pwc-archive/papers-with-abstracts huggingface dataset:
ACTRCE: Augmenting Experience via Teacher's Advice For Multi-Goal Reinforcement Learning
Authors: Harris Chan, Yuhuai Wu, Jamie Kiros, Sanja Fidler, Jimmy Ba
Date: 2019-02-12 00:00:00
Tasks: Multi-Goal Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Sparse reward is one of the most challenging problems in reinforcement
learning (RL). Hindsight Experience Replay (HER) attempts to address this issue
by converting a failed experience to a successful one by relabeling the goals.
Despite its effectiveness, HER has limited applicability because it lacks a
compact and universal goal representation. We present Augmenting experienCe via
TeacheR's adviCE (ACTRCE), an efficient reinforcement learning technique that
extends the HER framework using natural language as the goal representation. We
first analyze the differences among goal representation, and show that ACTRCE
can efficiently solve difficult reinforcement learning problems in challenging
3D navigation tasks, whereas HER with non-language goal representation failed
to learn. We also show that with language goal representations, the agent can
generalize to unseen instructions, and even generalize to instructions with
unseen lexicons. We further demonstrate it is crucial to use hindsight advice
to solve challenging tasks, and even small amount of advice is sufficient for
the agent to achieve good performance.
Center of circle after perspective transformation
Authors: Xi Wang, Albert Chern, Marc Alexa
Date: 2019-02-12 00:00:00

Video-based glint-free eye tracking commonly estimates gaze direction based
on the pupil center. The boundary of the pupil is fitted with an ellipse and
the euclidean center of the ellipse in the image is taken as the center of the
pupil. However, the center of the pupil is generally not mapped to the center
of the ellipse by the projective camera transformation. This error resulting
from using a point that is not the true center of the pupil directly affects
eye tracking accuracy. We investigate the underlying geometric problem of
determining the center of a circular object based on its projective image. The
main idea is to exploit two concentric circles -- in the application scenario
these are the pupil and the iris. We show that it is possible to computed the
center and the ratio of the radii from the mapped concentric circles with a
direct method that is fast and robust in practice. We evaluate our method on
synthetically generated data and find that it improves systematically over
using the center of the fitted ellipse. Apart from applications of eye tracking
we estimate that our approach will be useful in other tracking applications.
Bayesian Online Prediction of Change Points
Authors: Diego Agudelo-España, Sebastian Gomez-Gonzalez, Stefan Bauer, Bernhard Schölkopf, Jan Peters
Date: 2019-02-12 00:00:00
Tasks: Bayesian Inference, Change Point Detection, Prediction

Online detection of instantaneous changes in the generative process of a data sequence generally focuses on retrospective inference of such change points without considering their future occurrences. We extend the Bayesian Online Change Point Detection algorithm to also infer the number of time steps until the next change point (i.e., the residual time). This enables to handle observation models which depend on the total segment duration, which is useful to model data sequences with temporal scaling. The resulting inference algorithm for segment detection can be deployed in an online fashion, and we illustrate applications to synthetic and to two medical real-world data sets.
A Probabilistic Framework to Node-level Anomaly Detection in Communication Networks
Authors: Batiste Le Bars, Argyris Kalogeratos
Date: 2019-02-12 00:00:00
Tasks: Anomaly Detection

In this paper we consider the task of detecting abnormal communication volume
occurring at node-level in communication networks. The signal of the
communication activity is modeled by means of a clique stream: each occurring
communication event is instantaneous and activates an undirected subgraph
spanning over a set of equally participating nodes. We present a probabilistic
framework to model and assess the communication volume observed at any single
node. Specifically, we employ non-parametric regression to learn the
probability that a node takes part in a certain event knowing the set of other
nodes that are involved. On the top of that, we present a concentration
inequality around the estimated volume of events in which a node could
participate, which in turn allows us to build an efficient and interpretable
anomaly scoring function. Finally, the superior performance of the proposed
approach is empirically demonstrated in real-world sensor network data, as well
as using synthetic communication activity that is in accordance with that
latter setting.
Binary Stochastic Filtering: a Method for Neural Network Size Minimization and Supervised Feature Selection
Authors: Andrii Trelin, Ales Prochazka
Date: 2019-02-12 00:00:00
Tasks: feature selection

