pwc-archive/papers-with-abstracts huggingface dataset:
SAFE: Self-Attentive Function Embeddings for Binary Similarity
Authors: Luca Massarelli, Giuseppe Antonio Di Luna, Fabio Petroni, Leonardo Querzoni, Roberto Baldoni
Date: 2018-11-13 00:00:00
Tasks: Malware Analysis, Vulnerability Detection

The binary similarity problem consists in determining if two functions are similar by only considering their compiled form. Advanced techniques for binary similarity recently gained momentum as they can be applied in several fields, such as copyright disputes, malware analysis, vulnerability detection, etc., and thus have an immediate practical impact. Current solutions compare functions by first transforming their binary code in multi-dimensional vector representations (embeddings), and then comparing vectors through simple and efficient geometric operations. However, embeddings are usually derived from binary code using manual feature extraction, that may fail in considering important function characteristics, or may consider features that are not important for the binary similarity problem. In this paper we propose SAFE, a novel architecture for the embedding of functions based on a self-attentive neural network. SAFE works directly on disassembled binary functions, does not require manual feature extraction, is computationally more efficient than existing solutions (i.e., it does not incur in the computational overhead of building or manipulating control flow graphs), and is more general as it works on stripped binaries and on multiple architectures. We report the results from a quantitative and qualitative analysis that show how SAFE provides a noticeable performance improvement with respect to previous solutions. Furthermore, we show how clusters of our embedding vectors are closely related to the semantic of the implemented algorithms, paving the way for further interesting applications (e.g. semantic-based binary function search).
Pose Invariant 3D Face Reconstruction
Authors: Lei Jiang, Xiao-Jun Wu, Josef Kittler
Date: 2018-11-13 00:00:00
Tasks: 3D Face Reconstruction, Face Reconstruction

3D face reconstruction is an important task in the field of computer vision.
Although 3D face reconstruction has being developing rapidly in recent years,
it is still a challenge for face reconstruction under large pose. That is
because much of the information about a face in a large pose will be
unknowable. In order to address this issue, this paper proposes a novel 3D face
reconstruction algorithm (PIFR) based on 3D Morphable Model (3DMM). After input
a single face image, it generates a frontal image by normalizing the image.
Then we set weighted sum of the 3D parameters of the two images. Our method
solves the problem of face reconstruction of a single image of a traditional
method in a large pose, works on arbitrary Pose and Expressions, greatly
improves the accuracy of reconstruction. Experiments on the challenging AFW,
LFPW and AFLW database show that our algorithm significantly improves the
accuracy of 3D face reconstruction even under extreme poses .
Intelligent Drone Swarm for Search and Rescue Operations at Sea
Authors: Vincenzo Lomonaco, Angelo Trotta, Marta Ziosi, Juan de Dios Yáñez Ávila, Natalia Díaz-Rodríguez
Date: 2018-11-13 00:00:00

In recent years, a rising numbers of people arrived in the European Union,
traveling across the Mediterranean Sea or overland through Southeast Europe in
what has been later named as the European migrant crisis. In the last 5 years,
more than 16 thousands people have lost their lives in the Mediterranean sea
during the crossing. The United Nations Secretary General Strategy on New
Technologies is supporting the use of Artificial Intelligence (AI) and Robotics
to accelerate the achievement of the 2030 Sustainable Development Agenda, which
includes safe and regular migration processes among the others. In the same
spirit, the central idea of this project aims at using AI technology for Search
And Rescue (SAR) operations at sea. In particular, we propose an autonomous
fleet of self-organizing intelligent drones that would enable the coverage of a
broader area, speeding-up the search processes and finally increasing the
efficiency and effectiveness of migrants rescue operations.
Towards the Design of Aerostat Wind Turbine Arrays through AI
Authors: Larry Bull, Neil Phillips
Date: 2018-11-13 00:00:00

