pwc-archive/papers-with-abstracts huggingface dataset:
Security Theater: On the Vulnerability of Classifiers to Exploratory Attacks
Authors: Tegjyot Singh Sethi, Mehmed Kantardzic, Joung Woo Ryu
Date: 2018-03-24 00:00:00
Tasks: General Classification

The increasing scale and sophistication of cyberattacks has led to the
adoption of machine learning based classification techniques, at the core of
cybersecurity systems. These techniques promise scale and accuracy, which
traditional rule or signature based methods cannot. However, classifiers
operating in adversarial domains are vulnerable to evasion attacks by an
adversary, who is capable of learning the behavior of the system by employing
intelligently crafted probes. Classification accuracy in such domains provides
a false sense of security, as detection can easily be evaded by carefully
perturbing the input samples. In this paper, a generic data driven framework is
presented, to analyze the vulnerability of classification systems to black box
probing based attacks. The framework uses an exploration exploitation based
strategy, to understand an adversary's point of view of the attack defense
cycle. The adversary assumes a black box model of the defender's classifier and
can launch indiscriminate attacks on it, without information of the defender's
model type, training data or the domain of application. Experimental evaluation
on 10 real world datasets demonstrates that even models having high perceived
accuracy (>90%), by a defender, can be effectively circumvented with a high
evasion rate (>95%, on average). The detailed attack algorithms, adversarial
model and empirical evaluation, serve.
A Dynamic-Adversarial Mining Approach to the Security of Machine Learning
Authors: Tegjyot Singh Sethi, Mehmed Kantardzic, Lingyu Lyua, Jiashun Chen
Date: 2018-03-24 00:00:00
Tasks: BIG-bench Machine Learning, Drift Detection, Feature Importance, One-class classifier

Operating in a dynamic real world environment requires a forward thinking and
adversarial aware design for classifiers, beyond fitting the model to the
training data. In such scenarios, it is necessary to make classifiers - a)
harder to evade, b) easier to detect changes in the data distribution over
time, and c) be able to retrain and recover from model degradation. While most
works in the security of machine learning has concentrated on the evasion
resistance (a) problem, there is little work in the areas of reacting to
attacks (b and c). Additionally, while streaming data research concentrates on
the ability to react to changes to the data distribution, they often take an
adversarial agnostic view of the security problem. This makes them vulnerable
to adversarial activity, which is aimed towards evading the concept drift
detection mechanism itself. In this paper, we analyze the security of machine
learning, from a dynamic and adversarial aware perspective. The existing
techniques of Restrictive one class classifier models, Complex learning models
and Randomization based ensembles, are shown to be myopic as they approach
security as a static task. These methodologies are ill suited for a dynamic
environment, as they leak excessive information to an adversary, who can
subsequently launch attacks which are indistinguishable from the benign data.
Based on empirical vulnerability analysis against a sophisticated adversary, a
novel feature importance hiding approach for classifier design, is proposed.
The proposed design ensures that future attacks on classifiers can be detected
and recovered from. The proposed work presents motivation, by serving as a
blueprint, for future work in the area of Dynamic-Adversarial mining, which
combines lessons learned from Streaming data mining, Adversarial learning and
Cybersecurity.
Handling Adversarial Concept Drift in Streaming Data
Authors: Tegjyot Singh Sethi, Mehmed Kantardzic
Date: 2018-03-24 00:00:00
Tasks: Active Learning, Drift Detection

