pwc-archive/papers-with-abstracts huggingface dataset:
Adversarial Samples on Android Malware Detection Systems for IoT Systems
Authors: Xiaolei Liu, Xiaojiang Du, Xiaosong Zhang, Qingxin Zhu, Mohsen Guizani
Date: 2019-02-12 00:00:00
Tasks: Android Malware Detection, Malware Detection

Many IoT(Internet of Things) systems run Android systems or Android-like
systems. With the continuous development of machine learning algorithms, the
learning-based Android malware detection system for IoT devices has gradually
increased. However, these learning-based detection models are often vulnerable
to adversarial samples. An automated testing framework is needed to help these
learning-based malware detection systems for IoT devices perform security
analysis. The current methods of generating adversarial samples mostly require
training parameters of models and most of the methods are aimed at image data.
To solve this problem, we propose a \textbf{t}esting framework for
\textbf{l}earning-based \textbf{A}ndroid \textbf{m}alware \textbf{d}etection
systems(TLAMD) for IoT Devices. The key challenge is how to construct a
suitable fitness function to generate an effective adversarial sample without
affecting the features of the application. By introducing genetic algorithms
and some technical improvements, our test framework can generate adversarial
samples for the IoT Android Application with a success rate of nearly 100\% and
can perform black-box testing on the system.
RespNet: A deep learning model for extraction of respiration from photoplethysmogram
Authors: Vignesh Ravichandran, Balamurali Murugesan, Vaishali Balakarthikeyan, Sharath M. Shankaranarayana, Keerthi Ram, Preejith S. P, Jayaraj Joseph, Mohanasankar Sivaprakasam
Date: 2019-02-12 00:00:00

Respiratory ailments afflict a wide range of people and manifests itself
through conditions like asthma and sleep apnea. Continuous monitoring of
chronic respiratory ailments is seldom used outside the intensive care ward due
to the large size and cost of the monitoring system. While Electrocardiogram
(ECG) based respiration extraction is a validated approach, its adoption is
limited by access to a suitable continuous ECG monitor. Recently, due to the
widespread adoption of wearable smartwatches with in-built Photoplethysmogram
(PPG) sensor, it is being considered as a viable candidate for continuous and
unobtrusive respiration monitoring. Research in this domain, however, has been
predominantly focussed on estimating respiration rate from PPG. In this work, a
novel end-to-end deep learning network called RespNet is proposed to perform
the task of extracting the respiration signal from a given input PPG as opposed
to extracting respiration rate. The proposed network was trained and tested on
two different datasets utilizing different modalities of reference respiration
signal recordings. Also, the similarity and performance of the proposed network
against two conventional signal processing approaches for extracting
respiration signal were studied. The proposed method was tested on two
independent datasets with a Mean Squared Error of 0.262 and 0.145. The
Cross-Correlation coefficient of the respective datasets were found to be 0.933
and 0.931. The reported errors and similarity was found to be better than
conventional approaches. The proposed approach would aid clinicians to provide
comprehensive evaluation of sleep-related respiratory conditions and chronic
respiratory ailments while being comfortable and inexpensive for the patient.
Multi-objective Bayesian optimisation with preferences over objectives
Authors: Majid Abdolshah, Alistair Shilton, Santu Rana, Sunil Gupta, Svetha Venkatesh
Date: 2019-02-12 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Bayesian Optimisation

We present a multi-objective Bayesian optimisation algorithm that allows the user to express preference-order constraints on the objectives of the type "objective A is more important than objective B". These preferences are defined based on the stability of the obtained solutions with respect to preferred objective functions. Rather than attempting to find a representative subset of the complete Pareto front, our algorithm selects those Pareto-optimal points that satisfy these constraints. We formulate a new acquisition function based on expected improvement in dominated hypervolume (EHI) to ensure that the subset of Pareto front satisfying the constraints is thoroughly explored. The hypervolume calculation is weighted by the probability of a point satisfying the constraints from a gradient Gaussian Process model. We demonstrate our algorithm on both synthetic and real-world problems.
Effective Network Compression Using Simulation-Guided Iterative Pruning
Authors: Dae-Woong Jeong, Jaehun Kim, Young-Seok Kim, Tae-Ho Kim, Myungsu Chae
Date: 2019-02-12 00:00:00
Tasks: Deep Learning

