Schulte
Date: 2015-11-10 00:00:00
Tasks: BIG-bench Machine Learning, Relational Reasoning

The aim of the Prague Relational Learning Repository is to support machine learning research with multi-relational data. The repository currently contains 148 SQL databases hosted on a public MySQL server located at https://relational.fel.cvut.cz. The server is provided by the Czech Technical University (CTU). A searchable meta-database provides metadata (e.g., the number of tables in the database, the number of rows and columns in the tables, the number of self-relationships).
Dynamics of Human Cooperation in Economic Games
Authors: Martin Spanknebel, Klaus Pawelzik
Date: 2015-08-21 00:00:00

Human decision behaviour is quite diverse. In many games humans on average do
not achieve maximal payoff and the behaviour of individual players remains
inhomogeneous even after playing many rounds. For instance, in repeated
prisoner dilemma games humans do not always optimize their mean reward and
frequently exhibit broad distributions of cooperativity. The reasons for these
failures of maximization are not known. Here we show that the dynamics
resulting from the tendency to shift choice probabilities towards previously
rewarding choices in closed loop interaction with the strategy of the opponent
can not only explain systematic deviations from 'rationality', but also
reproduce the diversity of choice behaviours. As a representative example we
investigate the dynamics of choice probabilities in prisoner dilemma games with
opponents using strategies with different degrees of extortion and generosity.
We find that already a simple model for human learning can account for a
surprisingly wide range of human decision behaviours. It reproduces suppression
of cooperation against extortionists and increasing cooperation when playing
with generous opponents, explains the broad distributions of individual choices
in ensembles of players, and predicts the evolution of individual subjects'
cooperation rates over the course of the games. We conclude that important
aspects of human decision behaviours are rooted in elementary learning
mechanisms realised in the brain.
Tiny Descriptors for Image Retrieval with Unsupervised Triplet Hashing
Authors: Jie Lin, Olivier Morère, Julie Petta, Vijay Chandrasekhar, Antoine Veillard
Date: 2015-11-10 00:00:00
Tasks: image-classification, Image Classification, Image Retrieval, Retrieval, Triplet

A typical image retrieval pipeline starts with the comparison of global
descriptors from a large database to find a short list of candidate matches. A
good image descriptor is key to the retrieval pipeline and should reconcile two
contradictory requirements: providing recall rates as high as possible and
being as compact as possible for fast matching. Following the recent successes
of Deep Convolutional Neural Networks (DCNN) for large scale image
classification, descriptors extracted from DCNNs are increasingly used in place
of the traditional hand crafted descriptors such as Fisher Vectors (FV) with
better retrieval performances. Nevertheless, the dimensionality of a typical
DCNN descriptor --extracted either from the visual feature pyramid or the
fully-connected layers-- remains quite high at several thousands of scalar
values. In this paper, we propose Unsupervised Triplet Hashing (UTH), a fully
unsupervised method to compute extremely compact binary hashes --in the 32-256
bits range-- from high-dimensional global descriptors. UTH consists of two
successive deep learning steps. First, Stacked Restricted Boltzmann Machines
(SRBM), a type of unsupervised deep neural nets, are used to learn binary
embedding functions able to bring the descriptor size down to the desired
bitrate. SRBMs are typically able to ensure a very high compression rate at the
expense of loosing some desirable metric properties of the original DCNN
descriptor space. Then, triplet networks, a rank learning scheme based on
weight sharing nets is used to fine-tune the binary embedding functions to
retain as much as possible of the useful metric properties of the original
space. A thorough empirical evaluation conducted on multiple publicly available
dataset using DCNN descriptors shows that our method is able to significantly
outperform state-of-the-art unsupervised schemes in the target bit range.
Investigating the stylistic relevance of adjective and verb simile markers
Authors: Suzanne Mpouli, Jean-Gabriel Ganascia
Date: 2015-11-10 00:00:00

