pwc-archive/papers-with-abstracts huggingface dataset:
Machine Learning in Cyber-Security - Problems, Challenges and Data Sets
Authors: Idan Amit, John Matherly, William Hewlett, Zhi Xu, Yinnon Meshi, Yigal Weinberger
Date: 2018-12-19 00:00:00
Tasks: BIG-bench Machine Learning

We present cyber-security problems of high importance. We show that in order
to solve these cyber-security problems, one must cope with certain machine
learning challenges. We provide novel data sets representing the problems in
order to enable the academic community to investigate the problems and suggest
methods to cope with the challenges. We also present a method to generate
labels via pivoting, providing a solution to common problems of lack of labels
in cyber-security.
Physical Attribute Prediction Using Deep Residual Neural Networks
Authors: Rashidedin Jahandideh, Alireza Tavakoli Targhi, Maryam Tahmasbi
Date: 2018-12-19 00:00:00
Tasks: Attribute, Face Detection, Face Recognition, Physical Attribute Prediction, Prediction

Images taken from the Internet have been used alongside Deep Learning for
many different tasks such as: smile detection, ethnicity, hair style, hair
colour, gender and age prediction. After witnessing these usages, we were
wondering what other attributes can be predicted from facial images available
on the Internet. In this paper we tackle the prediction of physical attributes
from face images using Convolutional Neural Networks trained on our dataset
named FIRW. We crawled around 61, 000 images from the web, then use face
detection to crop faces from these real world images. We choose ResNet-50 as
our base network architecture. This network was pretrained for the task of face
recognition by using the VGG-Face dataset, and we finetune it by using our own
dataset to predict physical attributes. Separate networks are trained for the
prediction of body type, ethnicity, gender, height and weight; our models
achieve the following accuracies for theses tasks, respectively: 84.58%,
87.34%, 97.97%, 70.51%, 63.99%. To validate our choice of ResNet-50 as the base
architecture, we also tackle the famous CelebA dataset. Our models achieve an
averagy accuracy of 91.19% on CelebA, which is comparable to state-of-the-art
approaches.
Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images
Authors: Bruno Lecouat, Ken Chang, Chuan-Sheng Foo, Balagopal Unnikrishnan, James M. Brown, Houssam Zenati, Andrew Beers, Vijay Chandrasekhar, Jayashree Kalpathy-Cramer, Pavitra Krishnaswamy
Date: 2018-12-19 00:00:00
Tasks: Deep Learning, General Classification, Generative Adversarial Network, image-classification, Image Classification, Medical Image Classification

Supervised deep learning algorithms have enabled significant performance
gains in medical image classification tasks. But these methods rely on large
labeled datasets that require resource-intensive expert annotation.
Semi-supervised generative adversarial network (GAN) approaches offer a means
to learn from limited labeled data alongside larger unlabeled datasets, but
have not been applied to discern fine-scale, sparse or localized features that
define medical abnormalities. To overcome these limitations, we propose a
patch-based semi-supervised learning approach and evaluate performance on
classification of diabetic retinopathy from funduscopic images. Our
semi-supervised approach achieves high AUC with just 10-20 labeled training
images, and outperforms the supervised baselines by upto 15% when less than 30%
of the training dataset is labeled. Further, our method implicitly enables
interpretation of the SSL predictions. As this approach enables good accuracy,
resolution and interpretability with lower annotation burden, it sets the
pathway for scalable applications of deep learning in clinical imaging.
Fast and Accurate 3D Medical Image Segmentation with Data-swapping Method
Authors: Haruki Imai, Samuel Matzek, Tung D. Le, Yasushi Negishi, Kiyokuni Kawachiya
Date: 2018-12-19 00:00:00
Tasks: CPU, GPU, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Deep neural network models used for medical image segmentation are large
because they are trained with high-resolution three-dimensional (3D) images.
Graphics processing units (GPUs) are widely used to accelerate the trainings.
However, the memory on a GPU is not large enough to train the models. A popular
approach to tackling this problem is patch-based method, which divides a large
image into small patches and trains the models with these small patches.
However, this method would degrade the segmentation quality if a target object
spans multiple patches. In this paper, we propose a novel approach for 3D
medical image segmentation that utilizes the data-swapping, which swaps out
intermediate data from GPU memory to CPU memory to enlarge the effective GPU
memory size, for training high-resolution 3D medical images without patching.
We carefully tuned parameters in the data-swapping method to obtain the best
training performance for 3D U-Net, a widely used deep neural network model for
medical image segmentation. We applied our tuning to train 3D U-Net with
full-size images of 192 x 192 x 192 voxels in brain tumor dataset. As a result,
communication overhead, which is the most important issue, was reduced by
17.1%. Compared with the patch-based method for patches of 128 x 128 x 128
voxels, our training for full-size images achieved improvement on the mean Dice
score by 4.48% and 5.32 % for detecting whole tumor sub-region and tumor core
sub-region, respectively. The total training time was reduced from 164 hours to
47 hours, resulting in 3.53 times of acceleration.
Matrix Completion under Low-Rank Missing Mechanism
Authors: Xiaojun Mao, Raymond K. W. Wong, Song Xi Chen
Date: 2018-12-19 00:00:00
Tasks: Matrix Completion