Binary Stochastic Filtering (BSF), the algorithm for feature selection and neuron pruning is proposed in this work. The method defines filtering layer which penalizes amount of the information involved in the training process. This information could be the input data or output of the previous layer, which directly leads to the feature selection or neuron pruning respectively, producing \textit{ad hoc} subset of features or selecting optimal number of neurons in each layer. Filtering layer stochastically passes or drops features based on individual weights, which are tuned with standard backpropagation algorithm during the training process. Multifold decrease of neural network size has been achieved in the experiments. Besides, the method was able to select minimal number of features, surpassing literature references by the accuracy/dimensionality ratio.
RTbust: Exploiting Temporal Patterns for Botnet Detection on Twitter
Authors: Michele Mazza, Stefano Cresci, Marco Avvenuti, Walter Quattrociocchi, Maurizio Tesconi
Date: 2019-02-12 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

Within OSNs, many of our supposedly online friends may instead be fake
accounts called social bots, part of large groups that purposely re-share
targeted content. Here, we study retweeting behaviors on Twitter, with the
ultimate goal of detecting retweeting social bots. We collect a dataset of 10M
retweets. We design a novel visualization that we leverage to highlight benign
and malicious patterns of retweeting activity. In this way, we uncover a
'normal' retweeting pattern that is peculiar of human-operated accounts, and 3
suspicious patterns related to bot activities. Then, we propose a bot detection
technique that stems from the previous exploration of retweeting behaviors. Our
technique, called Retweet-Buster (RTbust), leverages unsupervised feature
extraction and clustering. An LSTM autoencoder converts the retweet time series
into compact and informative latent feature vectors, which are then clustered
with a hierarchical density-based algorithm. Accounts belonging to large
clusters characterized by malicious retweeting patterns are labeled as bots.
RTbust obtains excellent detection results, with F1 = 0.87, whereas competitors
achieve F1 < 0.76. Finally, we apply RTbust to a large dataset of retweets,
uncovering 2 previously unknown active botnets with hundreds of accounts.
The Cost of Privacy: Optimal Rates of Convergence for Parameter Estimation with Differential Privacy
Authors: T. Tony Cai, Yichen Wang, Linjun Zhang
Date: 2019-02-12 00:00:00
Tasks: parameter estimation, Privacy Preserving, regression

Privacy-preserving data analysis is a rising challenge in contemporary statistics, as the privacy guarantees of statistical methods are often achieved at the expense of accuracy. In this paper, we investigate the tradeoff between statistical accuracy and privacy in mean estimation and linear regression, under both the classical low-dimensional and modern high-dimensional settings. A primary focus is to establish minimax optimality for statistical estimation with the $(\varepsilon,\delta)$-differential privacy constraint. To this end, we find that classical lower bound arguments fail to yield sharp results, and new technical tools are called for. By refining the "tracing adversary" technique for lower bounds in the theoretical computer science literature, we formulate a general lower bound argument for minimax risks with differential privacy constraints, and apply this argument to high-dimensional mean estimation and linear regression problems. We also design computationally efficient algorithms that attain the minimax lower bounds up to a logarithmic factor. In particular, for the high-dimensional linear regression, a novel private iterative hard thresholding pursuit algorithm is proposed, based on a privately truncated version of stochastic gradient descent. The numerical performance of these algorithms is demonstrated by simulation studies and applications to real data containing sensitive information, for which privacy-preserving statistical methods are necessary.
Extended 2D Consensus Hippocampus Segmentation
Authors: Diedre Carmo, Bruna Silva, Clarissa Yasuda, Letícia Rittner, Roberto Lotufo
Date: 2019-02-12 00:00:00
Tasks: GPU, Hippocampus, Segmentation