Existing high-performance deep learning models require very intensive
computing. For this reason, it is difficult to embed a deep learning model into
a system with limited resources. In this paper, we propose the novel idea of
the network compression as a method to solve this limitation. The principle of
this idea is to make iterative pruning more effective and sophisticated by
simulating the reduced network. A simple experiment was conducted to evaluate
the method; the results showed that the proposed method achieved higher
performance than existing methods at the same pruning level.
VC Classes are Adversarially Robustly Learnable, but Only Improperly
Authors: Omar Montasser, Steve Hanneke, Nathan Srebro
Date: 2019-02-12 00:00:00

We study the question of learning an adversarially robust predictor. We show that any hypothesis class $\mathcal{H}$ with finite VC dimension is robustly PAC learnable with an improper learning rule. The requirement of being improper is necessary as we exhibit examples of hypothesis classes $\mathcal{H}$ with finite VC dimension that are not robustly PAC learnable with any proper learning rule.
MaCow: Masked Convolutional Generative Flow
Authors: Xuezhe Ma, Xiang Kong, Shanghang Zhang, Eduard Hovy
Date: 2019-02-12 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Computational Efficiency, Density Estimation, Image Generation

Flow-based generative models, conceptually attractive due to tractability of both the exact log-likelihood computation and latent-variable inference, and efficiency of both training and sampling, has led to a number of impressive empirical successes and spawned many advanced variants and theoretical investigations. Despite their computational efficiency, the density estimation performance of flow-based generative models significantly falls behind those of state-of-the-art autoregressive models. In this work, we introduce masked convolutional generative flow (MaCow), a simple yet effective architecture of generative flow using masked convolution. By restricting the local connectivity in a small kernel, MaCow enjoys the properties of fast and stable training, and efficient sampling, while achieving significant improvements over Glow for density estimation on standard image benchmarks, considerably narrowing the gap to autoregressive models.
Brain MRI Segmentation using Rule-Based Hybrid Approach
Authors: Mustansar Fiaz, Kamran Ali, Abdul Rehman, M. Junaid Gul, Soon Ki Jung
Date: 2019-02-12 00:00:00
Tasks: Anatomy, Image Segmentation, Medical Image Segmentation, MRI segmentation, Segmentation, Semantic Segmentation

Medical image segmentation being a substantial component of image processing
plays a significant role to analyze gross anatomy, to locate an infirmity and
to plan the surgical procedures. Segmentation of brain Magnetic Resonance
Imaging (MRI) is of considerable importance for the accurate diagnosis.
However, precise and accurate segmentation of brain MRI is a challenging task.
Here, we present an efficient framework for segmentation of brain MR images.
For this purpose, Gabor transform method is used to compute features of brain
MRI. Then, these features are classified by using four different classifiers
i.e., Incremental Supervised Neural Network (ISNN), K-Nearest Neighbor (KNN),
Probabilistic Neural Network (PNN), and Support Vector Machine (SVM).
Performance of these classifiers is investigated over different images of brain
MRI and the variation in the performance of these classifiers is observed for
different brain tissues. Thus, we proposed a rule-based hybrid approach to
segment brain MRI. Experimental results show that the performance of these
classifiers varies over each tissue MRI and the proposed rule-based hybrid
approach exhibits better segmentation of brain MRI tissues.
Improving learnability of neural networks: adding supplementary axes to disentangle data representation
Authors: Kim Bukweon, Lee Sung Min, Seo Jin Keun
Date: 2019-02-12 00:00:00
Tasks: Generative Adversarial Network, valid