Classifiers operating in a dynamic, real world environment, are vulnerable to
adversarial activity, which causes the data distribution to change over time.
These changes are traditionally referred to as concept drift, and several
approaches have been developed in literature to deal with the problem of drift
handling and detection. However, most concept drift handling techniques,
approach it as a domain independent task, to make them applicable to a wide
gamut of reactive systems. These techniques were developed from an adversarial
agnostic perspective, where they are naive and assume that drift is a benign
change, which can be fixed by updating the model. However, this is not the case
when an active adversary is trying to evade the deployed classification system.
In such an environment, the properties of concept drift are unique, as the
drift is intended to degrade the system and at the same time designed to avoid
detection by traditional concept drift detection techniques. This special
category of drift is termed as adversarial drift, and this paper analyzes its
characteristics and impact, in a streaming environment. A novel framework for
dealing with adversarial concept drift is proposed, called the Predict-Detect
streaming framework. Experimental evaluation of the framework, on generated
adversarial drifting data streams, demonstrates that this framework is able to
provide reliable unsupervised indication of drift, and is able to recover from
drifts swiftly. While traditional partially labeled concept drift detection
methodologies fail to detect adversarial drifts, the proposed framework is able
to detect such drifts and operates with <6% labeled data, on average. Also, the
framework provides benefits for active learning over imbalanced data streams,
by innately providing for feature space honeypots, where minority class
adversarial samples may be captured.
An Overview of Vulnerabilities of Voice Controlled Systems
Authors: Yuan Gong, Christian Poellabauer
Date: 2018-03-24 00:00:00
Tasks: General Classification

Over the last few years, a rapidly increasing number of Internet-of-Things
(IoT) systems that adopt voice as the primary user input have emerged. These
systems have been shown to be vulnerable to various types of voice spoofing
attacks. However, how exactly these techniques differ or relate to each other
has not been extensively studied. In this paper, we provide a survey of recent
attack and defense techniques for voice controlled systems and propose a
classification of these techniques. We also discuss the need for a universal
defense strategy that protects a system from various types of attacks.
Context-Aware Single-Shot Detector
Authors: Wei Xiang, Dong-Qing Zhang, Heather Yu, Vassilis Athitsos
Date: 2017-07-27 00:00:00
Tasks: object-detection, Object Detection

SSD is one of the state-of-the-art object detection algorithms, and it
combines high detection accuracy with real-time speed. However, it is widely
recognized that SSD is less accurate in detecting small objects compared to
large objects, because it ignores the context from outside the proposal boxes.
In this paper, we present CSSD--a shorthand for context-aware single-shot
multibox object detector. CSSD is built on top of SSD, with additional layers
modeling multi-scale contexts. We describe two variants of CSSD, which differ
in their context layers, using dilated convolution layers (DiCSSD) and
deconvolution layers (DeCSSD) respectively. The experimental results show that
the multi-scale context modeling significantly improves the detection accuracy.
In addition, we study the relationship between effective receptive fields
(ERFs) and the theoretical receptive fields (TRFs), particularly on a VGGNet.
The empirical results further strengthen our conclusion that SSD coupled with
context layers achieves better detection results especially for small objects
($+3.2\% {\rm AP}_{@0.5}$ on MS-COCO compared to the newest SSD), while
maintaining comparable runtime performance.
Fast variational Bayes for heavy-tailed PLDA applied to i-vectors and x-vectors
Authors: Anna Silnova, Niko Brummer, Daniel Garcia-Romero, David Snyder, Lukas Burget
Date: 2018-03-24 00:00:00

The standard state-of-the-art backend for text-independent speaker
recognizers that use i-vectors or x-vectors, is Gaussian PLDA (G-PLDA),
assisted by a Gaussianization step involving length normalization. G-PLDA can
be trained with both generative or discriminative methods. It has long been
known that heavy-tailed PLDA (HT-PLDA), applied without length normalization,
gives similar accuracy, but at considerable extra computational cost. We have
recently introduced a fast scoring algorithm for a discriminatively trained
HT-PLDA backend. This paper extends that work by introducing a fast,
variational Bayes, generative training algorithm. We compare old and new
backends, with and without length-normalization, with i-vectors and x-vectors,
on SRE'10, SRE'16 and SITW.
Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models
Authors: Hugh Salimbeni, Stefanos Eleftheriadis, James Hensman
Date: 2018-03-24 00:00:00
Tasks: Variational Inference

The natural gradient method has been used effectively in conjugate Gaussian
process models, but the non-conjugate case has been largely unexplored. We
examine how natural gradients can be used in non-conjugate stochastic settings,
together with hyperparameter learning. We conclude that the natural gradient
can significantly improve performance in terms of wall-clock time. For
ill-conditioned posteriors the benefit of the natural gradient method is
especially pronounced, and we demonstrate a practical setting where ordinary
gradients are unusable. We show how natural gradients can be computed
efficiently and automatically in any parameterization, using automatic
differentiation. Our code is integrated into the GPflow package.
Universal Denoising Networks : A Novel CNN Architecture for Image Denoising
Authors: Stamatios Lefkimmiatis
Date: 2017-11-21 00:00:00
Proceeding: CVPR 2018 6
Tasks: Color Image Denoising, Deblurring, Demosaicking, Denoising, Image Denoising