Hippocampus segmentation plays a key role in diagnosing various brain disorders such as Alzheimer's disease, epilepsy, multiple sclerosis, cancer, depression and others. Nowadays, segmentation is still mainly performed manually by specialists. Segmentation done by experts is considered to be a gold-standard when evaluating automated methods, buts it is a time consuming and arduos task, requiring specialized personnel. In recent years, efforts have been made to achieve reliable automated segmentation. For years the best performing authomatic methods were multi atlas based with around 80-85% Dice coefficient and very time consuming, but machine learning methods are recently rising with promising time and accuracy performance. A method for volumetric hippocampus segmentation is presented, based on the consensus of tri-planar U-Net inspired fully convolutional networks (FCNNs), with some modifications, including residual connections, VGG weight transfers, batch normalization and a patch extraction technique employing data from neighbor patches. A study on the impact of our modifications to the classical U-Net architecture was performed. Our method achieves cutting edge performance in our dataset, with around 96% volumetric Dice accuracy in our test data. In a public validation dataset, HARP, we achieve 87.48% DICE. GPU execution time is in the order of seconds per volume, and source code is publicly available. Also, masks are shown to be similar to other recent state-of-the-art hippocampus segmentation methods in a third dataset, without manual annotations.
A Domain Generalization Perspective on Listwise Context Modeling
Authors: Lin Zhu, Yihong Chen, Bowen He
Date: 2019-02-12 00:00:00
Tasks: Domain Generalization, Information Retrieval, Learning-To-Rank, Retrieval

As one of the most popular techniques for solving the ranking problem in
information retrieval, Learning-to-rank (LETOR) has received a lot of attention
both in academia and industry due to its importance in a wide variety of data
mining applications. However, most of existing LETOR approaches choose to learn
a single global ranking function to handle all queries, and ignore the
substantial differences that exist between queries. In this paper, we propose a
domain generalization strategy to tackle this problem. We propose
Query-Invariant Listwise Context Modeling (QILCM), a novel neural architecture
which eliminates the detrimental influence of inter-query variability by
learning \textit{query-invariant} latent representations, such that the ranking
system could generalize better to unseen queries. We evaluate our techniques on
benchmark datasets, demonstrating that QILCM outperforms previous
state-of-the-art approaches by a substantial margin.
To Ensemble or Not Ensemble: When does End-To-End Training Fail?
Authors: Andrew M. Webb, Charles Reynolds, Wenlin Chen, Henry Reeve, Dan-Andrei Iliescu, Mikel Lujan, Gavin Brown
Date: 2019-02-12 00:00:00
Tasks: Diversity

End-to-End training (E2E) is becoming more and more popular to train complex Deep Network architectures. An interesting question is whether this trend will continue-are there any clear failure cases for E2E training? We study this question in depth, for the specific case of E2E training an ensemble of networks. Our strategy is to blend the gradient smoothly in between two extremes: from independent training of the networks, up to to full E2E training. We find clear failure cases, where over-parameterized models cannot be trained E2E. A surprising result is that the optimum can sometimes lie in between the two, neither an ensemble or an E2E system. The work also uncovers links to Dropout, and raises questions around the nature of ensemble diversity and multi-branch networks.
Learning interpretable continuous-time models of latent stochastic dynamical systems
Authors: Lea Duncker, Gergo Bohner, Julien Boussard, Maneesh Sahani
Date: 2019-02-12 00:00:00