A new form of aerostat wind generation system which contains an array of
interacting turbines is proposed. The design of the balloon turbine components
is undertaken through the combination of artificial intelligence and rapid
prototyping techniques such that the need for highly accurate
models/simulations of the lift and wake dynamics is removed/reduced. Initial
small-scale wind tunnel testing to determine design and algorithmic
fundamentals will be presented.
Predicting Distresses using Deep Learning of Text Segments in Annual Reports
Authors: Rastin Matin, Casper Hansen, Christian Hansen, Pia Mølgaard
Date: 2018-11-13 00:00:00
Tasks: Descriptive

Corporate distress models typically only employ the numerical financial
variables in the firms' annual reports. We develop a model that employs the
unstructured textual data in the reports as well, namely the auditors' reports
and managements' statements. Our model consists of a convolutional recurrent
neural network which, when concatenated with the numerical financial variables,
learns a descriptive representation of the text that is suited for corporate
distress prediction. We find that the unstructured data provides a
statistically significant enhancement of the distress prediction performance,
in particular for large firms where accurate predictions are of the utmost
importance. Furthermore, we find that auditors' reports are more informative
than managements' statements and that a joint model including both managements'
statements and auditors' reports displays no enhancement relative to a model
including only auditors' reports. Our model demonstrates a direct improvement
over existing state-of-the-art models.
Anomaly Detection using Autoencoders in High Performance Computing Systems
Authors: Andrea Borghesi, Andrea Bartolini, Michele Lombardi, Michela Milano, Luca Benini
Date: 2018-11-13 00:00:00
Tasks: Anomaly Detection, Vocal Bursts Intensity Prediction

Anomaly detection in supercomputers is a very difficult problem due to the
big scale of the systems and the high number of components. The current state
of the art for automated anomaly detection employs Machine Learning methods or
statistical regression models in a supervised fashion, meaning that the
detection tool is trained to distinguish among a fixed set of behaviour classes
(healthy and unhealthy states).
  We propose a novel approach for anomaly detection in High Performance
Computing systems based on a Machine (Deep) Learning technique, namely a type
of neural network called autoencoder. The key idea is to train a set of
autoencoders to learn the normal (healthy) behaviour of the supercomputer nodes
and, after training, use them to identify abnormal conditions. This is
different from previous approaches which where based on learning the abnormal
condition, for which there are much smaller datasets (since it is very hard to
identify them to begin with).
  We test our approach on a real supercomputer equipped with a fine-grained,
scalable monitoring infrastructure that can provide large amount of data to
characterize the system behaviour. The results are extremely promising: after
the training phase to learn the normal system behaviour, our method is capable
of detecting anomalies that have never been seen before with a very good
accuracy (values ranging between 88% and 96%).
A conjugate prior for the Dirichlet distribution
Authors: Jean-Marc Andreoli
Date: 2018-11-13 00:00:00

This note investigates a conjugate class for the Dirichlet distribution class
in the exponential family.
How Secure are Deep Learning Algorithms from Side-Channel based Reverse Engineering?
Authors: Manaar Alam, Debdeep Mukhopadhyay
Date: 2018-11-13 00:00:00
Tasks: Deep Learning, Medical Image Analysis, Privacy Preserving, Two-sample testing

Deep Learning algorithms have recently become the de-facto paradigm for
various prediction problems, which include many privacy-preserving applications
like online medical image analysis. Presumably, the privacy of data in a deep
learning system is a serious concern. There have been several efforts to
analyze and exploit the information leakages from deep learning architectures
to compromise data privacy. In this paper, however, we attempt to provide an
evaluation strategy for such information leakages through deep neural network
architectures by considering a case study on Convolutional Neural Network (CNN)
based image classifier. The approach takes the aid of low-level hardware
information, provided by Hardware Performance Counters (HPCs), during the
execution of a CNN classifier and a simple hypothesis testing in order to
produce an alarm if there exists any information leakage on the actual input.
Deep Neural Network Concepts for Background Subtraction: A Systematic Review and Comparative Evaluation
Authors: Thierry Bouwmans, Sajid Javed, Maryam Sultana, Soon Ki Jung
Date: 2018-11-13 00:00:00
Tasks: Video Background Subtraction

