pwc-archive/papers-with-abstracts huggingface dataset:
Mining a Sub-Matrix of Maximal Sum
Authors: Vincent Branders, Pierre Schaus, Pierre Dupont
Date: 2017-09-25 00:00:00

Biclustering techniques have been widely used to identify homogeneous
subgroups within large data matrices, such as subsets of genes similarly
expressed across subsets of patients. Mining a max-sum sub-matrix is a related
but distinct problem for which one looks for a (non-necessarily contiguous)
rectangular sub-matrix with a maximal sum of its entries. Le Van et al. (Ranked
Tiling, 2014) already illustrated its applicability to gene expression analysis
and addressed it with a constraint programming (CP) approach combined with
large neighborhood search (CP-LNS). In this work, we exhibit some key
properties of this NP-hard problem and define a bounding function such that
larger problems can be solved in reasonable time. Two different algorithms are
proposed in order to exploit the highlighted characteristics of the problem: a
CP approach with a global constraint (CPGC) and mixed integer linear
programming (MILP). Practical experiments conducted both on synthetic and real
gene expression data exhibit the characteristics of these approaches and their
relative benefits over the original CP-LNS method. Overall, the CPGC approach
tends to be the fastest to produce a good solution. Yet, the MILP formulation
is arguably the easiest to formulate and can also be competitive.
House Price Prediction Using LSTM
Authors: Xiaochen Chen, Lai Wei, Jiaxin Xu
Date: 2017-09-25 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis

In this paper, we use the house price data ranging from January 2004 to
October 2016 to predict the average house price of November and December in
2016 for each district in Beijing, Shanghai, Guangzhou and Shenzhen. We apply
Autoregressive Integrated Moving Average model to generate the baseline while
LSTM networks to build prediction model. These algorithms are compared in terms
of Mean Squared Error. The result shows that the LSTM model has excellent
properties with respect to predict time series. Also, stateful LSTM networks
and stack LSTM networks are employed to further study the improvement of
accuracy of the house prediction model.
Towards continuous control of flippers for a multi-terrain robot using deep reinforcement learning
Authors: Giuseppe Paolo, Lei Tai, Ming Liu
Date: 2017-09-25 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper we focus on developing a control algorithm for multi-terrain
tracked robots with flippers using a reinforcement learning (RL) approach. The
work is based on the deep deterministic policy gradient (DDPG) algorithm,
proven to be very successful in simple simulation environments. The algorithm
works in an end-to-end fashion in order to control the continuous position of
the flippers. This end-to-end approach makes it easy to apply the controller to
a wide array of circumstances, but the huge flexibility comes to the cost of an
increased difficulty of solution. The complexity of the task is enlarged even
more by the fact that real multi-terrain robots move in partially observable
environments. Notwithstanding these complications, being able to smoothly
control a multi-terrain robot can produce huge benefits in impaired people
daily lives or in search and rescue situations.
DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks
Authors: Sen Wang, Ronald Clark, Hongkai Wen, Niki Trigoni
Date: 2017-09-25 00:00:00
Tasks: Monocular Visual Odometry, Motion Estimation, Visual Odometry

This paper studies monocular visual odometry (VO) problem. Most of existing
VO algorithms are developed under a standard pipeline including feature
extraction, feature matching, motion estimation, local optimisation, etc.
Although some of them have demonstrated superior performance, they usually need
to be carefully designed and specifically fine-tuned to work well in different
environments. Some prior knowledge is also required to recover an absolute
scale for monocular VO. This paper presents a novel end-to-end framework for
monocular VO by using deep Recurrent Convolutional Neural Networks (RCNNs).
Since it is trained and deployed in an end-to-end manner, it infers poses
directly from a sequence of raw RGB images (videos) without adopting any module
in the conventional VO pipeline. Based on the RCNNs, it not only automatically
learns effective feature representation for the VO problem through
Convolutional Neural Networks, but also implicitly models sequential dynamics
and relations using deep Recurrent Neural Networks. Extensive experiments on
the KITTI VO dataset show competitive performance to state-of-the-art methods,
verifying that the end-to-end Deep Learning technique can be a viable
complement to the traditional VO systems.
Non-iterative Label Propagation in Optimal Leading Forest
Authors: Ji Xu, Guoyin Wang
Date: 2017-09-25 00:00:00
Tasks: graph construction, Relation

