pwc-archive/papers-with-abstracts huggingface dataset:
Feasibility of Supervised Machine Learning for Cloud Security
Authors: Deval Bhamare, Tara Salman, Mohammed Samaka, Aiman Erbad, Raj Jain
Date: 2018-10-23 00:00:00
Tasks: BIG-bench Machine Learning, Cloud Computing

Cloud computing is gaining significant attention, however, security is the
biggest hurdle in its wide acceptance. Users of cloud services are under
constant fear of data loss, security threats and availability issues. Recently,
learning-based methods for security applications are gaining popularity in the
literature with the advents in machine learning techniques. However, the major
challenge in these methods is obtaining real-time and unbiased datasets. Many
datasets are internal and cannot be shared due to privacy issues or may lack
certain statistical characteristics. As a result of this, researchers prefer to
generate datasets for training and testing purpose in the simulated or closed
experimental environments which may lack comprehensiveness. Machine learning
models trained with such a single dataset generally result in a semantic gap
between results and their application. There is a dearth of research work which
demonstrates the effectiveness of these models across multiple datasets
obtained in different environments. We argue that it is necessary to test the
robustness of the machine learning models, especially in diversified operating
conditions, which are prevalent in cloud scenarios. In this work, we use the
UNSW dataset to train the supervised machine learning models. We then test
these models with ISOT dataset. We present our results and argue that more
research in the field of machine learning is still required for its
applicability to the cloud security.
Automatic Full Compilation of Julia Programs and ML Models to Cloud TPUs
Authors: Keno Fischer, Elliot Saba
Date: 2018-10-23 00:00:00
Tasks: BIG-bench Machine Learning, CPU

Google's Cloud TPUs are a promising new hardware architecture for machine
learning workloads. They have powered many of Google's milestone machine
learning achievements in recent years. Google has now made TPUs available for
general use on their cloud platform and as of very recently has opened them up
further to allow use by non-TensorFlow frontends. We describe a method and
implementation for offloading suitable sections of Julia programs to TPUs via
this new API and the Google XLA compiler. Our method is able to completely fuse
the forward pass of a VGG19 model expressed as a Julia program into a single
TPU executable to be offloaded to the device. Our method composes well with
existing compiler-based automatic differentiation techniques on Julia code, and
we are thus able to also automatically obtain the VGG19 backwards pass and
similarly offload it to the TPU. Targeting TPUs using our compiler, we are able
to evaluate the VGG19 forward pass on a batch of 100 images in 0.23s which
compares favorably to the 52.4s required for the original model on the CPU. Our
implementation is less than 1000 lines of Julia, with no TPU specific changes
made to the core Julia compiler or any other Julia packages.
Deep Neural Network inference with reduced word length
Authors: Lukas Mauch, Bin Yang
Date: 2018-10-23 00:00:00

Deep neural networks (DNN) are powerful models for many pattern recognition
tasks, yet their high computational complexity and memory requirement limit
them to applications on high-performance computing platforms. In this paper, we
propose a new method to evaluate DNNs trained with 32bit floating point
(float32) accuracy using only low precision integer arithmetics in combination
with binary shift and clipping operations. Because hardware implementation of
these operations is much simpler than high precision floating point
calculation, our method can be used for an efficient DNN inference on dedicated
hardware. In experiments on MNIST, we demonstrate that DNNs trained with
float32 can be evaluated using a combination of 2bit integer arithmetics and a
few float32 calculations in each layer or only 3bit integer arithmetics in
combination with binary shift and clipping without significant performance
degradation.
DropFilter: Dropout for Convolutions
Authors: Zhengsu Chen Jianwei Niu Qi Tian
Date: 2018-10-23 00:00:00