Conventional neural networks show a powerful framework for background
subtraction in video acquired by static cameras. Indeed, the well-known SOBS
method and its variants based on neural networks were the leader methods on the
largescale CDnet 2012 dataset during a long time. Recently, convolutional
neural networks which belong to deep learning methods were employed with
success for background initialization, foreground detection and deep learned
features. Currently, the top current background subtraction methods in CDnet
2014 are based on deep neural networks with a large gap of performance in
comparison on the conventional unsupervised approaches based on multi-features
or multi-cues strategies. Furthermore, a huge amount of papers was published
since 2016 when Braham and Van Droogenbroeck published their first work on CNN
applied to background subtraction providing a regular gain of performance. In
this context, we provide the first review of deep neural network concepts in
background subtraction for novices and experts in order to analyze this success
and to provide further directions. For this, we first surveyed the methods used
background initialization, background subtraction and deep learned features.
Then, we discuss the adequacy of deep neural networks for background
subtraction. Finally, experimental results are presented on the CDnet 2014
dataset.
Image Captioning Based on a Hierarchical Attention Mechanism and Policy Gradient Optimization
Authors: Shi-Yang Yan, Yuan Xie, Fang-Yu Wu, Jeremy S. Smith, Wenjin Lu, Bai-Ling Zhang
Date: 2018-11-13 00:00:00
Tasks: Generative Adversarial Network, Image Captioning, Reinforcement Learning, Reinforcement Learning (RL)

Automatically generating the descriptions of an image, i.e., image
captioning, is an important and fundamental topic in artificial intelligence,
which bridges the gap between computer vision and natural language processing.
Based on the successful deep learning models, especially the CNN model and Long
Short-Term Memories (LSTMs) with attention mechanism, we propose a hierarchical
attention model by utilizing both of the global CNN features and the local
object features for more effective feature representation and reasoning in
image captioning. The generative adversarial network (GAN), together with a
reinforcement learning (RL) algorithm, is applied to solve the exposure bias
problem in RNN-based supervised training for language problems. In addition,
through the automatic measurement of the consistency between the generated
caption and the image content by the discriminator in the GAN framework and RL
optimization, we make the finally generated sentences more accurate and
natural. Comprehensive experiments show the improved performance of the
hierarchical attention mechanism and the effectiveness of our RL-based
optimization method. Our model achieves state-of-the-art results on several
important metrics in the MSCOCO dataset, using only greedy inference.
Modality Attention for End-to-End Audio-visual Speech Recognition
Authors: Pan Zhou, Wenwen Yang, Wei Chen, Yan-Feng Wang, Jia Jia
Date: 2018-11-13 00:00:00
Tasks: Audio-Visual Speech Recognition, Robust Speech Recognition, speech-recognition, Speech Recognition, Visual Speech Recognition

Audio-visual speech recognition (AVSR) system is thought to be one of the
most promising solutions for robust speech recognition, especially in noisy
environment. In this paper, we propose a novel multimodal attention based
method for audio-visual speech recognition which could automatically learn the
fused representation from both modalities based on their importance. Our method
is realized using state-of-the-art sequence-to-sequence (Seq2seq)
architectures. Experimental results show that relative improvements from 2% up
to 36% over the auditory modality alone are obtained depending on the different
signal-to-noise-ratio (SNR). Compared to the traditional feature concatenation
methods, our proposed approach can achieve better recognition performance under
both clean and noisy conditions. We believe modality attention based end-to-end
method can be easily generalized to other multimodal tasks with correlated
information.
Modular Networks: Learning to Decompose Neural Computation
Authors: Louis Kirsch, Julius Kunze, David Barber
Date: 2018-11-13 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Diversity, Language Modeling, Language Modelling

Scaling model capacity has been vital in the success of deep learning. For a
typical network, necessary compute resources and training time grow
dramatically with model size. Conditional computation is a promising way to
increase the number of parameters with a relatively small increase in
resources. We propose a training algorithm that flexibly chooses neural modules
based on the data to be processed. Both the decomposition and modules are
learned end-to-end. In contrast to existing approaches, training does not rely
on regularization to enforce diversity in module use. We apply modular networks
both to image recognition and language modeling tasks, where we achieve
superior performance compared to several baselines. Introspection reveals that
modules specialize in interpretable contexts.
An Online Attention-based Model for Speech Recognition
Authors: Ruchao Fan, Pan Zhou, Wei Chen, Jia Jia, Gang Liu
Date: 2018-11-13 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, model, speech-recognition, Speech Recognition