Matrix completion is a modern missing data problem where both the missing structure and the underlying parameter are high dimensional. Although missing structure is a key component to any missing data problems, existing matrix completion methods often assume a simple uniform missing mechanism. In this work, we study matrix completion from corrupted data under a novel low-rank missing mechanism. The probability matrix of observation is estimated via a high dimensional low-rank matrix estimation procedure, and further used to complete the target matrix via inverse probabilities weighting. Due to both high dimensional and extreme (i.e., very small) nature of the true probability matrix, the effect of inverse probability weighting requires careful study. We derive optimal asymptotic convergence rates of the proposed estimators for both the observation probabilities and the target matrix.
Fast Botnet Detection From Streaming Logs Using Online Lanczos Method
Authors: Zheng Chen, Xinli Yu, Chi Zhang, Jin Zhang, Cui Lin, Bo Song, Jianliang Gao, Xiaohua Hu, Wei-Shih Yang, Erjia Yan
Date: 2018-12-19 00:00:00

Botnet, a group of coordinated bots, is becoming the main platform of
malicious Internet activities like DDOS, click fraud, web scraping, spam/rumor
distribution, etc. This paper focuses on design and experiment of a new
approach for botnet detection from streaming web server logs, motivated by its
wide applicability, real-time protection capability, ease of use and better
security of sensitive data. Our algorithm is inspired by a Principal Component
Analysis (PCA) to capture correlation in data, and we are first to recognize
and adapt Lanczos method to improve the time complexity of PCA-based botnet
detection from cubic to sub-cubic, which enables us to more accurately and
sensitively detect botnets with sliding time windows rather than fixed time
windows. We contribute a generalized online correlation matrix update formula,
and a new termination condition for Lanczos iteration for our purpose based on
error bound and non-decreasing eigenvalues of symmetric matrices. On our
dataset of an ecommerce website logs, experiments show the time cost of Lanczos
method with different time windows are consistently only 20% to 25% of PCA.
Found in Translation: Learning Robust Joint Representations by Cyclic Translations Between Modalities
Authors: Hai Pham, Paul Pu Liang, Thomas Manzini, Louis-Philippe Morency, Barnabas Poczos
Date: 2018-12-19 00:00:00
Tasks: Machine Translation, Multimodal Sentiment Analysis, Sentiment Analysis, Translation

Multimodal sentiment analysis is a core research area that studies speaker sentiment expressed from the language, visual, and acoustic modalities. The central challenge in multimodal learning involves inferring joint representations that can process and relate information from these modalities. However, existing work learns joint representations by requiring all modalities as input and as a result, the learned representations may be sensitive to noisy or missing modalities at test time. With the recent success of sequence to sequence (Seq2Seq) models in machine translation, there is an opportunity to explore new ways of learning joint representations that may not require all input modalities at test time. In this paper, we propose a method to learn robust joint representations by translating between modalities. Our method is based on the key insight that translation from a source to a target modality provides a method of learning joint representations using only the source modality as input. We augment modality translations with a cycle consistency loss to ensure that our joint representations retain maximal information from all modalities. Once our translation model is trained with paired multimodal data, we only need data from the source modality at test time for final sentiment prediction. This ensures that our model remains robust from perturbations or missing information in the other modalities. We train our model with a coupled translation-prediction objective and it achieves new state-of-the-art results on multimodal sentiment analysis datasets: CMU-MOSI, ICT-MMMO, and YouTube. Additional experiments show that our model learns increasingly discriminative joint representations with more input modalities while maintaining robustness to missing or perturbed modalities.
DTMT: A Novel Deep Transition Architecture for Neural Machine Translation
Authors: Fandong Meng, Jinchao Zhang
Date: 2018-12-19 00:00:00
Tasks: Machine Translation, NMT, Translation