Using a large number of parameters , deep neural networks have achieved
remarkable performance on computer vison and natural language processing tasks.
However the networks usually suffer from overfitting by using too much
parameters. Dropout is a widely use method to deal with overfitting. Although
dropout can significantly regularize densely connected layers in neural
networks, it leads to suboptimal results when using for convolutional layers.
To track this problem, we propose DropFilter, a new dropout method for
convolutional layers. DropFilter randomly suppresses the outputs of some
filters. Because it is observed that co-adaptions are more likely to occurs
inter filters rather than intra filters in convolutional layers. Using
DropFilter, we remarkably improve the performance of convolutional networks on
CIFAR and ImageNet.
DCSVM: Fast Multi-class Classification using Support Vector Machines
Authors: Duleep Rathgamage Don, Ionut E. Iacob
Date: 2018-10-23 00:00:00
Tasks: Classification, General Classification, Multi-class Classification

We present DCSVM, an efficient algorithm for multi-class classification using
Support Vector Machines. DCSVM is a divide and conquer algorithm which relies
on data sparsity in high dimensional space and performs a smart partitioning of
the whole training data set into disjoint subsets that are easily separable. A
single prediction performed between two partitions eliminates at once one or
more classes in one partition, leaving only a reduced number of candidate
classes for subsequent steps. The algorithm continues recursively, reducing the
number of classes at each step, until a final binary decision is made between
the last two classes left in the competition. In the best case scenario, our
algorithm makes a final decision between $k$ classes in $O(\log k)$ decision
steps and in the worst case scenario DCSVM makes a final decision in $k-1$
steps, which is not worse than the existent techniques.
Self-Erasing Network for Integral Object Attention
Authors: Qibin Hou, Peng-Tao Jiang, Yunchao Wei, Ming-Ming Cheng
Date: 2018-10-23 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Object, Semantic Segmentation

Recently, adversarial erasing for weakly-supervised object attention has been
deeply studied due to its capability in localizing integral object regions.
However, such a strategy raises one key problem that attention regions will
gradually expand to non-object regions as training iterations continue, which
significantly decreases the quality of the produced attention maps. To tackle
such an issue as well as promote the quality of object attention, we introduce
a simple yet effective Self-Erasing Network (SeeNet) to prohibit attentions
from spreading to unexpected background regions. In particular, SeeNet
leverages two self-erasing strategies to encourage networks to use reliable
object and background cues for learning to attention. In this way, integral
object regions can be effectively highlighted without including much more
background regions. To test the quality of the generated attention maps, we
employ the mined object regions as heuristic cues for learning semantic
segmentation models. Experiments on Pascal VOC well demonstrate the superiority
of our SeeNet over other state-of-the-art methods.
Fruit and Vegetable Identification Using Machine Learning for Retail Applications
Authors: Frida Femling, Adam Olsson, Fernando Alonso-Fernandez
Date: 2018-10-23 00:00:00
Tasks: BIG-bench Machine Learning

This paper describes an approach of creating a system identifying fruit and
vegetables in the retail market using images captured with a video camera
attached to the system. The system helps the customers to label desired fruits
and vegetables with a price according to its weight. The purpose of the system
is to minimize the number of human computer interactions, speed up the
identification process and improve the usability of the graphical user
interface compared to existing manual systems. The hardware of the system is
constituted by a Raspberry Pi, camera, display, load cell and a case. To
classify an object, different convolutional neural networks have been tested
and retrained. To test the usability, a heuristic evaluation has been performed
with several users, concluding that the implemented system is more user
friendly compared to existing systems.
PreCo: A Large-scale Dataset in Preschool Vocabulary for Coreference Resolution
Authors: Hong Chen, Zhenhua Fan, Hao Lu, Alan L. Yuille, Shu Rong
Date: 2018-10-23 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Clustering, coreference-resolution, Coreference Resolution