We develop an approach to learn an interpretable semi-parametric model of a
latent continuous-time stochastic dynamical system, assuming noisy
high-dimensional outputs sampled at uneven times. The dynamics are described by
a nonlinear stochastic differential equation (SDE) driven by a Wiener process,
with a drift evolution function drawn from a Gaussian process (GP) conditioned
on a set of learnt fixed points and corresponding local Jacobian matrices. This
form yields a flexible nonparametric model of the dynamics, with a
representation corresponding directly to the interpretable portraits routinely
employed in the study of nonlinear dynamical systems. The learning algorithm
combines inference of continuous latent paths underlying observed data with a
sparse variational description of the dynamical process. We demonstrate our
approach on simulated data from different nonlinear dynamical systems.
Examining Adversarial Learning against Graph-based IoT Malware Detection Systems
Authors: Ahmed Abusnaina, Aminollah Khormali, Hisham Alasmary, Jeman Park, Afsah Anwar, Ulku Meteriz, Aziz Mohaisen
Date: 2019-02-12 00:00:00
Tasks: Adversarial Attack, General Classification, Graph Embedding, Malware Classification, Malware Detection

The main goal of this study is to investigate the robustness of graph-based
Deep Learning (DL) models used for Internet of Things (IoT) malware
classification against Adversarial Learning (AL). We designed two approaches to
craft adversarial IoT software, including Off-the-Shelf Adversarial Attack
(OSAA) methods, using six different AL attack approaches, and Graph Embedding
and Augmentation (GEA). The GEA approach aims to preserve the functionality and
practicality of the generated adversarial sample through a careful embedding of
a benign sample to a malicious one. Our evaluations demonstrate that OSAAs are
able to achieve a misclassification rate (MR) of 100%. Moreover, we observed
that the GEA approach is able to misclassify all IoT malware samples as benign.
TensorSCONE: A Secure TensorFlow Framework using Intel SGX
Authors: Roland Kunkel, Do Le Quoc, Franz Gregor, Sergei Arnautov, Pramod Bhatotia, Christof Fetzer
Date: 2019-02-12 00:00:00
Tasks: BIG-bench Machine Learning, Cloud Computing

Machine learning has become a critical component of modern data-driven online
services. Typically, the training phase of machine learning techniques requires
to process large-scale datasets which may contain private and sensitive
information of customers. This imposes significant security risks since modern
online services rely on cloud computing to store and process the sensitive
data. In the untrusted computing infrastructure, security is becoming a
paramount concern since the customers need to trust the thirdparty cloud
provider. Unfortunately, this trust has been violated multiple times in the
past. To overcome the potential security risks in the cloud, we answer the
following research question: how to enable secure executions of machine
learning computations in the untrusted infrastructure? To achieve this goal, we
propose a hardware-assisted approach based on Trusted Execution Environments
(TEEs), specifically Intel SGX, to enable secure execution of the machine
learning computations over the private and sensitive datasets. More
specifically, we propose a generic and secure machine learning framework based
on Tensorflow, which enables secure execution of existing applications on the
commodity untrusted infrastructure. In particular, we have built our system
called TensorSCONE from ground-up by integrating TensorFlow with SCONE, a
shielded execution framework based on Intel SGX. The main challenge of this
work is to overcome the architectural limitations of Intel SGX in the context
of building a secure TensorFlow system. Our evaluation shows that we achieve
reasonable performance overheads while providing strong security properties
with low TCB.
Verification Code Recognition Based on Active and Deep Learning
Authors: Dongliang Xu, Bailing Wang, XiaoJiang Du, Xiaoyan Zhu, zhitao Guan, Xiaoyan Yu, Jingyu Liu
Date: 2019-02-12 00:00:00
Tasks: Deep Learning

A verification code is an automated test method used to distinguish between
humans and computers. Humans can easily identify verification codes, whereas
machines cannot. With the development of convolutional neural networks,
automatically recognizing a verification code is now possible for machines.
However, the advantages of convolutional neural networks depend on the data
used by the training classifier, particularly the size of the training set.
Therefore, identifying a verification code using a convolutional neural network
is difficult when training data are insufficient. This study proposes an active
and deep learning strategy to obtain new training data on a special
verification code set without manual intervention. A feature learning model for
a scene with less training data is presented in this work, and the verification
code is identified by the designed convolutional neural network. Experiments
show that the method can considerably improve the recognition accuracy of a
neural network when the amount of initial training data is small.
Cross-Modal Music Retrieval and Applications: An Overview of Key Methodologies
Authors: Meinard Müller, Andreas Arzt, Stefan Balke, Matthias Dorfer, Gerhard Widmer
Date: 2019-02-12 00:00:00
Tasks: Cross-Modal Retrieval, Retrieval