Attention-based end-to-end models such as Listen, Attend and Spell (LAS),
simplify the whole pipeline of traditional automatic speech recognition (ASR)
systems and become popular in the field of speech recognition. In previous
work, researchers have shown that such architectures can acquire comparable
results to state-of-the-art ASR systems, especially when using a bidirectional
encoder and global soft attention (GSA) mechanism. However, bidirectional
encoder and GSA are two obstacles for real-time speech recognition. In this
work, we aim to stream LAS baseline by removing the above two obstacles. On the
encoder side, we use a latency-controlled (LC) bidirectional structure to
reduce the delay of forward computation. Meanwhile, an adaptive monotonic
chunk-wise attention (AMoChA) mechanism is proposed to replace GSA for the
calculation of attention weight distribution. Furthermore, we propose two
methods to alleviate the huge performance degradation when combining LC and
AMoChA. Finally, we successfully acquire an online LAS model, LC-AMoChA, which
has only 3.5% relative performance reduction to LAS baseline on our internal
Mandarin corpus.
Interpretable Credit Application Predictions With Counterfactual Explanations
Authors: Rory Mc Grath, Luca Costabello, Chan Le Van, Paul Sweeney, Farbod Kamiab, Zhao Shen, Freddy Lecue
Date: 2018-11-13 00:00:00
Tasks: counterfactual

We predict credit applications with off-the-shelf, interchangeable black-box
classifiers and we explain single predictions with counterfactual explanations.
Counterfactual explanations expose the minimal changes required on the input
data to obtain a different result e.g., approved vs rejected application.
Despite their effectiveness, counterfactuals are mainly designed for changing
an undesired outcome of a prediction i.e. loan rejected. Counterfactuals,
however, can be difficult to interpret, especially when a high number of
features are involved in the explanation. Our contribution is two-fold: i) we
propose positive counterfactuals, i.e. we adapt counterfactual explanations to
also explain accepted loan applications, and ii) we propose two weighting
strategies to generate more interpretable counterfactuals. Experiments on the
HELOC loan applications dataset show that our contribution outperforms the
baseline counterfactual generation strategy, by leading to smaller and hence
more interpretable counterfactuals.
BAN: Focusing on Boundary Context for Object Detection
Authors: Yonghyun Kim, Taewook Kim, Bong-Nam Kang, Jieun Kim, Daijin Kim
Date: 2018-11-13 00:00:00
Tasks: Object, object-detection, Object Detection

Visual context is one of the important clue for object detection and the
context information for boundaries of an object is especially valuable. We
propose a boundary aware network (BAN) designed to exploit the visual contexts
including boundary information and surroundings, named boundary context, and
define three types of the boundary contexts: side, vertex and in/out-boundary
context. Our BAN consists of 10 sub-networks for the area belonging to the
boundary contexts. The detection head of BAN is defined as an ensemble of these
sub-networks with different contributions depending on the sub-problem of
detection. To verify our method, we visualize the activation of the
sub-networks according to the boundary contexts and empirically show that the
sub-networks contribute more to the related sub-problem in detection. We
evaluate our method on PASCAL VOC detection benchmark and MS COCO dataset. The
proposed method achieves the mean Average Precision (mAP) of 83.4% on PASCAL
VOC and 36.9% on MS COCO. BAN allows the convolution network to provide an
additional source of contexts for detection and selectively focus on the more
important contexts, and it can be generally applied to many other detection
methods as well to enhance the accuracy in detection.
A Multi-layer LSTM-based Approach for Robot Command Interaction Modeling
Authors: Martino Mensio, Emanuele Bastianelli, Ilaria Tiddi, Giuseppe Rizzo
Date: 2018-11-13 00:00:00
Tasks: Natural Language Understanding, Semantic Parsing