We introduce PreCo, a large-scale English dataset for coreference resolution.
The dataset is designed to embody the core challenges in coreference, such as
entity representation, by alleviating the challenge of low overlap between
training and test sets and enabling separated analysis of mention detection and
mention clustering. To strengthen the training-test overlap, we collect a large
corpus of about 38K documents and 12.4M words which are mostly from the
vocabulary of English-speaking preschoolers. Experiments show that with higher
training-test overlap, error analysis on PreCo is more efficient than the one
on OntoNotes, a popular existing dataset. Furthermore, we annotate singleton
mentions making it possible for the first time to quantify the influence that a
mention detector makes on coreference resolution performance. The dataset is
freely available at https://preschool-lab.github.io/PreCo/.
Action and intention recognition of pedestrians in urban traffic
Authors: Dimitrios Varytimidis, Fernando Alonso-Fernandez, Boris Duran, Cristofer Englund
Date: 2018-10-23 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Intent Detection, Motion Detection

Action and intention recognition of pedestrians in urban settings are
challenging problems for Advanced Driver Assistance Systems as well as future
autonomous vehicles to maintain smooth and safe traffic. This work investigates
a number of feature extraction methods in combination with several machine
learning algorithms to build knowledge on how to automatically detect the
action and intention of pedestrians in urban traffic. We focus on the motion
and head orientation to predict whether the pedestrian is about to cross the
street or not. The work is based on the Joint Attention for Autonomous Driving
(JAAD) dataset, which contains 346 videoclips of various traffic scenarios
captured with cameras mounted in the windshield of a car. An accuracy of 72%
for head orientation estimation and 85% for motion detection is obtained in our
experiments.
Improving Automated Latent Fingerprint Identification using Extended Minutia Types
Authors: Ram P. Krish, Julian Fierrez, Daniel Ramos, Fernando Alonso-Fernandez, Josef Bigun
Date: 2018-10-23 00:00:00

Latent fingerprints are usually processed with Automated Fingerprint
Identification Systems (AFIS) by law enforcement agencies to narrow down
possible suspects from a criminal database. AFIS do not commonly use all
discriminatory features available in fingerprints but typically use only some
types of features automatically extracted by a feature extraction algorithm. In
this work, we explore ways to improve rank identification accuracies of AFIS
when only a partial latent fingerprint is available. Towards solving this
challenge, we propose a method that exploits extended fingerprint features
(unusual/rare minutiae) not commonly considered in AFIS. This new method can be
combined with any existing minutiae-based matcher. We first compute a
similarity score based on least squares between latent and tenprint minutiae
points, with rare minutiae features as reference points. Then the similarity
score of the reference minutiae-based matcher at hand is modified based on a
fitting error from the least square similarity stage. We use a realistic
forensic fingerprint casework database in our experiments which contains rare
minutiae features obtained from Guardia Civil, the Spanish law enforcement
agency. Experiments are conducted using three minutiae-based matchers as a
reference, namely: NIST-Bozorth3, VeriFinger-SDK and MCC-SDK. We report
significant improvements in the rank identification accuracies when these
minutiae matchers are augmented with our proposed algorithm based on rare
minutiae features.
Expression Recognition Using the Periocular Region: A Feasibility Study
Authors: Fernando Alonso-Fernandez, Josef Bigun, Cristofer Englund
Date: 2018-10-23 00:00:00

This paper investigates the feasibility of using the periocular region for
expression recognition. Most works have tried to solve this by analyzing the
whole face. Periocular is the facial region in the immediate vicinity of the
eye. It has the advantage of being available over a wide range of distances and
under partial face occlusion, thus making it suitable for unconstrained or
uncooperative scenarios. We evaluate five different image descriptors on a
dataset of 1,574 images from 118 subjects. The experimental results show an
average/overall accuracy of 67.0%/78.0% by fusion of several descriptors. While
this accuracy is still behind that attained with full-face methods, it is
noteworthy to mention that our initial approach employs only one frame to
predict the expression, in contraposition to state of the art, exploiting
several order more data comprising spatial-temporal data which is often not
available.
SING: Symbol-to-Instrument Neural Generator
Authors: Alexandre Défossez, Neil Zeghidour, Nicolas Usunier, Léon Bottou, Francis Bach
Date: 2018-10-23 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Audio Synthesis, Decoder, Music Generation

