pwc-archive/papers-with-abstracts huggingface dataset:
Darknet and Deepnet Mining for Proactive Cybersecurity Threat Intelligence
Authors: Eric Nunes, Ahmad Diab, Andrew Gunn, Ericsson Marin, Vineet Mishra, Vivin Paliath, John Robertson, Jana Shakarian, Amanda Thart, Paulo Shakarian
Date: 2016-07-28 00:00:00
Tasks: BIG-bench Machine Learning

In this paper, we present an operational system for cyber threat intelligence
gathering from various social platforms on the Internet particularly sites on
the darknet and deepnet. We focus our attention to collecting information from
hacker forum discussions and marketplaces offering products and services
focusing on malicious hacking. We have developed an operational system for
obtaining information from these sites for the purposes of identifying emerging
cyber threats. Currently, this system collects on average 305 high-quality
cyber threat warnings each week. These threat warnings include information on
newly developed malware and exploits that have not yet been deployed in a
cyber-attack. This provides a significant service to cyber-defenders. The
system is significantly augmented through the use of various data mining and
machine learning techniques. With the use of machine learning models, we are
able to recall 92% of products in marketplaces and 80% of discussions on forums
relating to malicious hacking with high precision. We perform preliminary
analysis on the data collected, demonstrating its application to aid a security
expert for better threat analysis.
A Deep Primal-Dual Network for Guided Depth Super-Resolution
Authors: Gernot Riegler, David Ferstl, Matthias Rüther, Horst Bischof
Date: 2016-07-28 00:00:00
Tasks: Rolling Shutter Correction, Super-Resolution

In this paper we present a novel method to increase the spatial resolution of
depth images. We combine a deep fully convolutional network with a non-local
variational method in a deep primal-dual network. The joint network computes a
noise-free, high-resolution estimate from a noisy, low-resolution input depth
map. Additionally, a high-resolution intensity image is used to guide the
reconstruction in the network. By unrolling the optimization steps of a
first-order primal-dual algorithm and formulating it as a network, we can train
our joint method end-to-end. This not only enables us to learn the weights of
the fully convolutional network, but also to optimize all parameters of the
variational method and its optimization procedure. The training of such a deep
network requires a large dataset for supervision. Therefore, we generate
high-quality depth maps and corresponding color images with a physically based
renderer. In an exhaustive evaluation we show that our method outperforms the
state-of-the-art on multiple benchmarks.
The iterative reweighted Mixed-Norm Estimate for spatio-temporal MEG/EEG source reconstruction
Authors: Daniel Strohmeier, Yousra Bekhti, Jens Haueisen, Alexandre Gramfort
Date: 2016-07-28 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Source imaging based on magnetoencephalography (MEG) and
electroencephalography (EEG) allows for the non-invasive analysis of brain
activity with high temporal and good spatial resolution. As the
bioelectromagnetic inverse problem is ill-posed, constraints are required. For
the analysis of evoked brain activity, spatial sparsity of the neuronal
activation is a common assumption. It is often taken into account using convex
constraints based on the l1-norm. The resulting source estimates are however
biased in amplitude and often suboptimal in terms of source selection due to
high correlations in the forward model. In this work, we demonstrate that an
inverse solver based on a block-separable penalty with a Frobenius norm per
block and a l0.5-quasinorm over blocks addresses both of these issues. For
solving the resulting non-convex optimization problem, we propose the iterative
reweighted Mixed Norm Estimate (irMxNE), an optimization scheme based on
iterative reweighted convex surrogate optimization problems, which are solved
efficiently using a block coordinate descent scheme and an active set strategy.
We compare the proposed sparse imaging method to the dSPM and the RAP-MUSIC
approach based on two MEG data sets. We provide empirical evidence based on
simulations and analysis of MEG data that the proposed method improves on the
standard Mixed Norm Estimate (MxNE) in terms of amplitude bias, support
recovery, and stability.
Positive Definite Estimation of Large Covariance Matrix Using Generalized Nonconvex Penalties
Authors: Fei Wen, Yuan Yang, Peilin Liu, Robert C. Qiu
Date: 2016-04-15 00:00:00
Tasks: Clustering

This work addresses the issue of large covariance matrix estimation in
high-dimensional statistical analysis. Recently, improved iterative algorithms
with positive-definite guarantee have been developed. However, these algorithms
cannot be directly extended to use a nonconvex penalty for sparsity inducing.
Generally, a nonconvex penalty has the capability of ameliorating the bias
problem of the popular convex lasso penalty, and thus is more advantageous. In
this work, we propose a class of positive-definite covariance estimators using
generalized nonconvex penalties. We develop a first-order algorithm based on
the alternating direction method framework to solve the nonconvex optimization
problem efficiently. The convergence of this algorithm has been proved.
Further, the statistical properties of the new estimators have been analyzed
for generalized nonconvex penalties. Moreover, extension of this algorithm to
covariance estimation from sketched measurements has been considered. The
performances of the new estimators have been demonstrated by both a simulation
study and a gene clustering example for tumor tissues. Code for the proposed
estimators is available at https://github.com/FWen/Nonconvex-PDLCE.git.
Faceless Person Recognition; Privacy Implications in Social Media
Authors: Seong Joon Oh, Rodrigo Benenson, Mario Fritz, Bernt Schiele
Date: 2016-07-28 00:00:00
Tasks: Person Recognition

