pwc-archive/papers-with-abstracts huggingface dataset:
Building and Measuring Privacy-Preserving Predictive Blacklists
Authors: Luca Melis, Apostolos Pyrgelis, Emiliano De Cristofaro
Date: 2015-12-13 00:00:00
Tasks: Privacy Preserving

(Withdrawn) Collaborative security initiatives are increasingly often
advocated to improve timeliness and effectiveness of threat mitigation. Among
these, collaborative predictive blacklisting (CPB) aims to forecast attack
sources based on alerts contributed by multiple organizations that might be
targeted in similar ways. Alas, CPB proposals thus far have only focused on
improving hit counts, but overlooked the impact of collaboration on false
positives and false negatives. Moreover, sharing threat intelligence often
prompts important privacy, confidentiality, and liability issues. In this
paper, we first provide a comprehensive measurement analysis of two
state-of-the-art CPB systems: one that uses a trusted central party to collect
alerts [Soldo et al., Infocom'10] and a peer-to-peer one relying on controlled
data sharing [Freudiger et al., DIMVA'15], studying the impact of collaboration
on both correct and incorrect predictions. Then, we present a novel
privacy-friendly approach that significantly improves over previous work,
achieving a better balance of true and false positive rates, while minimizing
information disclosure. Finally, we present an extension that allows our system
to scale to very large numbers of organizations.
Graph-based Isometry Invariant Representation Learning
Authors: Renata Khasanova, Pascal Frossard
Date: 2017-03-01 00:00:00
Proceeding: ICML 2017 8
Tasks: General Classification, Representation Learning, Translation, Video Classification

Learning transformation invariant representations of visual data is an
important problem in computer vision. Deep convolutional networks have
demonstrated remarkable results for image and video classification tasks.
However, they have achieved only limited success in the classification of
images that undergo geometric transformations. In this work we present a novel
Transformation Invariant Graph-based Network (TIGraNet), which learns
graph-based features that are inherently invariant to isometric transformations
such as rotation and translation of input images. In particular, images are
represented as signals on graphs, which permits to replace classical
convolution and pooling layers in deep networks with graph spectral convolution
and dynamic graph pooling layers that together contribute to invariance to
isometric transformations. Our experiments show high performance on rotated and
translated images from the test set compared to classical architectures that
are very sensitive to transformations in the data. The inherent invariance
properties of our framework provide key advantages, such as increased
resiliency to data variability and sustained performance with limited training
sets.
Reasoning in Non-Probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples
Authors: Tarek R. Besold, Artur d'Avila Garcez, Keith Stenning, Leendert van der Torre, Michiel van Lambalgen
Date: 2017-01-18 00:00:00

This article aims to achieve two goals: to show that probability is not the
only way of dealing with uncertainty (and even more, that there are kinds of
uncertainty which are for principled reasons not addressable with probabilistic
means); and to provide evidence that logic-based methods can well support
reasoning with uncertainty. For the latter claim, two paradigmatic examples are
presented: Logic Programming with Kleene semantics for modelling reasoning from
information in a discourse, to an interpretation of the state of affairs of the
intended model, and a neural-symbolic implementation of Input/Output logic for
dealing with uncertainty in dynamic normative contexts.
Convergence rate of a simulated annealing algorithm with noisy observations
Authors: Clément Bouttier, Ioana Gavra
Date: 2017-03-01 00:00:00
Tasks: global-optimization

In this paper we propose a modified version of the simulated annealing
algorithm for solving a stochastic global optimization problem. More precisely,
we address the problem of finding a global minimizer of a function with noisy
evaluations. We provide a rate of convergence and its optimized parametrization
to ensure a minimal number of evaluations for a given accuracy and a confidence
level close to 1. This work is completed with a set of numerical
experimentations and assesses the practical performance both on benchmark test
cases and on real world examples.
Investigating the Characteristics of One-Sided Matching Mechanisms Under Various Preferences and Risk Attitudes
Authors: Hadi Hosseini, Kate Larson, Robin Cohen
Date: 2017-03-01 00:00:00