Similes play an important role in literary texts not only as rhetorical
devices and as figures of speech but also because of their evocative power,
their aptness for description and the relative ease with which they can be
combined with other figures of speech (Israel et al. 2004). Detecting all types
of simile constructions in a particular text therefore seems crucial when
analysing the style of an author. Few research studies however have been
dedicated to the study of less prominent simile markers in fictional prose and
their relevance for stylistic studies. The present paper studies the frequency
of adjective and verb simile markers in a corpus of British and French novels
in order to determine which ones are really informative and worth including in
a stylistic analysis. Furthermore, are those adjectives and verb simile markers
used differently in both languages?
3D Time-lapse Reconstruction from Internet Photos
Authors: Ricardo Martin-Brualla, David Gallup, Steven M. Seitz
Date: 2015-11-10 00:00:00
Proceeding: ICCV 2015 12

Given an Internet photo collection of a landmark, we compute a 3D time-lapse video sequence where a virtual camera moves continuously in time and space. While previous work assumed a static camera, the addition of camera motion during the time-lapse creates a very compelling impression of parallax. Achieving this goal, however, requires addressing multiple technical challenges, including solving for time-varying depth maps, regularizing 3D point color profiles over time, and reconstructing high quality, hole-free images at every frame from the projected profiles. Our results show photorealistic time-lapses of skylines and natural scenes over many years, with dramatic parallax effects.
Deep Representation of Facial Geometric and Photometric Attributes for Automatic 3D Facial Expression Recognition
Authors: Huibin Li, Jian Sun, Dong Wang, Zongben Xu, Liming Chen
Date: 2015-11-10 00:00:00
Tasks: 3D Facial Expression Recognition, Facial Expression Recognition, Facial Expression Recognition (FER)

In this paper, we present a novel approach to automatic 3D Facial Expression
Recognition (FER) based on deep representation of facial 3D geometric and 2D
photometric attributes. A 3D face is firstly represented by its geometric and
photometric attributes, including the geometry map, normal maps, normalized
curvature map and texture map. These maps are then fed into a pre-trained deep
convolutional neural network to generate the deep representation. Then the
facial expression prediction is simplyachieved by training linear SVMs over the
deep representation for different maps and fusing these SVM scores. The
visualizations show that the deep representation provides a complete and highly
discriminative coding scheme for 3D faces. Comprehensive experiments on the
BU-3DFE database demonstrate that the proposed deep representation can
outperform the widely used hand-crafted descriptors (i.e., LBP, SIFT, HOG,
Gabor) and the state-of-art approaches under the same experimental protocols.
Information retrieval in folktales using natural language processing
Authors: Adrian Groza, Lidia Corde
Date: 2015-11-10 00:00:00
Tasks: Information Retrieval, Retrieval

Our aim is to extract information about literary characters in unstructured
texts. We employ natural language processing and reasoning on domain
ontologies. The first task is to identify the main characters and the parts of
the story where these characters are described or act. We illustrate the system
in a scenario in the folktale domain. The system relies on a folktale ontology
that we have developed based on Propp's model for folktales morphology.
Improvised Salient Object Detection and Manipulation
Authors: Abhishek Maity
Date: 2015-11-10 00:00:00
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection

In case of salient subject recognition, computer algorithms have been heavily
relied on scanning of images from top-left to bottom-right systematically and
apply brute-force when attempting to locate objects of interest. Thus, the
process turns out to be quite time consuming. Here a novel approach and a
simple solution to the above problem is discussed. In this paper, we implement
an approach to object manipulation and detection through segmentation map,
which would help to desaturate or, in other words, wash out the background of
the image. Evaluation for the performance is carried out using the Jaccard
index against the well-known Ground-truth target box technique.
On the Generalization Properties of Differential Privacy
Authors: Kobbi Nissim, Uri Stemmer
Date: 2015-04-22 00:00:00