As we shift more of our lives into the virtual domain, the volume of data
shared on the web keeps increasing and presents a threat to our privacy. This
works contributes to the understanding of privacy implications of such data
sharing by analysing how well people are recognisable in social media data. To
facilitate a systematic study we define a number of scenarios considering
factors such as how many heads of a person are tagged and if those heads are
obfuscated or not. We propose a robust person recognition system that can
handle large variations in pose and clothing, and can be trained with few
training samples. Our results indicate that a handful of images is enough to
threaten users' privacy, even in the presence of obfuscation. We show detailed
experimental results, and discuss their implications.
Video Registration in Egocentric Vision under Day and Night Illumination Changes
Authors: Stefano Alletto, Giuseppe Serra, Rita Cucchiara
Date: 2016-07-28 00:00:00

With the spread of wearable devices and head mounted cameras, a wide range of
application requiring precise user localization is now possible. In this paper
we propose to treat the problem of obtaining the user position with respect to
a known environment as a video registration problem. Video registration, i.e.
the task of aligning an input video sequence to a pre-built 3D model, relies on
a matching process of local keypoints extracted on the query sequence to a 3D
point cloud. The overall registration performance is strictly tied to the
actual quality of this 2D-3D matching, and can degrade if environmental
conditions such as steep changes in lighting like the ones between day and
night occur. To effectively register an egocentric video sequence under these
conditions, we propose to tackle the source of the problem: the matching
process. To overcome the shortcomings of standard matching techniques, we
introduce a novel embedding space that allows us to obtain robust matches by
jointly taking into account local descriptors, their spatial arrangement and
their temporal robustness. The proposal is evaluated using unconstrained
egocentric video sequences both in terms of matching quality and resulting
registration performance using different 3D models of historical landmarks. The
results show that the proposed method can outperform state of the art
registration algorithms, in particular when dealing with the challenges of
night and day sequences.
Stereo Video Deblurring
Authors: Anita Sellent, Carsten Rother, Stefan Roth
Date: 2016-07-28 00:00:00
Tasks: Deblurring, Video Deblurring

Videos acquired in low-light conditions often exhibit motion blur, which
depends on the motion of the objects relative to the camera. This is not only
visually unpleasing, but can hamper further processing. With this paper we are
the first to show how the availability of stereo video can aid the challenging
video deblurring task. We leverage 3D scene flow, which can be estimated
robustly even under adverse conditions. We go beyond simply determining the
object motion in two ways: First, we show how a piecewise rigid 3D scene flow
representation allows to induce accurate blur kernels via local homographies.
Second, we exploit the estimated motion boundaries of the 3D scene flow to
mitigate ringing artifacts using an iterative weighting scheme. Being aware of
3D object motion, our approach can deal robustly with an arbitrary number of
independently moving objects. We demonstrate its benefit over state-of-the-art
video deblurring using quantitative and qualitative experiments on rendered
scenes and real videos.
Randomised Algorithm for Feature Selection and Classification
Authors: Aida Brankovic, Alessandro Falsone, Maria Prandini, Luigi Piroddi
Date: 2016-07-28 00:00:00
Tasks: Classification, feature selection, General Classification

We here introduce a novel classification approach adopted from the nonlinear
model identification framework, which jointly addresses the feature selection
and classifier design tasks. The classifier is constructed as a polynomial
expansion of the original attributes and a model structure selection process is
applied to find the relevant terms of the model. The selection method
progressively refines a probability distribution defined on the model structure
space, by extracting sample models from the current distribution and using the
aggregate information obtained from the evaluation of the population of models
to reinforce the probability of extracting the most important terms. To reduce
the initial search space, distance correlation filtering can be applied as a
preprocessing technique. The proposed method is evaluated and compared to other
well-known feature selection and classification methods on standard benchmark
classification problems. The results show the effectiveness of the proposed
method with respect to competitor methods both in terms of classification
accuracy and model complexity. The obtained models have a simple structure,
easily amenable to interpretation and analysis.
Deep Image Retrieval: Learning global representations for image search
Authors: Albert Gordo, Jon Almazan, Jerome Revaud, Diane Larlus
Date: 2016-04-05 00:00:00
Tasks: Image Retrieval, Region Proposal, Retrieval