One-sided matching mechanisms are fundamental for assigning a set of
indivisible objects to a set of self-interested agents when monetary transfers
are not allowed. Two widely-studied randomized mechanisms in multiagent
settings are the Random Serial Dictatorship (RSD) and the Probabilistic Serial
Rule (PS). Both mechanisms require only that agents specify ordinal preferences
and have a number of desirable economic and computational properties. However,
the induced outcomes of the mechanisms are often incomparable and thus there
are challenges when it comes to deciding which mechanism to adopt in practice.
In this paper, we first consider the space of general ordinal preferences and
provide empirical results on the (in)comparability of RSD and PS. We analyze
their respective economic properties under general and lexicographic
preferences. We then instantiate utility functions with the goal of gaining
insights on the manipulability, efficiency, and envyfreeness of the mechanisms
under different risk-attitude models. Our results hold under various preference
distribution models, which further confirm the broad use of RSD in most
practical applications.
Tracing Linguistic Relations in Winning and Losing Sides of Explicit Opposing Groups
Authors: Ceyda Sanli, Anupam Mondal, Erik Cambria
Date: 2017-03-01 00:00:00
Tasks: Decision Making, Opinion Mining

Linguistic relations in oral conversations present how opinions are
constructed and developed in a restricted time. The relations bond ideas,
arguments, thoughts, and feelings, re-shape them during a speech, and finally
build knowledge out of all information provided in the conversation. Speakers
share a common interest to discuss. It is expected that each speaker's reply
includes duplicated forms of words from previous speakers. However, linguistic
adaptation is observed and evolves in a more complex path than just
transferring slightly modified versions of common concepts. A conversation
aiming a benefit at the end shows an emergent cooperation inducing the
adaptation. Not only cooperation, but also competition drives the adaptation or
an opposite scenario and one can capture the dynamic process by tracking how
the concepts are linguistically linked. To uncover salient complex dynamic
events in verbal communications, we attempt to discover self-organized
linguistic relations hidden in a conversation with explicitly stated winners
and losers. We examine open access data of the United States Supreme Court. Our
understanding is crucial in big data research to guide how transition states in
opinion mining and decision-making should be modeled and how this required
knowledge to guide the model should be pinpointed, by filtering large amount of
data.
Random crossings in dependency trees
Authors: Ramon Ferrer-i-Cancho
Date: 2013-05-20 00:00:00
Tasks: Sentence

It has been hypothesized that the rather small number of crossings in real
syntactic dependency trees is a side-effect of pressure for dependency length
minimization. Here we answer a related important research question: what would
be the expected number of crossings if the natural order of a sentence was lost
and replaced by a random ordering? We show that this number depends only on the
number of vertices of the dependency tree (the sentence length) and the second
moment about zero of vertex degrees. The expected number of crossings is
minimum for a star tree (crossings are impossible) and maximum for a linear
tree (the number of crossings is of the order of the square of the sequence
length).
Big Learning with Bayesian Methods
Authors: Jun Zhu, Jianfei Chen, Wen-Bo Hu, Bo Zhang
Date: 2014-11-24 00:00:00
Tasks: Bayesian Inference, BIG-bench Machine Learning, Distributed Computing

Explosive growth in data and availability of cheap computing resources have
sparked increasing interest in Big learning, an emerging subfield that studies
scalable machine learning algorithms, systems, and applications with Big Data.
Bayesian methods represent one important class of statistic methods for machine
learning, with substantial recent developments on adaptive, flexible and
scalable Bayesian learning. This article provides a survey of the recent
advances in Big learning with Bayesian methods, termed Big Bayesian Learning,
including nonparametric Bayesian methods for adaptively inferring model
complexity, regularized Bayesian inference for improving the flexibility via
posterior regularization, and scalable algorithms and systems based on
stochastic subsampling and distributed computing for dealing with large-scale
applications.
Random Feature Expansions for Deep Gaussian Processes
Authors: Kurt Cutajar, Edwin V. Bonilla, Pietro Michiardi, Maurizio Filippone
Date: 2016-10-14 00:00:00
Proceeding: ICML 2017 8
Tasks: Gaussian Processes, Variational Inference