As the first robotic platforms slowly approach our everyday life, we can
imagine a near future where service robots will be easily accessible by
non-expert users through vocal interfaces. The capability of managing natural
language would indeed speed up the process of integrating such platform in the
ordinary life. Semantic parsing is a fundamental task of the Natural Language
Understanding process, as it allows extracting the meaning of a user utterance
to be used by a machine. In this paper, we present a preliminary study to
semantically parse user vocal commands for a House Service robot, using a
multi-layer Long-Short Term Memory neural network with attention mechanism. The
system is trained on the Human Robot Interaction Corpus, and it is
preliminarily compared with previous approaches.
Massively Distributed SGD: ImageNet/ResNet-50 Training in a Flash
Authors: Hiroaki Mikami, Hisahiro Suganuma, Pongsakorn U-chupala, Yoshiki Tanaka, Yuichi Kageyama
Date: 2018-11-13 00:00:00
Tasks: GPU

Scaling the distributed deep learning to a massive GPU cluster level is
challenging due to the instability of the large mini-batch training and the
overhead of the gradient synchronization. We address the instability of the
large mini-batch training with batch-size control and label smoothing. We
address the overhead of the gradient synchronization with 2D-Torus all-reduce.
Specifically, 2D-Torus all-reduce arranges GPUs in a logical 2D grid and
performs a series of collective operation in different orientations. These two
techniques are implemented with Neural Network Libraries (NNL). We have
successfully trained ImageNet/ResNet-50 in 122 seconds without significant
accuracy loss on ABCI cluster.
Theoretical Analysis of Adversarial Learning: A Minimax Approach
Authors: Zhuozhuo Tu, Jingwei Zhang, DaCheng Tao
Date: 2018-11-13 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Adversarial Robustness, General Classification, Multi-class Classification

Here we propose a general theoretical method for analyzing the risk bound in
the presence of adversaries. Specifically, we try to fit the adversarial
learning problem into the minimax framework. We first show that the original
adversarial learning problem can be reduced to a minimax statistical learning
problem by introducing a transport map between distributions. Then, we prove a
new risk bound for this minimax problem in terms of covering numbers under a
weak version of Lipschitz condition. Our method can be applied to multi-class
classification problems and commonly used loss functions such as the hinge and
ramp losses. As some illustrative examples, we derive the adversarial risk
bounds for SVMs, deep neural networks, and PCA, and our bounds have two
data-dependent terms, which can be optimized for achieving adversarial
robustness.
An Orchestrated Empirical Study on Deep Learning Frameworks and Platforms
Authors: Qianyu Guo, Xiaofei Xie, Lei Ma, Qiang Hu, Ruitao Feng, Li Li, Yang Liu, Jianjun Zhao, Xiaohong Li
Date: 2018-11-13 00:00:00
Tasks: Autonomous Driving, Deep Learning

Deep learning (DL) has recently achieved tremendous success in a variety of
cutting-edge applications, e.g., image recognition, speech and natural language
processing, and autonomous driving. Besides the available big data and hardware
evolution, DL frameworks and platforms play a key role to catalyze the
research, development, and deployment of DL intelligent solutions. However, the
difference in computation paradigm, architecture design and implementation of
existing DL frameworks and platforms brings challenges for DL software
development, deployment, maintenance, and migration. Up to the present, it
still lacks a comprehensive study on how current diverse DL frameworks and
platforms influence the DL software development process.
  In this paper, we initiate the first step towards the investigation on how
existing state-of-the-art DL frameworks (i.e., TensorFlow, Theano, and Torch)
and platforms (i.e., server/desktop, web, and mobile) support the DL software
development activities. We perform an in-depth and comparative evaluation on
metrics such as learning accuracy, DL model size, robustness, and performance,
on state-of-the-art DL frameworks across platforms using two popular datasets
MNIST and CIFAR-10. Our study reveals that existing DL frameworks still suffer
from compatibility issues, which becomes even more severe when it comes to
different platforms. We pinpoint the current challenges and opportunities
towards developing high quality and compatible DL systems. To ignite further
investigation along this direction to address urgent industrial demands of
intelligent solutions, we make all of our assembled feasible toolchain and
dataset publicly available.