We propose a novel approach for instance-level image retrieval. It produces a
global and compact fixed-length representation for each image by aggregating
many region-wise descriptors. In contrast to previous works employing
pre-trained deep networks as a black box to produce features, our method
leverages a deep architecture trained for the specific task of image retrieval.
Our contribution is twofold: (i) we leverage a ranking framework to learn
convolution and projection weights that are used to build the region features;
and (ii) we employ a region proposal network to learn which regions should be
pooled to form the final global descriptor. We show that using clean training
data is key to the success of our approach. To that aim, we use a large scale
but noisy landmark dataset and develop an automatic cleaning approach. The
proposed architecture produces a global image representation in a single
forward pass. Our approach significantly outperforms previous approaches based
on global descriptors on standard datasets. It even surpasses most prior works
based on costly local descriptor indexing and spatial verification. Additional
material is available at www.xrce.xerox.com/Deep-Image-Retrieval.
Inter-Battery Topic Representation Learning
Authors: Cheng Zhang, Hedvig Kjellstrom, Carl Henrik Ek
Date: 2016-05-19 00:00:00
Tasks: feature selection, Representation Learning, Topic Models

In this paper, we present the Inter-Battery Topic Model (IBTM). Our approach
extends traditional topic models by learning a factorized latent variable
representation. The structured representation leads to a model that marries
benefits traditionally associated with a discriminative approach, such as
feature selection, with those of a generative model, such as principled
regularization and ability to handle missing data. The factorization is
provided by representing data in terms of aligned pairs of observations as
different views. This provides means for selecting a representation that
separately models topics that exist in both views from the topics that are
unique to a single view. This structured consolidation allows for efficient and
robust inference and provides a compact and efficient representation. Learning
is performed in a Bayesian fashion by maximizing a rigorous bound on the
log-likelihood. Firstly, we illustrate the benefits of the model on a synthetic
dataset,. The model is then evaluated in both uni- and multi-modality settings
on two different classification tasks with off-the-shelf convolutional neural
network (CNN) features which generate state-of-the-art results with extremely
compact representations.
A Siamese Long Short-Term Memory Architecture for Human Re-Identification
Authors: Rahul Rama Varior, Bing Shuai, Jiwen Lu, Dong Xu, Gang Wang
Date: 2016-07-28 00:00:00
Proceeding: European Conference on Computer Vision 2016 10
Tasks: Person Re-Identification

Matching pedestrians across multiple camera views known as human
re-identification (re-identification) is a challenging problem in visual
surveillance. In the existing works concentrating on feature extraction,
representations are formed locally and independent of other regions. We present
a novel siamese Long Short-Term Memory (LSTM) architecture that can process
image regions sequentially and enhance the discriminative capability of local
feature representation by leveraging contextual information. The feedback
connections and internal gating mechanism of the LSTM cells enable our model to
memorize the spatial dependencies and selectively propagate relevant contextual
information through the network. We demonstrate improved performance compared
to the baseline algorithm with no LSTM units and promising results compared to
state-of-the-art methods on Market-1501, CUHK03 and VIPeR datasets.
Visualization of the internal mechanism of LSTM cells shows meaningful patterns
can be learned by our method.
Variational perturbation and extended Plefka approaches to dynamics on random networks: the case of the kinetic Ising model
Authors: Ludovica Bachschmid-Romano, Claudia Battistin, Manfred Opper, Yasser Roudi
Date: 2016-07-28 00:00:00

We describe and analyze some novel approaches for studying the dynamics of
Ising spin glass models. We first briefly consider the variational approach
based on minimizing the Kullback-Leibler divergence between independent
trajectories and the real ones and note that this approach only coincides with
the mean field equations from the saddle point approximation to the generating
functional when the dynamics is defined through a logistic link function, which
is the case for the kinetic Ising model with parallel update. We then spend the
rest of the paper developing two ways of going beyond the saddle point
approximation to the generating functional. In the first one, we develop a
variational perturbative approximation to the generating functional by
expanding the action around a quadratic function of the local fields and
conjugate local fields whose parameters are optimized. We derive analytical
expressions for the optimal parameters and show that when the optimization is
suitably restricted, we recover the mean field equations that are exact for the
fully asymmetric random couplings (M\'ezard and Sakellariou, 2011). However,
without this restriction the results are different. We also describe an
extended Plefka expansion in which in addition to the magnetization, we also
fix the correlation and response functions. Finally, we numerically study the
performance of these approximations for Sherrington-Kirkpatrick type couplings
for various coupling strengths, degrees of coupling symmetry and external
fields. We show that the dynamical equations derived from the extended Plefka
expansion outperform the others in all regimes, although it is computationally
more demanding. The unconstrained variational approach does not perform well in
the small coupling regime, while it approaches dynamical TAP equations of
(Roudi and Hertz, 2011) for strong couplings.
Multi-Agent Continuous Transportation with Online Balanced Partitioning
Authors: Chao Wang, Somchaya Liemhetcharat, Kian Hsiang Low
Date: 2015-11-23 00:00:00