The composition of multiple Gaussian Processes as a Deep Gaussian Process
(DGP) enables a deep probabilistic nonparametric approach to flexibly tackle
complex machine learning problems with sound quantification of uncertainty.
Existing inference approaches for DGP models have limited scalability and are
notoriously cumbersome to construct. In this work, we introduce a novel
formulation of DGPs based on random feature expansions that we train using
stochastic variational inference. This yields a practical learning framework
which significantly advances the state-of-the-art in inference for DGPs, and
enables accurate quantification of uncertainty. We extensively showcase the
scalability and performance of our proposal on several datasets with up to 8
million observations, and various DGP architectures with up to 30 hidden
layers.
Online EM Algorithm for Latent Data Models
Authors: Olivier Cappé, Eric Moulines
Date: 2007-12-27 00:00:00

In this contribution, we propose a generic online (also sometimes called
adaptive or recursive) version of the Expectation-Maximisation (EM) algorithm
applicable to latent variable models of independent observations. Compared to
the algorithm of Titterington (1984), this approach is more directly connected
to the usual EM algorithm and does not rely on integration with respect to the
complete data distribution. The resulting algorithm is usually simpler and is
shown to achieve convergence to the stationary points of the Kullback-Leibler
divergence between the marginal distribution of the observation and the model
distribution at the optimal rate, i.e., that of the maximum likelihood
estimator. In addition, the proposed approach is also suitable for conditional
(or regression) models, as illustrated in the case of the mixture of linear
regressions model.
PetroSurf3D - A Dataset for high-resolution 3D Surface Segmentation
Authors: Georg Poier, Markus Seidl, Matthias Zeppelzauer, Christian Reinbacher, Martin Schaich, Giovanna Bellandi, Alberto Marretta, Horst Bischof
Date: 2016-10-06 00:00:00
Tasks: Interactive Segmentation, Segmentation, Vocal Bursts Intensity Prediction

The development of powerful 3D scanning hardware and reconstruction
algorithms has strongly promoted the generation of 3D surface reconstructions
in different domains. An area of special interest for such 3D reconstructions
is the cultural heritage domain, where surface reconstructions are generated to
digitally preserve historical artifacts. While reconstruction quality nowadays
is sufficient in many cases, the robust analysis (e.g. segmentation, matching,
and classification) of reconstructed 3D data is still an open topic. In this
paper, we target the automatic and interactive segmentation of high-resolution
3D surface reconstructions from the archaeological domain. To foster research
in this field, we introduce a fully annotated and publicly available
large-scale 3D surface dataset including high-resolution meshes, depth maps and
point clouds as a novel benchmark dataset to the community. We provide baseline
results for our existing random forest-based approach and for the first time
investigate segmentation with convolutional neural networks (CNNs) on the data.
Results show that both approaches have complementary strengths and weaknesses
and that the provided dataset represents a challenge for future research.
Phylogenetic Tools in Astrophysics
Authors: Didier Fraix-Burnet
Date: 2017-03-01 00:00:00
Tasks: Clustering, Diversity

Multivariate clustering in astrophysics is a recent development justified by
the bigger and bigger surveys of the sky. The phylogenetic approach is probably
the most unexpected technique that has appeared for the unsupervised
classification of galaxies, stellar populations or globular clusters. On one
side, this is a somewhat natural way of classifying astrophysical entities
which are all evolving objects. On the other side, several conceptual and
practical difficulties arize, such as the hierarchical representation of the
astrophysical diversity, the continuous nature of the parameters, and the
adequation of the result to the usual practice for the physical interpretation.
Most of these have now been solved through the studies of limited samples of
stellar clusters and galaxies. Up to now, only the Maximum Parsimony
(cladistics) has been used since it is the simplest and most general
phylogenetic technique. Probabilistic and network approaches are obvious
extensions that should be explored in the future.
Human Eye Visual Hyperacuity: A New Paradigm for Sensing?
Authors: Adur Lagunas, Oier Dominguez, Susana Martinez-Conde, Stephen L. Macknik, Carlos del-Rio
Date: 2017-03-01 00:00:00