We design a novel network architecture for learning discriminative image
models that are employed to efficiently tackle the problem of grayscale and
color image denoising. Based on the proposed architecture, we introduce two
different variants. The first network involves convolutional layers as a core
component, while the second one relies instead on non-local filtering layers
and thus it is able to exploit the inherent non-local self-similarity property
of natural images. As opposed to most of the existing deep network approaches,
which require the training of a specific model for each considered noise level,
the proposed models are able to handle a wide range of noise levels using a
single set of learned parameters, while they are very robust when the noise
degrading the latent image does not match the statistics of the noise used
during training. The latter argument is supported by results that we report on
publicly available images corrupted by unknown noise and which we compare
against solutions obtained by competing methods. At the same time the
introduced networks achieve excellent results under additive white Gaussian
noise (AWGN), which are comparable to those of the current state-of-the-art
network, while they depend on a more shallow architecture with the number of
trained parameters being one order of magnitude smaller. These properties make
the proposed networks ideal candidates to serve as sub-solvers on restoration
methods that deal with general inverse imaging problems such as deblurring,
demosaicking, superresolution, etc.
Posterior Concentration for Sparse Deep Learning
Authors: Nicholas Polson, Veronika Rockova
Date: 2018-03-24 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Deep Learning

Spike-and-Slab Deep Learning (SS-DL) is a fully Bayesian alternative to
Dropout for improving generalizability of deep ReLU networks. This new type of
regularization enables provable recovery of smooth input-output maps with
unknown levels of smoothness. Indeed, we show that the posterior distribution
concentrates at the near minimax rate for $\alpha$-H\"older smooth maps,
performing as well as if we knew the smoothness level $\alpha$ ahead of time.
Our result sheds light on architecture design for deep neural networks, namely
the choice of depth, width and sparsity level. These network attributes
typically depend on unknown smoothness in order to be optimal. We obviate this
constraint with the fully Bayes construction. As an aside, we show that SS-DL
does not overfit in the sense that the posterior concentrates on smaller
networks with fewer (up to the optimal number of) nodes and links. Our results
provide new theoretical justifications for deep ReLU networks from a Bayesian
point of view.
Characterizing Diseases and disorders in Gay Users' tweets
Authors: Frank Webb, Amir Karami, Vanessa Kitzie
Date: 2018-03-24 00:00:00

A lack of information exists about the health issues of lesbian, gay,
bisexual, transgender, and queer (LGBTQ) people who are often excluded from
national demographic assessments, health studies, and clinical trials. As a
result, medical experts and researchers lack a holistic understanding of the
health disparities facing these populations. Fortunately, publicly available
social media data such as Twitter data can be utilized to support the decisions
of public health policy makers and managers with respect to LGBTQ people. This
research employs a computational approach to collect tweets from gay users on
health-related topics and model these topics. To determine the nature of
health-related information shared by men who have sex with men on Twitter, we
collected thousands of tweets from 177 active users. We sampled these tweets
using a framework that can be applied to other LGBTQ sub-populations in future
research. We found 11 diseases in 7 categories based on ICD 10 that are in line
with the published studies and official reports.
Learning Monocular 3D Human Pose Estimation from Multi-view Images
Authors: Helge Rhodin, Jörg Spörri, Isinsu Katircioglu, Victor Constantin, Frédéric Meyer, Erich Müller, Mathieu Salzmann, Pascal Fua
Date: 2018-03-13 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Human Pose Estimation, Monocular 3D Human Pose Estimation, Pose Estimation