Past years have witnessed rapid developments in Neural Machine Translation (NMT). Most recently, with advanced modeling and training techniques, the RNN-based NMT (RNMT) has shown its potential strength, even compared with the well-known Transformer (self-attentional) model. Although the RNMT model can possess very deep architectures through stacking layers, the transition depth between consecutive hidden states along the sequential axis is still shallow. In this paper, we further enhance the RNN-based NMT through increasing the transition depth between consecutive hidden states and build a novel Deep Transition RNN-based Architecture for Neural Machine Translation, named DTMT. This model enhances the hidden-to-hidden transition with multiple non-linear transformations, as well as maintains a linear transformation path throughout this deep transition by the well-designed linear transformation mechanism to alleviate the gradient vanishing problem. Experiments show that with the specially designed deep transition modules, our DTMT can achieve remarkable improvements on translation quality. Experimental results on Chinese->English translation task show that DTMT can outperform the Transformer model by +2.09 BLEU points and achieve the best results ever reported in the same dataset. On WMT14 English->German and English->French translation tasks, DTMT shows superior quality to the state-of-the-art NMT systems, including the Transformer and the RNMT+.
Unifying Topic, Sentiment & Preference in an HDP-Based Rating Regression Model for Online Reviews
Authors: Zheng Chen, Yong Zhang, Yue Shang, Xiaohua Hu
Date: 2018-12-19 00:00:00
Tasks: Clustering, Collaborative Filtering, Online Review Rating, regression, Sentiment Analysis

This paper proposes a new HDP based online review rating regression model
named Topic-Sentiment-Preference Regression Analysis (TSPRA). TSPRA combines
topics (i.e. product aspects), word sentiment and user preference as regression
factors, and is able to perform topic clustering, review rating prediction,
sentiment analysis and what we invent as "critical aspect" analysis altogether
in one framework. TSPRA extends sentiment approaches by integrating the key
concept "user preference" in collaborative filtering (CF) models into
consideration, while it is distinct from current CF models by decoupling "user
preference" and "sentiment" as independent factors. Our experiments conducted
on 22 Amazon datasets show overwhelming better performance in rating
predication against a state-of-art model FLAME (2015) in terms of error,
Pearson's Correlation and number of inverted pairs. For sentiment analysis, we
compare the derived word sentiments against a public sentiment resource
SenticNet3 and our sentiment estimations clearly make more sense in the context
of online reviews. Last, as a result of the de-correlation of "user preference"
from "sentiment", TSPRA is able to evaluate a new concept "critical aspects",
defined as the product aspects seriously concerned by users but negatively
commented in reviews. Improvement to such "critical aspects" could be most
effective to enhance user experience.
Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, Applications, and Prospects
Authors: Tian-Zhu Xiang, Gui-Song Xia, Liangpei Zhang
Date: 2018-12-19 00:00:00

The past few decades have witnessed the great progress of unmanned aircraft vehicles (UAVs) in civilian fields, especially in photogrammetry and remote sensing. In contrast with the platforms of manned aircraft and satellite, the UAV platform holds many promising characteristics: flexibility, efficiency, high-spatial/temporal resolution, low cost, easy operation, etc., which make it an effective complement to other remote-sensing platforms and a cost-effective means for remote sensing. Considering the popularity and expansion of UAV-based remote sensing in recent years, this paper provides a systematic survey on the recent advances and future prospectives of UAVs in the remote-sensing community. Specifically, the main challenges and key technologies of remote-sensing data processing based on UAVs are discussed and summarized firstly. Then, we provide an overview of the widespread applications of UAVs in remote sensing. Finally, some prospects for future work are discussed. We hope this paper will provide remote-sensing researchers an overall picture of recent UAV-based remote sensing developments and help guide the further research on this topic.
Modular meta-learning in abstract graph networks for combinatorial generalization
Authors: Ferran Alet, Maria Bauza, Alberto Rodriguez, Tomas Lozano-Perez, Leslie P. Kaelbling
Date: 2018-12-19 00:00:00
Tasks: Meta-Learning