Over-parameterized deep neural networks have proven to be able to learn an
arbitrary dataset with 100$\%$ training accuracy. Because of a risk of
overfitting and computational cost issues, we cannot afford to increase the
number of network nodes if we want achieve better training results for medical
images. Previous deep learning research shows that the training ability of a
neural network improves dramatically (for the same epoch of training) when a
few nodes with supplementary information are added to the network. These few
informative nodes allow the network to learn features that are otherwise
difficult to learn by generating a disentangled data representation. This paper
analyzes how concatenation of additional information as supplementary axes
affects the training of the neural networks. This analysis was conducted for a
simple multilayer perceptron (MLP) classification model with a rectified linear
unit (ReLU) on two-dimensional training data. We compared the networks with and
without concatenation of supplementary information to support our analysis. The
model with concatenation showed more robust and accurate training results
compared to the model without concatenation. We also confirmed that our
findings are valid for deeper convolutional neural networks (CNN) using
ultrasound images and for a conditional generative adversarial network (cGAN)
using the MNIST data.
De-identification without losing faces
Authors: Yuezun Li, Siwei Lyu
Date: 2019-02-12 00:00:00
Tasks: Attribute, De-identification

Training of deep learning models for computer vision requires large image or
video datasets from real world. Often, in collecting such datasets, we need to
protect the privacy of the people captured in the images or videos, while still
preserve the useful attributes such as facial expressions. In this work, we
describe a new face de-identification method that can preserve essential facial
attributes in the faces while concealing the identities. Our method takes
advantage of the recent advances in face attribute transfer models, while
maintaining a high visual quality. Instead of changing factors of the original
faces or synthesizing faces completely, our method use a trained facial
attribute transfer model to map non-identity related facial attributes to the
face of donors, who are a small number (usually 2 to 3) of consented subjects.
Using the donors' faces ensures that the natural appearance of the synthesized
faces, while ensuring the identity of the synthesized faces are changed. On the
other hand, the FATM blends the donors' facial attributes to those of the
original faces to diversify the appearance of the synthesized faces.
Experimental results on several sets of images and videos demonstrate the
effectiveness of our face de-ID algorithm.
Preferences Implicit in the State of the World
Authors: Rohin Shah, Dmitrii Krasheninnikov, Jordan Alexander, Pieter Abbeel, Anca Dragan
Date: 2019-02-12 00:00:00
Proceeding: ICLR 2019 5
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning (RL) agents optimize only the features specified in a
reward function and are indifferent to anything left out inadvertently. This
means that we must not only specify what to do, but also the much larger space
of what not to do. It is easy to forget these preferences, since these
preferences are already satisfied in our environment. This motivates our key
insight: when a robot is deployed in an environment that humans act in, the
state of the environment is already optimized for what humans want. We can
therefore use this implicit preference information from the state to fill in
the blanks. We develop an algorithm based on Maximum Causal Entropy IRL and use
it to evaluate the idea in a suite of proof-of-concept environments designed to
show its properties. We find that information from the initial state can be
used to infer both side effects that should be avoided as well as preferences
for how the environment should be organized. Our code can be found at
https://github.com/HumanCompatibleAI/rlsp.
LS-Tree: Model Interpretation When the Data Are Linguistic
Authors: Jianbo Chen, Michael. I. Jordan
Date: 2019-02-11 00:00:00
Tasks: General Classification, Sentence

We study the problem of interpreting trained classification models in the
setting of linguistic data sets. Leveraging a parse tree, we propose to assign
least-squares based importance scores to each word of an instance by exploiting
syntactic constituency structure. We establish an axiomatic characterization of
these importance scores by relating them to the Banzhaf value in coalitional
game theory. Based on these importance scores, we develop a principled method
for detecting and quantifying interactions between words in a sentence. We
demonstrate that the proposed method can aid in interpretability and
diagnostics for several widely-used language models.
Riemannian joint dimensionality reduction and dictionary learning on symmetric positive definite manifold
Authors: Hiroyuki Kasai, Bamdev Mishra
Date: 2019-02-11 00:00:00
Tasks: Classification, Dictionary Learning, Dimensionality Reduction, General Classification, image-classification, Image Classification, Riemannian optimization

Dictionary leaning (DL) and dimensionality reduction (DR) are powerful tools
to analyze high-dimensional noisy signals. This paper presents a proposal of a
novel Riemannian joint dimensionality reduction and dictionary learning
(R-JDRDL) on symmetric positive definite (SPD) manifolds for classification
tasks. The joint learning considers the interaction between dimensionality
reduction and dictionary learning procedures by connecting them into a unified
framework. We exploit a Riemannian optimization framework for solving DL and DR
problems jointly. Finally, we demonstrate that the proposed R-JDRDL outperforms
existing state-of-the-arts algorithms when used for image classification tasks.
Nearest Neighbor Median Shift Clustering for Binary Data
Authors: Gaël Beck, Tarn Duong, Mustapha Lebbah, Hanane Azzag
Date: 2019-02-11 00:00:00
Tasks: Clustering