We introduce the concept of continuous transportation task to the context of
multi-agent systems. A continuous transportation task is one in which a
multi-agent team visits a number of fixed locations, picks up objects, and
delivers them to a final destination. The goal is to maximize the rate of
transportation while the objects are replenished over time. Examples of
problems that need continuous transportation are foraging, area sweeping, and
first/last mile problem. Previous approaches typically neglect the interference
and are highly dependent on communications among agents. Some also incorporate
an additional reconnaissance agent to gather information. In this paper, we
present a hybrid of centralized and distributed approaches that minimize the
interference and communications in the multi-agent team without the need for a
reconnaissance agent. We contribute two partitioning-transportation algorithms
inspired by existing algorithms, and contribute one novel online
partitioning-transportation algorithm with information gathering in the
multi-agent team. Our algorithms have been implemented and tested extensively
in the simulation. The results presented in this paper demonstrate the
effectiveness of our algorithms that outperform the existing algorithms, even
without any communications between the agents and without the presence of a
reconnaissance agent.
Local Feature Detectors, Descriptors, and Image Representations: A Survey
Authors: Yusuke Uchida
Date: 2016-07-28 00:00:00
Tasks: Image Retrieval, Retrieval, Survey

With the advances in both stable interest region detectors and robust and
distinctive descriptors, local feature-based image or object retrieval has
become a popular research topic. %All of the local feature-based image
retrieval system involves two important processes: local feature extraction and
image representation. The other key technology for image retrieval systems is
image representation such as the bag-of-visual words (BoVW), Fisher vector, or
Vector of Locally Aggregated Descriptors (VLAD) framework. In this paper, we
review local features and image representations for image retrieval. Because
many and many methods are proposed in this area, these methods are grouped into
several classes and summarized. In addition, recent deep learning-based
approaches for image retrieval are briefly reviewed.
25 years of CNNs: Can we compare to human abstraction capabilities?
Authors: Sebastian Stabinger, Antonio Rodríguez-Sánchez, Justus Piater
Date: 2016-07-28 00:00:00

We try to determine the progress made by convolutional neural networks over
the past 25 years in classifying images into abstractc lasses. For this purpose
we compare the performance of LeNet to that of GoogLeNet at classifying
randomly generated images which are differentiated by an abstract property
(e.g., one class contains two objects of the same size, the other class two
objects of different sizes). Our results show that there is still work to do in
order to solve vision problems humans are able to solve without much
difficulty.
Tensor Representations via Kernel Linearization for Action Recognition from 3D Skeletons (Extended Version)
Authors: Piotr Koniusz, Anoop Cherian, Fatih Porikli
Date: 2016-04-01 00:00:00
Tasks: 3D Action Recognition, Action Recognition, Form, Temporal Action Localization

In this paper, we explore tensor representations that can compactly capture
higher-order relationships between skeleton joints for 3D action recognition.
We first define RBF kernels on 3D joint sequences, which are then linearized to
form kernel descriptors. The higher-order outer-products of these kernel
descriptors form our tensor representations. We present two different kernels
for action recognition, namely (i) a sequence compatibility kernel that
captures the spatio-temporal compatibility of joints in one sequence against
those in the other, and (ii) a dynamics compatibility kernel that explicitly
models the action dynamics of a sequence. Tensors formed from these kernels are
then used to train an SVM. We present experiments on several benchmark datasets
and demonstrate state of the art results, substantiating the effectiveness of
our representations.
Review Based Rating Prediction
Authors: Tal Hadad
Date: 2016-06-30 00:00:00
Tasks: Information Retrieval, Marketing, Prediction, Recommendation Systems, Retrieval

Recommendation systems are an important units in today's e-commerce
applications, such as targeted advertising, personalized marketing and
information retrieval. In recent years, the importance of contextual
information has motivated generation of personalized recommendations according
to the available contextual information of users.
  Compared to the traditional systems which mainly utilize users' rating
history, review-based recommendation hopefully provide more relevant results to
users. We introduce a review-based recommendation approach that obtains
contextual information by mining user reviews. The proposed approach relate to
features obtained by analyzing textual reviews using methods developed in
Natural Language Processing (NLP) and information retrieval discipline to
compute a utility function over a given item. An item utility is a measure that
shows how much it is preferred according to user's current context.
  In our system, the context inference is modeled as similarity between the
users reviews history and the item reviews history. As an example application,
we used our method to mine contextual data from customers' reviews of movies
and use it to produce review-based rating prediction. The predicted ratings can
generate recommendations that are item-based and should appear at the
recommended items list in the product page. Our evaluations suggest that our
system can help produce better prediction rating scores in comparison to the
standard prediction methods.