The human eye appears to be using a low number of sensors for image
capturing. Furthermore, regarding the physical dimensions of
cones-photoreceptors responsible for the sharp central vision-, we may realize
that these sensors are of a relatively small size and area. Nonetheless, the
eye is capable to obtain high resolution images due to visual hyperacuity and
presents an impressive sensitivity and dynamic range when set against
conventional digital cameras of similar characteristics. This article is based
on the hypothesis that the human eye may be benefiting from diffraction to
improve both image resolution and acquisition process. The developed method
intends to explain and simulate using MATLAB software the visual hyperacuity:
the introduction of a controlled diffraction pattern at an initial stage,
enables the use of a reduced number of sensors for capturing the image and
makes possible a subsequent processing to improve the final image resolution.
The results have been compared with the outcome of an equivalent system but in
absence of diffraction, achieving promising results. The main conclusion of
this work is that diffraction could be helpful for capturing images or signals
when a small number of sensors available, which is far from being a
resolution-limiting factor.
Learning A Physical Long-term Predictor
Authors: Sebastien Ehrhardt, Aron Monszpart, Niloy J. Mitra, Andrea Vedaldi
Date: 2017-03-01 00:00:00

Evolution has resulted in highly developed abilities in many natural
intelligences to quickly and accurately predict mechanical phenomena. Humans
have successfully developed laws of physics to abstract and model such
mechanical phenomena. In the context of artificial intelligence, a recent line
of work has focused on estimating physical parameters based on sensory data and
use them in physical simulators to make long-term predictions. In contrast, we
investigate the effectiveness of a single neural network for end-to-end
long-term prediction of mechanical phenomena. Based on extensive evaluation, we
demonstrate that such networks can outperform alternate approaches having even
access to ground-truth physical simulators, especially when some physical
parameters are unobserved or not known a-priori. Further, our network outputs a
distribution of outcomes to capture the inherent uncertainty in the data. Our
approach demonstrates for the first time the possibility of making actionable
long-term predictions from sensor data without requiring to explicitly model
the underlying physical laws.
Improving Object Detection with Region Similarity Learning
Authors: Feng Gao, Yihang Lou, Yan Bai, Shiqi Wang, Tiejun Huang, Ling-Yu Duan
Date: 2017-03-01 00:00:00
Tasks: Multi-Task Learning, Object, object-detection, Object Detection, Triplet

Object detection aims to identify instances of semantic objects of a certain
class in images or videos. The success of state-of-the-art approaches is
attributed to the significant progress of object proposal and convolutional
neural networks (CNNs). Most promising detectors involve multi-task learning
with an optimization objective of softmax loss and regression loss. The first
is for multi-class categorization, while the latter is for improving
localization accuracy. However, few of them attempt to further investigate the
hardness of distinguishing different sorts of distracting background regions
(i.e., negatives) from true object regions (i.e., positives). To improve the
performance of classifying positive object regions vs. a variety of negative
background regions, we propose to incorporate triplet embedding into learning
objective. The triplet units are formed by assigning each negative region to a
meaningful object class and establishing class- specific negatives, followed by
triplets construction. Over the benchmark PASCAL VOC 2007, the proposed triplet
em- bedding has improved the performance of well-known FastRCNN model with a
mAP gain of 2.1%. In particular, the state-of-the-art approach OHEM can benefit
from the triplet embedding and has achieved a mAP improvement of 1.2%.
Incorporating Intra-Class Variance to Fine-Grained Visual Recognition
Authors: Yan Bai, Feng Gao, Yihang Lou, Shiqi Wang, Tiejun Huang, Ling-Yu Duan
Date: 2017-03-01 00:00:00
Tasks: Fine-Grained Visual Recognition, Metric Learning, Retrieval, Triplet