Recent progress in deep learning for audio synthesis opens the way to models
that directly produce the waveform, shifting away from the traditional paradigm
of relying on vocoders or MIDI synthesizers for speech or music generation.
Despite their successes, current state-of-the-art neural audio synthesizers
such as WaveNet and SampleRNN suffer from prohibitive training and inference
times because they are based on autoregressive models that generate audio
samples one at a time at a rate of 16kHz. In this work, we study the more
computationally efficient alternative of generating the waveform frame-by-frame
with large strides. We present SING, a lightweight neural audio synthesizer for
the original task of generating musical notes given desired instrument, pitch
and velocity. Our model is trained end-to-end to generate notes from nearly
1000 instruments with a single decoder, thanks to a new loss function that
minimizes the distances between the log spectrograms of the generated and
target waveforms. On the generalization task of synthesizing notes for pairs of
pitch and instrument not seen during training, SING produces audio with
significantly improved perceptual quality compared to a state-of-the-art
autoencoder based on WaveNet as measured by a Mean Opinion Score (MOS), and is
about 32 times faster for training and 2, 500 times faster for inference.
Visual Semantic Re-ranker for Text Spotting
Authors: Ahmed Sabir, Francesc Moreno-Noguer, Lluís Padró
Date: 2018-10-23 00:00:00
Tasks: Text Spotting

Many current state-of-the-art methods for text recognition are based on
purely local information and ignore the semantic correlation between text and
its surrounding visual context. In this paper, we propose a post-processing
approach to improve the accuracy of text spotting by using the semantic
relation between the text and the scene. We initially rely on an off-the-shelf
deep neural network that provides a series of text hypotheses for each input
image. These text hypotheses are then re-ranked using the semantic relatedness
with the object in the image. As a result of this combination, the performance
of the original network is boosted with a very low computational cost. The
proposed framework can be used as a drop-in complement for any text-spotting
algorithm that outputs a ranking of word hypotheses. We validate our approach
on ICDAR'17 shared task dataset.
Testing the Generalization Power of Neural Network Models Across NLI Benchmarks
Authors: Aarne Talman, Stergios Chatzikyriakidis
Date: 2018-10-23 00:00:00
Proceeding: WS 2019 8
Tasks: Natural Language Inference, Transfer Learning

Neural network models have been very successful in natural language inference, with the best models reaching 90% accuracy in some benchmarks. However, the success of these models turns out to be largely benchmark specific. We show that models trained on a natural language inference dataset drawn from one benchmark fail to perform well in others, even if the notion of inference assumed in these benchmarks is the same or similar. We train six high performing neural network models on different datasets and show that each one of these has problems of generalizing when we replace the original test set with a test set taken from another corpus designed for the same task. In light of these results, we argue that most of the current neural network models are not able to generalize well in the task of natural language inference. We find that using large pre-trained language models helps with transfer learning when the datasets are similar enough. Our results also highlight that the current NLI datasets do not cover the different nuances of inference extensively enough.
Analysis of Atomistic Representations Using Weighted Skip-Connections
Authors: Kim A. Nicoli, Pan Kessel, Michael Gastegger, Kristof T. Schütt
Date: 2018-10-23 00:00:00
Tasks: BIG-bench Machine Learning, Property Prediction

In this work, we extend the SchNet architecture by using weighted skip
connections to assemble the final representation. This enables us to study the
relative importance of each interaction block for property prediction. We
demonstrate on both the QM9 and MD17 dataset that their relative weighting
depends strongly on the chemical composition and configurational degrees of
freedom of the molecules which opens the path towards a more detailed
understanding of machine learning models for molecules.
On PAC-Bayesian Bounds for Random Forests
Authors: Stephan Sloth Lorenzen, Christian Igel, Yevgeny Seldin
Date: 2018-10-23 00:00:00
Tasks: Generalization Bounds