There has been a rapid growth of digitally available music data, including
audio recordings, digitized images of sheet music, album covers and liner
notes, and video clips. This huge amount of data calls for retrieval strategies
that allow users to explore large music collections in a convenient way. More
precisely, there is a need for cross-modal retrieval algorithms that, given a
query in one modality (e.g., a short audio excerpt), find corresponding
information and entities in other modalities (e.g., the name of the piece and
the sheet music). This goes beyond exact audio identification and subsequent
retrieval of metainformation as performed by commercial applications like
Shazam [1].
Manifestation of Image Contrast in Deep Networks
Authors: Arash Akbarinia, Karl R. Gegenfurtner
Date: 2019-02-12 00:00:00

Contrast is subject to dramatic changes across the visual field, depending on
the source of light and scene configurations. Hence, the human visual system
has evolved to be more sensitive to contrast than absolute luminance. This
feature is equally desired for machine vision: the ability to recognise
patterns even when aspects of them are transformed due to variation in local
and global contrast. In this work, we thoroughly investigate the impact of
image contrast on prominent deep convolutional networks, both during the
training and testing phase. The results of conducted experiments testify to an
evident deterioration in the accuracy of all state-of-the-art networks at
low-contrast images. We demonstrate that "contrast-augmentation" is a
sufficient condition to endow a network with invariance to contrast. This
practice shows no negative side effects, quite the contrary, it might allow a
model to refrain from other illuminance related over-fittings. This ability can
also be achieved by a short fine-tuning procedure, which opens new lines of
investigation on mechanisms involved in two networks whose weights are over
99.9% correlated, yet astonishingly produce utterly different outcomes. Our
further analysis suggests that the optimisation algorithm is an influential
factor, however with a significantly lower effect; and while the choice of an
architecture manifests a negligible impact on this phenomenon, the first layers
appear to be more critical.
An adaptive stochastic optimization algorithm for resource allocation
Authors: Xavier Fontaine, Shie Mannor, Vianney Perchet
Date: 2019-02-12 00:00:00
Tasks: Stochastic Optimization

We consider the classical problem of sequential resource allocation where a decision maker must repeatedly divide a budget between several resources, each with diminishing returns. This can be recast as a specific stochastic optimization problem where the objective is to maximize the cumulative reward, or equivalently to minimize the regret. We construct an algorithm that is {\em adaptive} to the complexity of the problem, expressed in term of the regularity of the returns of the resources, measured by the exponent in the {\L}ojasiewicz inequality (or by their universal concavity parameter). Our parameter-independent algorithm recovers the optimal rates for strongly-concave functions and the classical fast rates of multi-armed bandit (for linear reward functions). Moreover, the algorithm improves existing results on stochastic optimization in this regret minimization setting for intermediate cases.
The effect of scene context on weakly supervised semantic segmentation
Authors: Mohammad Kamalzare, Reza Kahani, Alireza Talebpour, Ahmad Mahmoudi-Aznaveh
Date: 2019-02-12 00:00:00
Tasks: Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Image semantic segmentation is parsing image into several partitions in such a way that each region of which involves a semantic concept. In a weakly supervised manner, since only image-level labels are available, discriminating objects from the background is challenging, and in some cases, much more difficult. More specifically, some objects which are commonly seen in one specific scene (e.g. 'train' typically is seen on 'railroad track') are much more likely to be confused. In this paper, we propose a method to add the target-specific scenes in order to overcome the aforementioned problem. Actually, we propose a scene recommender which suggests to add some specific scene contexts to the target dataset in order to train the model more accurately. It is notable that this idea could be a complementary part of the baselines of many other methods. The experiments validate the effectiveness of the proposed method for the objects for which the scene context is added.