Fine-grained visual recognition aims to capture discriminative
characteristics amongst visually similar categories. The state-of-the-art
research work has significantly improved the fine-grained recognition
performance by deep metric learning using triplet network. However, the impact
of intra-category variance on the performance of recognition and robust feature
representation has not been well studied. In this paper, we propose to leverage
intra-class variance in metric learning of triplet network to improve the
performance of fine-grained recognition. Through partitioning training images
within each category into a few groups, we form the triplet samples across
different categories as well as different groups, which is called Group
Sensitive TRiplet Sampling (GS-TRS). Accordingly, the triplet loss function is
strengthened by incorporating intra-class variance with GS-TRS, which may
contribute to the optimization objective of triplet network. Extensive
experiments over benchmark datasets CompCar and VehicleID show that the
proposed GS-TRS has significantly outperformed state-of-the-art approaches in
both classification and retrieval tasks.
EMNIST: an extension of MNIST to handwritten letters
Authors: Gregory Cohen, Saeed Afshar, Jonathan Tapson, André van Schaik
Date: 2017-02-17 00:00:00
Tasks: General Classification, Image Classification

The MNIST dataset has become a standard benchmark for learning,
classification and computer vision systems. Contributing to its widespread
adoption are the understandable and intuitive nature of the task, its
relatively small size and storage requirements and the accessibility and
ease-of-use of the database itself. The MNIST database was derived from a
larger dataset known as the NIST Special Database 19 which contains digits,
uppercase and lowercase handwritten letters. This paper introduces a variant of
the full NIST dataset, which we have called Extended MNIST (EMNIST), which
follows the same conversion paradigm used to create the MNIST dataset. The
result is a set of datasets that constitute a more challenging classification
tasks involving letters and digits, and that shares the same image structure
and parameters as the original MNIST task, allowing for direct compatibility
with all existing classifiers and systems. Benchmark results are presented
along with a validation of the conversion process through the comparison of the
classification results on converted NIST digits and the MNIST digits.
Scalable Learning of Non-Decomposable Objectives
Authors: Elad ET. Eban, Mariano Schain, Alan Mackey, Ariel Gordon, Rif A. Saurous, Gal Elidan
Date: 2016-08-16 00:00:00
Tasks: Retrieval

Modern retrieval systems are often driven by an underlying machine learning
model. The goal of such systems is to identify and possibly rank the few most
relevant items for a given query or context. Thus, such systems are typically
evaluated using a ranking-based performance metric such as the area under the
precision-recall curve, the $F_\beta$ score, precision at fixed recall, etc.
Obviously, it is desirable to train such systems to optimize the metric of
interest.
  In practice, due to the scalability limitations of existing approaches for
optimizing such objectives, large-scale retrieval systems are instead trained
to maximize classification accuracy, in the hope that performance as measured
via the true objective will also be favorable. In this work we present a
unified framework that, using straightforward building block bounds, allows for
highly scalable optimization of a wide range of ranking-based objectives. We
demonstrate the advantage of our approach on several real-life retrieval
problems that are significantly larger than those considered in the literature,
while achieving substantial improvement in performance over the
accuracy-objective baseline.
Saliency Fusion in Eigenvector Space with Multi-Channel Pulse Coupled Neural Network
Authors: Nevrez Imamoglu, Zhixuan Wei, Huangjun Shi, Yuki Yoshida, Myagmarbayar Nergui, Jose Gonzalez, Dongyun Gu, Weidong Chen, Kenzo Nonami, Wenwei Yu
Date: 2017-03-01 00:00:00

Saliency computation has become a popular research field for many
applications due to the useful information provided by saliency maps. For a
saliency map, local relations around the salient regions in multi-channel
perspective should be taken into consideration by aiming uniformity on the
region of interest as an internal approach. And, irrelevant salient regions
have to be avoided as much as possible. Most of the works achieve these
criteria with external processing modules; however, these can be accomplished
during the conspicuity map fusion process. Therefore, in this paper, a new
model is proposed for saliency/conspicuity map fusion with two concepts: a)
input image transformation relying on the principal component analysis (PCA),
and b) saliency conspicuity map fusion with multi-channel pulsed coupled neural
network (m-PCNN). Experimental results, which are evaluated by precision,
recall, F-measure, and area under curve (AUC), support the reliability of the
proposed method by enhancing the saliency computation.