We describe in this paper the theory and practice behind a new modal
clustering method for binary data. Our approach (BinNNMS) is based on the
nearest neighbor median shift. The median shift is an extension of the
well-known mean shift, which was designed for continuous data, to handle binary
data. We demonstrate that BinNNMS can discover accurately the location of
clusters in binary data with theoretical and experimental analyses.
Performance Dynamics and Termination Errors in Reinforcement Learning: A Unifying Perspective
Authors: Nikki Lijing Kuang, Clement H. C. Leung
Date: 2019-02-11 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In reinforcement learning, a decision needs to be made at some point as to
whether it is worthwhile to carry on with the learning process or to terminate
it. In many such situations, stochastic elements are often present which govern
the occurrence of rewards, with the sequential occurrences of positive rewards
randomly interleaved with negative rewards. For most practical learners, the
learning is considered useful if the number of positive rewards always exceeds
the negative ones. A situation that often calls for learning termination is
when the number of negative rewards exceeds the number of positive rewards.
However, while this seems reasonable, the error of premature termination,
whereby termination is enacted along with the conclusion of learning failure
despite the positive rewards eventually far outnumber the negative ones, can be
significant. In this paper, using combinatorial analysis we study the error
probability in wrongly terminating a reinforcement learning activity which
undermines the effectiveness of an optimal policy, and we show that the
resultant error can be quite high. Whilst we demonstrate mathematically that
such errors can never be eliminated, we propose some practical mechanisms that
can effectively reduce such errors. Simulation experiments have been carried
out, the results of which are in close agreement with our theoretical findings.
Stochastic Reinforcement Learning
Authors: Nikki Lijing Kuang, Clement H. C. Leung, Vienne W. K. Sung
Date: 2019-02-11 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In reinforcement learning episodes, the rewards and punishments are often
non-deterministic, and there are invariably stochastic elements governing the
underlying situation. Such stochastic elements are often numerous and cannot be
known in advance, and they have a tendency to obscure the underlying rewards
and punishments patterns. Indeed, if stochastic elements were absent, the same
outcome would occur every time and the learning problems involved could be
greatly simplified. In addition, in most practical situations, the cost of an
observation to receive either a reward or punishment can be significant, and
one would wish to arrive at the correct learning conclusion by incurring
minimum cost. In this paper, we present a stochastic approach to reinforcement
learning which explicitly models the variability present in the learning
environment and the cost of observation. Criteria and rules for learning
success are quantitatively analyzed, and probabilities of exceeding the
observation cost bounds are also obtained.
Domain Constraint Approximation based Semi Supervision
Authors: Yifu Wu, Jin Wei, Rigoberto Roche
Date: 2019-02-11 00:00:00

Deep learning for supervised learning has achieved astonishing performance in various machine learning applications. However, annotated data is expensive and rare. In practice, only a small portion of data samples are annotated. Pseudo-ensembling-based approaches have achieved state-of-the-art results in computer vision related tasks. However, it still relies on the quality of an initial model built by labeled data. Less labeled data may degrade model performance a lot. Domain constraint is another way regularize the posterior but has some limitation. In this paper, we proposed a fuzzy domain-constraint-based framework which loses the requirement of traditional constraint learning and enhances the model quality for semi supervision. Simulations results show the effectiveness of our design.
ReStoCNet: Residual Stochastic Binary Convolutional Spiking Neural Network for Memory-Efficient Neuromorphic Computing
Authors: Gopalakrishnan Srinivasan, Kaushik Roy
Date: 2019-02-11 00:00:00
Tasks: Computational Efficiency, Dimensionality Reduction