A new line of work, started with Dwork et al., studies the task of answering
statistical queries using a sample and relates the problem to the concept of
differential privacy. By the Hoeffding bound, a sample of size $O(\log
k/\alpha^2)$ suffices to answer $k$ non-adaptive queries within error $\alpha$,
where the answers are computed by evaluating the statistical queries on the
sample. This argument fails when the queries are chosen adaptively (and can
hence depend on the sample). Dwork et al. showed that if the answers are
computed with $(\epsilon,\delta)$-differential privacy then $O(\epsilon)$
accuracy is guaranteed with probability $1-O(\delta^\epsilon)$. Using the
Private Multiplicative Weights mechanism, they concluded that the sample size
can still grow polylogarithmically with the $k$.
  Very recently, Bassily et al. presented an improved bound and showed that (a
variant of) the private multiplicative weights algorithm can answer $k$
adaptively chosen statistical queries using sample complexity that grows
logarithmically in $k$. However, their results no longer hold for every
differentially private algorithm, and require modifying the private
multiplicative weights algorithm in order to obtain their high probability
bounds.
  We greatly simplify the results of Dwork et al. and improve on the bound by
showing that differential privacy guarantees $O(\epsilon)$ accuracy with
probability $1-O(\delta\log(1/\epsilon)/\epsilon)$. It would be tempting to
guess that an $(\epsilon,\delta)$-differentially private computation should
guarantee $O(\epsilon)$ accuracy with probability $1-O(\delta)$. However, we
show that this is not the case, and that our bound is tight (up to logarithmic
factors).
More General Queries and Less Generalization Error in Adaptive Data Analysis
Authors: Raef Bassily, Adam Smith, Thomas Steinke, Jonathan Ullman
Date: 2015-03-16 00:00:00

Adaptivity is an important feature of data analysis---typically the choice of
questions asked about a dataset depends on previous interactions with the same
dataset. However, generalization error is typically bounded in a non-adaptive
model, where all questions are specified before the dataset is drawn. Recent
work by Dwork et al. (STOC '15) and Hardt and Ullman (FOCS '14) initiated the
formal study of this problem, and gave the first upper and lower bounds on the
achievable generalization error for adaptive data analysis.
  Specifically, suppose there is an unknown distribution $\mathcal{P}$ and a
set of $n$ independent samples $x$ is drawn from $\mathcal{P}$. We seek an
algorithm that, given $x$ as input, "accurately" answers a sequence of
adaptively chosen "queries" about the unknown distribution $\mathcal{P}$. How
many samples $n$ must we draw from the distribution, as a function of the type
of queries, the number of queries, and the desired level of accuracy?
  In this work we make two new contributions towards resolving this question:
  *We give upper bounds on the number of samples $n$ that are needed to answer
statistical queries that improve over the bounds of Dwork et al.
  *We prove the first upper bounds on the number of samples required to answer
more general families of queries. These include arbitrary low-sensitivity
queries and the important class of convex risk minimization queries.
  As in Dwork et al., our algorithms are based on a connection between
differential privacy and generalization error, but we feel that our analysis is
simpler and more modular, which may be useful for studying these questions in
the future.
Dimension of Marginals of Kronecker Product Models
Authors: Guido Montufar, Jason Morton
Date: 2015-11-10 00:00:00

A Kronecker product model is the set of visible marginal probability
distributions of an exponential family whose sufficient statistics matrix
factorizes as a Kronecker product of two matrices, one for the visible
variables and one for the hidden variables. We estimate the dimension of these
models by the maximum rank of the Jacobian in the limit of large parameters.
The limit is described by the tropical morphism; a piecewise linear map with
pieces corresponding to slicings of the visible matrix by the normal fan of the
hidden matrix. We obtain combinatorial conditions under which the model has the
expected dimension, equal to the minimum of the number of natural parameters
and the dimension of the ambient probability simplex. Additionally, we prove
that the binary restricted Boltzmann machine always has the expected dimension.
Building a Large-scale Multimodal Knowledge Base System for Answering Visual Queries
Authors: Yuke Zhu, Ce Zhang, Christopher Ré, Li Fei-Fei
Date: 2015-07-20 00:00:00
Tasks: Knowledge Base Construction, Retrieval

The complexity of the visual world creates significant challenges for
comprehensive visual understanding. In spite of recent successes in visual
recognition, today's vision systems would still struggle to deal with visual
queries that require a deeper reasoning. We propose a knowledge base (KB)
framework to handle an assortment of visual queries, without the need to train
new classifiers for new tasks. Building such a large-scale multimodal KB
presents a major challenge of scalability. We cast a large-scale MRF into a KB
representation, incorporating visual, textual and structured data, as well as
their diverse relations. We introduce a scalable knowledge base construction
system that is capable of building a KB with half billion variables and
millions of parameters in a few hours. Our system achieves competitive results
compared to purpose-built models on standard recognition and retrieval tasks,
while exhibiting greater flexibility in answering richer visual queries.