Existing guarantees in terms of rigorous upper bounds on the generalization
error for the original random forest algorithm, one of the most frequently used
machine learning methods, are unsatisfying. We discuss and evaluate various
PAC-Bayesian approaches to derive such bounds. The bounds do not require
additional hold-out data, because the out-of-bag samples from the bagging in
the training process can be exploited. A random forest predicts by taking a
majority vote of an ensemble of decision trees. The first approach is to bound
the error of the vote by twice the error of the corresponding Gibbs classifier
(classifying with a single member of the ensemble selected at random). However,
this approach does not take into account the effect of averaging out of errors
of individual classifiers when taking the majority vote. This effect provides a
significant boost in performance when the errors are independent or negatively
correlated, but when the correlations are strong the advantage from taking the
majority vote is small. The second approach based on PAC-Bayesian C-bounds
takes dependencies between ensemble members into account, but it requires
estimating correlations between the errors of the individual classifiers. When
the correlations are high or the estimation is poor, the bounds degrade. In our
experiments, we compute generalization bounds for random forests on various
benchmark data sets. Because the individual decision trees already perform
well, their predictions are highly correlated and the C-bounds do not lead to
satisfactory results. For the same reason, the bounds based on the analysis of
Gibbs classifiers are typically superior and often reasonably tight. Bounds
based on a validation set coming at the cost of a smaller training set gave
better performance guarantees, but worse performance in most experiments.
Bayesian Deconvolution of Scanning Electron Microscopy Images Using Point-spread Function Estimation and Non-local Regularization
Authors: Joris Roels, Jan Aelterman, Jonas De Vylder, Hiep Luong, Yvan Saeys, Wilfried Philips
Date: 2018-10-23 00:00:00

Microscopy is one of the most essential imaging techniques in life sciences.
High-quality images are required in order to solve (potentially life-saving)
biomedical research problems. Many microscopy techniques do not achieve
sufficient resolution for these purposes, being limited by physical diffraction
and hardware deficiencies. Electron microscopy addresses optical diffraction by
measuring emitted or transmitted electrons instead of photons, yielding
nanometer resolution. Despite pushing back the diffraction limit, blur should
still be taken into account because of practical hardware imperfections and
remaining electron diffraction. Deconvolution algorithms can remove some of the
blur in post-processing but they depend on knowledge of the point-spread
function (PSF) and should accurately regularize noise. Any errors in the
estimated PSF or noise model will reduce their effectiveness. This paper
proposes a new procedure to estimate the lateral component of the point spread
function of a 3D scanning electron microscope more accurately. We also propose
a Bayesian maximum a posteriori deconvolution algorithm with a non-local image
prior which employs this PSF estimate and previously developed noise
statistics. We demonstrate visual quality improvements and show that applying
our method improves the quality of subsequent segmentation steps.
Convolutional Neural Network Pruning to Accelerate Membrane Segmentation in Electron Microscopy
Authors: Joris Roels, Jonas De Vylder, Jan Aelterman, Yvan Saeys, Wilfried Philips
Date: 2018-10-23 00:00:00
Tasks: Network Pruning, Segmentation

Biological membranes are one of the most basic structures and regions of
interest in cell biology. In the study of membranes, segment extraction is a
well-known and difficult problem because of impeding noise, directional and
thickness variability, etc. Recent advances in electron microscopy membrane
segmentation are able to cope with such difficulties by training convolutional
neural networks. However, because of the massive amount of features that have
to be extracted while propagating forward, the practical usability diminishes,
even with state-of-the-art GPU's. A significant part of these network features
typically contains redundancy through correlation and sparsity. In this work,
we propose a pruning method for convolutional neural networks that ensures the
training loss increase is minimized. We show that the pruned networks, after
retraining, are more efficient in terms of time and memory, without
significantly affecting the network accuracy. This way, we manage to obtain
real-time membrane segmentation performance, for our specific electron
microscopy setup.