In this work, we propose ReStoCNet, a residual stochastic multilayer
convolutional Spiking Neural Network (SNN) composed of binary kernels, to
reduce the synaptic memory footprint and enhance the computational efficiency
of SNNs for complex pattern recognition tasks. ReStoCNet consists of an input
layer followed by stacked convolutional layers for hierarchical input feature
extraction, pooling layers for dimensionality reduction, and fully-connected
layer for inference. In addition, we introduce residual connections between the
stacked convolutional layers to improve the hierarchical feature learning
capability of deep SNNs. We propose Spike Timing Dependent Plasticity (STDP)
based probabilistic learning algorithm, referred to as Hybrid-STDP (HB-STDP),
incorporating Hebbian and anti-Hebbian learning mechanisms, to train the binary
kernels forming ReStoCNet in a layer-wise unsupervised manner. We demonstrate
the efficacy of ReStoCNet and the presented HB-STDP based unsupervised training
methodology on the MNIST and CIFAR-10 datasets. We show that residual
connections enable the deeper convolutional layers to self-learn useful
high-level input features and mitigate the accuracy loss observed in deep SNNs
devoid of residual connections. The proposed ReStoCNet offers >20x kernel
memory compression compared to full-precision (32-bit) SNN while yielding high
enough classification accuracy on the chosen pattern recognition tasks.
Learning to Authenticate with Deep Multibiometric Hashing and Neural Network Decoding
Authors: Veeru Talreja, Sobhan Soleymani, Matthew C. Valenti, Nasser M. Nasrabadi
Date: 2019-02-11 00:00:00
Tasks: Binarization, Decoder, Deep Hashing

In this paper, we propose a novel multimodal deep hashing neural decoder
(MDHND) architecture, which integrates a deep hashing framework with a neural
network decoder (NND) to create an effective multibiometric authentication
system. The MDHND consists of two separate modules: a multimodal deep hashing
(MDH) module, which is used for feature-level fusion and binarization of
multiple biometrics, and a neural network decoder (NND) module, which is used
to refine the intermediate binary codes generated by the MDH and compensate for
the difference between enrollment and probe biometrics (variations in pose,
illumination, etc.). Use of NND helps to improve the performance of the overall
multimodal authentication system. The MDHND framework is trained in 3 steps
using joint optimization of the two modules. In Step 1, the MDH parameters are
trained and learned to generate a shared multimodal latent code; in Step 2, the
latent codes from Step 1 are passed through a conventional error-correcting
code (ECC) decoder to generate the ground truth to train a neural network
decoder (NND); in Step 3, the NND decoder is trained using the ground truth
from Step 2 and the MDH and NND are jointly optimized. Experimental results on
a standard multimodal dataset demonstrate the superiority of our method
relative to other current multimodal authentication systems
Synthesizing New Retinal Symptom Images by Multiple Generative Models
Authors: Yi-Chieh Liu, Hao-Hsiang Yang, Chao-Han Huck Yang, Jia-Hong Huang, Meng Tian, Hiromasa Morikawa, Yi-Chang James Tsai, Jesper Tegner
Date: 2019-02-11 00:00:00
Tasks: General Classification, Image Generation

Age-Related Macular Degeneration (AMD) is an asymptomatic retinal disease
which may result in loss of vision. There is limited access to high-quality
relevant retinal images and poor understanding of the features defining
sub-classes of this disease. Motivated by recent advances in machine learning
we specifically explore the potential of generative modeling, using Generative
Adversarial Networks (GANs) and style transferring, to facilitate clinical
diagnosis and disease understanding by feature extraction. We design an
analytic pipeline which first generates synthetic retinal images from clinical
images; a subsequent verification step is applied. In the synthesizing step we
merge GANs (DCGANs and WGANs architectures) and style transferring for the
image generation, whereas the verified step controls the accuracy of the
generated images. We find that the generated images contain sufficient
pathological details to facilitate ophthalmologists' task of disease
classification and in discovery of disease relevant features. In particular,
our system predicts the drusen and geographic atrophy sub-classes of AMD.
Furthermore, the performance using CFP images for GANs outperforms the
classification based on using only the original clinical dataset. Our results
are evaluated using existing classifier of retinal diseases and class activated
maps, supporting the predictive power of the synthetic images and their utility
for feature extraction. Our code examples are available online.