Modular meta-learning is a new framework that generalizes to unseen datasets
by combining a small set of neural modules in different ways. In this work we
propose abstract graph networks: using graphs as abstractions of a system's
subparts without a fixed assignment of nodes to system subparts, for which we
would need supervision. We combine this idea with modular meta-learning to get
a flexible framework with combinatorial generalization to new tasks built in.
We then use it to model the pushing of arbitrarily shaped objects from little
or no training data.
CNN based Multi-Instance Multi-Task Learning for Syndrome Differentiation of Diabetic Patients
Authors: Zeyuan Wang, Josiah Poon, Shiding Sun, Simon Poon
Date: 2018-12-19 00:00:00
Tasks: Missing Values, Multi-Task Learning, object-detection, Object Detection, Representation Learning

Syndrome differentiation in Traditional Chinese Medicine (TCM) is the process
of understanding and reasoning body condition, which is the essential step and
premise of effective treatments. However, due to its complexity and lack of
standardization, it is challenging to achieve. In this study, we consider each
patient's record as a one-dimensional image and symptoms as pixels, in which
missing and negative values are represented by zero pixels. The objective is to
find relevant symptoms first and then map them to proper syndromes, that is
similar to the object detection problem in computer vision. Inspired from it,
we employ multi-instance multi-task learning combined with the convolutional
neural network (MIMT-CNN) for syndrome differentiation, which takes region
proposals as input and output image labels directly. The neural network
consists of region proposals generation, convolutional layer, fully connected
layer, and max pooling (multi-instance pooling) layer followed by the sigmoid
function in each syndrome prediction task for image representation learning and
final results generation. On the diabetes dataset, it performs better than all
other baseline methods. Moreover, it shows stability and reliability to
generate results, even on the dataset with small sample size, a large number of
missing values and noises.
Light Weight Color Image Warping with Inter-Channel Information
Authors: Chuangye Zhang, Yan Niu, Tieru Wu, Xi-Ming Li
Date: 2018-12-19 00:00:00
Tasks: Optical Flow Estimation

Image warping is a necessary step in many multimedia applications such as
texture mapping, image-based rendering, panorama stitching, image resizing and
optical flow computation etc. Traditionally, color image warping interpolation
is performed in each color channel independently. In this paper, we show that
the warping quality can be significantly enhanced by exploiting the
cross-channel correlation. We design a warping scheme that integrates
intra-channel interpolation with cross-channel variation at very low
computational cost, which is required for interactive multimedia applications
on mobile devices. The effectiveness and efficiency of our method are validated
by extensive experiments.
Real-Time, Highly Accurate Robotic Grasp Detection using Fully Convolutional Neural Network with Rotation Ensemble Module
Authors: Dongwon Park, Yonghyeok Seo, Se Young Chun
Date: 2018-12-19 00:00:00
Tasks: Face Detection, Robotic Grasping

Rotation invariance has been an important topic in computer vision tasks. Ideally, robot grasp detection should be rotation-invariant. However, rotation-invariance in robotic grasp detection has been only recently studied by using rotation anchor box that are often time-consuming and unreliable for multiple objects. In this paper, we propose a rotation ensemble module (REM) for robotic grasp detection using convolutions that rotates network weights. Our proposed REM was able to outperform current state-of-the-art methods by achieving up to 99.2% (image-wise), 98.6% (object-wise) accuracies on the Cornell dataset with real-time computation (50 frames per second). Our proposed method was also able to yield reliable grasps for multiple objects and up to 93.8% success rate for the real-time robotic grasping task with a 4-axis robot arm for small novel objects that was significantly higher than the baseline methods by 11-56%.
Learning On-Road Visual Control for Self-Driving Vehicles with Auxiliary Tasks
Authors: Yilun Chen, Praveen Palanisamy, Priyantha Mudalige, Katharina Muelling, John M. Dolan
Date: 2018-12-19 00:00:00
Tasks: Optical Flow Estimation, Semantic Segmentation, Sensor Fusion, Visual Navigation