Accurate 3D human pose estimation from single images is possible with
sophisticated deep-net architectures that have been trained on very large
datasets. However, this still leaves open the problem of capturing motions for
which no such database exists. Manual annotation is tedious, slow, and
error-prone. In this paper, we propose to replace most of the annotations by
the use of multiple views, at training time only. Specifically, we train the
system to predict the same pose in all views. Such a consistency constraint is
necessary but not sufficient to predict accurate poses. We therefore complement
it with a supervised loss aiming to predict the correct pose in a small set of
labeled images, and with a regularization term that penalizes drift from
initial predictions. Furthermore, we propose a method to estimate camera pose
jointly with human pose, which lets us utilize multi-view footage where
calibration is difficult, e.g., for pan-tilt or moving handheld cameras. We
demonstrate the effectiveness of our approach on established benchmarks, as
well as on a new Ski dataset with rotating cameras and expert ski motion, for
which annotations are truly hard to obtain.
Social Media Analysis For Organizations: Us Northeastern Public And State Libraries Case Study
Authors: Matthew Collins, Amir Karami
Date: 2018-03-24 00:00:00
Tasks: Public Relations

Social networking sites such as Twitter have provided a great opportunity for
organizations such as public libraries to disseminate information for public
relations purposes. However, there is a need to analyze vast amounts of social
media data. This study presents a computational approach to explore the content
of tweets posted by nine public libraries in the northeastern United States of
America. In December 2017, this study extracted more than 19,000 tweets from
the Twitter accounts of seven state libraries and two urban public libraries.
Computational methods were applied to collect the tweets and discover
meaningful themes. This paper shows how the libraries have used Twitter to
represent their services and provides a starting point for different
organizations to evaluate the themes of their public tweets.
The Rapidly Changing Landscape of Conversational Agents
Authors: Vinayak Mathur, Arpit Singh
Date: 2018-03-22 00:00:00
Tasks: Generative Adversarial Network, Reinforcement Learning

Conversational agents have become ubiquitous, ranging from goal-oriented
systems for helping with reservations to chit-chat models found in modern
virtual assistants. In this survey paper, we explore this fascinating field. We
look at some of the pioneering work that defined the field and gradually move
to the current state-of-the-art models. We look at statistical, neural,
generative adversarial network based and reinforcement learning based
approaches and how they evolved. Along the way we discuss various challenges
that the field faces, lack of context in utterances, not having a good
quantitative metric to compare models, lack of trust in agents because they do
not have a consistent persona etc. We structure this paper in a way that
answers these pertinent questions and discusses competing approaches to solve
them.
Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks
Authors: Zhen-Hua Feng, Josef Kittler, Muhammad Awais, Patrik Huber, Xiao-Jun Wu
Date: 2017-11-17 00:00:00
Proceeding: CVPR 2018 6
Tasks: Data Augmentation, Face Alignment

We present a new loss function, namely Wing loss, for robust facial landmark
localisation with Convolutional Neural Networks (CNNs). We first compare and
analyse different loss functions including L2, L1 and smooth L1. The analysis
of these loss functions suggests that, for the training of a CNN-based
localisation model, more attention should be paid to small and medium range
errors. To this end, we design a piece-wise loss function. The new loss
amplifies the impact of errors from the interval (-w, w) by switching from L1
loss to a modified logarithm function.
  To address the problem of under-representation of samples with large
out-of-plane head rotations in the training set, we propose a simple but
effective boosting strategy, referred to as pose-based data balancing. In
particular, we deal with the data imbalance problem by duplicating the minority
training samples and perturbing them by injecting random image rotation,
bounding box translation and other data augmentation approaches. Last, the
proposed approach is extended to create a two-stage framework for robust facial
landmark localisation. The experimental results obtained on AFLW and 300W
demonstrate the merits of the Wing loss function, and prove the superiority of
the proposed method over the state-of-the-art approaches.
Merging and Evolution: Improving Convolutional Neural Networks for Mobile Applications
Authors: Zheng Qin, Zhaoning Zhang, Shiqing Zhang, Hao Yu, Yuxing Peng
Date: 2018-03-24 00:00:00