A safe and robust on-road navigation system is a crucial component of
achieving fully automated vehicles. NVIDIA recently proposed an End-to-End
algorithm that can directly learn steering commands from raw pixels of a front
camera by using one convolutional neural network. In this paper, we leverage
auxiliary information aside from raw images and design a novel network
structure, called Auxiliary Task Network (ATN), to help boost the driving
performance while maintaining the advantage of minimal training data and an
End-to-End training method. In this network, we introduce human prior knowledge
into vehicle navigation by transferring features from image recognition tasks.
Image semantic segmentation is applied as an auxiliary task for navigation. We
consider temporal information by introducing an LSTM module and optical flow to
the network. Finally, we combine vehicle kinematics with a sensor fusion step.
We discuss the benefits of our method over state-of-the-art visual navigation
methods both in the Udacity simulation environment and on the real-world
Comma.ai dataset.
Streaming Voice Query Recognition using Causal Convolutional Recurrent Neural Networks
Authors: Raphael Tang, Gefei Yang, Hong Wei, Yajie Mao, Ferhan Ture, Jimmy Lin
Date: 2018-12-19 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Efficient Neural Network, Keyword Spotting, speech-recognition, Speech Recognition, Voice Query Recognition

Voice-enabled commercial products are ubiquitous, typically enabled by
lightweight on-device keyword spotting (KWS) and full automatic speech
recognition (ASR) in the cloud. ASR systems require significant computational
resources in training and for inference, not to mention copious amounts of
annotated speech data. KWS systems, on the other hand, are less
resource-intensive but have limited capabilities. On the Comcast Xfinity X1
entertainment platform, we explore a middle ground between ASR and KWS: We
introduce a novel, resource-efficient neural network for voice query
recognition that is much more accurate than state-of-the-art CNNs for KWS, yet
can be easily trained and deployed with limited resources. On an evaluation
dataset representing the top 200 voice queries, we achieve a low false alarm
rate of 1% and a query error rate of 6%. Our model performs inference 8.24x
faster than the current ASR system.
Generative One-Shot Learning (GOL): A Semi-Parametric Approach to One-Shot Learning in Autonomous Vision
Authors: Sorin Grigorescu
Date: 2018-12-19 00:00:00
Tasks: Autonomous Driving, One-Shot Learning

Highly Autonomous Driving (HAD) systems rely on deep neural networks for the
visual perception of the driving environment. Such networks are trained on
large manually annotated databases. In this work, a semi-parametric approach to
one-shot learning is proposed, with the aim of bypassing the manual annotation
step required for training perceptions systems used in autonomous driving. The
proposed generative framework, coined Generative One-Shot Learning (GOL), takes
as input single one-shot objects, or generic patterns, and a small set of
so-called regularization samples used to drive the generative process. New
synthetic data is generated as Pareto optimal solutions from one-shot objects
using a set of generalization functions built into a generalization generator.
GOL has been evaluated on environment perception challenges encountered in
autonomous vision.
Discriminative analysis of the human cortex using spherical CNNs - a study on Alzheimer's disease diagnosis
Authors: Xinyang Feng, Jie Yang, Andrew F. Laine, Elsa D. Angelini
Date: 2018-12-19 00:00:00
Tasks: General Classification

In neuroimaging studies, the human cortex is commonly modeled as a sphere to
preserve the topological structure of the cortical surface. Cortical
neuroimaging measures hence can be modeled in spherical representation. In this
work, we explore analyzing the human cortex using spherical CNNs in an
Alzheimer's disease (AD) classification task using cortical morphometric
measures derived from structural MRI. Our results show superior performance in
classifying AD versus cognitively normal and in predicting MCI progression
within two years, using structural MRI information only. This work demonstrates
for the first time the potential of the spherical CNNs framework in the
discriminative analysis of the human cortex and could be extended to other
modalities and other neurological diseases.