Compact neural networks are inclined to exploit "sparsely-connected"
convolutions such as depthwise convolution and group convolution for employment
in mobile applications. Compared with standard "fully-connected" convolutions,
these convolutions are more computationally economical. However,
"sparsely-connected" convolutions block the inter-group information exchange,
which induces severe performance degradation. To address this issue, we present
two novel operations named merging and evolution to leverage the inter-group
information. Our key idea is encoding the inter-group information with a narrow
feature map, then combining the generated features with the original network
for better representation. Taking advantage of the proposed operations, we then
introduce the Merging-and-Evolution (ME) module, an architectural unit
specifically designed for compact networks. Finally, we propose a family of
compact neural networks called MENet based on ME modules. Extensive experiments
on ILSVRC 2012 dataset and PASCAL VOC 2007 dataset demonstrate that MENet
consistently outperforms other state-of-the-art compact networks under
different computational budgets. For instance, under the computational budget
of 140 MFLOPs, MENet surpasses ShuffleNet by 1% and MobileNet by 1.95% on
ILSVRC 2012 top-1 accuracy, while by 2.3% and 4.1% on PASCAL VOC 2007 mAP,
respectively.
Predicting Gaze in Egocentric Video by Learning Task-dependent Attention Transition
Authors: Yifei Huang, Minjie Cai, Zhenqiang Li, Yoichi Sato
Date: 2018-03-24 00:00:00
Proceeding: ECCV 2018 9
Tasks: Gaze Prediction, Prediction, Saliency Prediction

We present a new computational model for gaze prediction in egocentric videos
by exploring patterns in temporal shift of gaze fixations (attention
transition) that are dependent on egocentric manipulation tasks. Our assumption
is that the high-level context of how a task is completed in a certain way has
a strong influence on attention transition and should be modeled for gaze
prediction in natural dynamic scenes. Specifically, we propose a hybrid model
based on deep neural networks which integrates task-dependent attention
transition with bottom-up saliency prediction. In particular, the
task-dependent attention transition is learned with a recurrent neural network
to exploit the temporal context of gaze fixations, e.g. looking at a cup after
moving gaze away from a grasped bottle. Experiments on public egocentric
activity datasets show that our model significantly outperforms
state-of-the-art gaze prediction methods and is able to learn meaningful
transition of human attention.
Equation Embeddings
Authors: Kriste Krstovski, David M. Blei
Date: 2018-03-24 00:00:00
Tasks: Articles, Semantic Similarity, Semantic Textual Similarity

We present an unsupervised approach for discovering semantic representations
of mathematical equations. Equations are challenging to analyze because each is
unique, or nearly unique. Our method, which we call equation embeddings, finds
good representations of equations by using the representations of their
surrounding words. We used equation embeddings to analyze four collections of
scientific articles from the arXiv, covering four computer science domains
(NLP, IR, AI, and ML) and $\sim$98.5k equations. Quantitatively, we found that
equation embeddings provide better models when compared to existing word
embedding approaches. Qualitatively, we found that equation embeddings provide
coherent semantic representations of equations and can capture semantic
similarity to other equations and to words.
Scalable and Effective Deep CCA via Soft Decorrelation
Authors: Xiaobin Chang, Tao Xiang, Timothy M. Hospedales
Date: 2017-07-30 00:00:00
Proceeding: CVPR 2018 6
Tasks: MULTI-VIEW LEARNING

Recently the widely used multi-view learning model, Canonical Correlation
Analysis (CCA) has been generalised to the non-linear setting via deep neural
networks. Existing deep CCA models typically first decorrelate the feature
dimensions of each view before the different views are maximally correlated in
a common latent space. This feature decorrelation is achieved by enforcing an
exact decorrelation constraint; these models are thus computationally expensive
due to the matrix inversion or SVD operations required for exact decorrelation
at each training iteration. Furthermore, the decorrelation step is often
separated from the gradient descent based optimisation, resulting in
sub-optimal solutions. We propose a novel deep CCA model Soft CCA to overcome
these problems. Specifically, exact decorrelation is replaced by soft
decorrelation via a mini-batch based Stochastic Decorrelation Loss (SDL) to be
optimised jointly with the other training objectives. Extensive experiments
show that the proposed soft CCA is more effective and efficient than existing
deep CCA models. In addition, our SDL loss can be applied to other deep models
beyond multi-view learning, and obtains superior performance compared to
existing decorrelation losses.