Graph based semi-supervised learning (GSSL) has intuitive representation and
can be improved by exploiting the matrix calculation. However, it has to
perform iterative optimization to achieve a preset objective, which usually
leads to low efficiency. Another inconvenience lying in GSSL is that when new
data come, the graph construction and the optimization have to be conducted all
over again. We propose a sound assumption, arguing that: the neighboring data
points are not in peer-to-peer relation, but in a partial-ordered relation
induced by the local density and distance between the data; and the label of a
center can be regarded as the contribution of its followers. Starting from the
assumption, we develop a highly efficient non-iterative label propagation
algorithm based on a novel data structure named as optimal leading forest
(LaPOLeaF). The major weaknesses of the traditional GSSL are addressed by this
study. We further scale LaPOLeaF to accommodate big data by utilizing block
distance matrix technique, parallel computing, and Locality-Sensitive Hashing
(LSH). Experiments on large datasets have shown the promising results of the
proposed methods.
Toward a full-scale neural machine translation in production: the Booking.com use case
Authors: Pavel Levin, Nishikant Dhanuka, Talaat Khalil, Fedor Kovalev, Maxim Khalilov
Date: 2017-09-18 00:00:00
Proceeding: MTSummit 2017 9
Tasks: Machine Translation, NMT, Translation

While some remarkable progress has been made in neural machine translation
(NMT) research, there have not been many reports on its development and
evaluation in practice. This paper tries to fill this gap by presenting some of
our findings from building an in-house travel domain NMT system in a large
scale E-commerce setting. The three major topics that we cover are optimization
and training (including different optimization strategies and corpus sizes),
handling real-world content and evaluating results.
An Information-theoretic Approach to Machine-oriented Music Summarization
Authors: Francisco Raposo, David Martins de Matos, Ricardo Ribeiro
Date: 2016-12-07 00:00:00

Music summarization allows for higher efficiency in processing, storage, and
sharing of datasets. Machine-oriented approaches, being agnostic to human
consumption, optimize these aspects even further. Such summaries have already
been successfully validated in some MIR tasks. We now generalize previous
conclusions by evaluating the impact of generic summarization of music from a
probabilistic perspective. We estimate Gaussian distributions for original and
summarized songs and compute their relative entropy, in order to measure
information loss incurred by summarization. Our results suggest that relative
entropy is a good predictor of summarization performance in the context of
tasks relying on a bag-of-features model. Based on this observation, we further
propose a straightforward yet expressive summarizer, which minimizes relative
entropy with respect to the original song, that objectively outperforms
previous methods and is better suited to avoid potential copyright issues.
Deep Learning Based Cryptographic Primitive Classification
Authors: Gregory D. Hill, Xavier J. A. Bellekens
Date: 2017-09-25 00:00:00
Tasks: Classification, Deep Learning, General Classification

Cryptovirological augmentations present an immediate, incomparable threat.
Over the last decade, the substantial proliferation of crypto-ransomware has
had widespread consequences for consumers and organisations alike. Established
preventive measures perform well, however, the problem has not ceased. Reverse
engineering potentially malicious software is a cumbersome task due to platform
eccentricities and obfuscated transmutation mechanisms, hence requiring
smarter, more efficient detection strategies. The following manuscript presents
a novel approach for the classification of cryptographic primitives in compiled
binary executables using deep learning. The model blueprint, a DCNN, is
fittingly configured to learn from variable-length control flow diagnostics
output from a dynamic trace. To rival the size and variability of contemporary
data compendiums, hence feeding the model cognition, a methodology for the
procedural generation of synthetic cryptographic binaries is defined, utilising
core primitives from OpenSSL with multivariate obfuscation, to draw a vastly
scalable distribution. The library, CryptoKnight, rendered an algorithmic pool
of AES, RC4, Blowfish, MD5 and RSA to synthesis combinable variants which are
automatically fed in its core model. Converging at 91% accuracy, CryptoKnight
is successfully able to classify the sample algorithms with minimal loss.
Handwriting recognition using Cohort of LSTM and lexicon verification with extremely large lexicon
Authors: Bruno Stuner, Clément Chatelain, Thierry Paquet
Date: 2016-12-22 00:00:00
Tasks: Handwriting Recognition

State-of-the-art methods for handwriting recognition are based on Long Short
Term Memory (LSTM) recurrent neural networks (RNN), which now provides very
impressive character recognition performance. The character recognition is
generally coupled with a lexicon driven decoding process which integrates
dictionaries. Unfortunately these dictionaries are limited to hundred of
thousands words for the best systems, which prevent from having a good language
coverage, and therefore limit the global recognition performance. In this
article, we propose an alternative to the lexicon driven decoding process based
on a lexicon verification process, coupled with an original cascade
architecture. The cascade is made of a large number of complementary networks
extracted from a single training (called cohort), making the learning process
very light. The proposed method achieves new state-of-the art word recognition
performance on the Rimes and IAM databases. Dealing with gigantic lexicon of 3
millions words, the methods also demonstrates interesting performance with a
fast decision stage.
Deep Sparse Subspace Clustering
Authors: Xi Peng, Jiashi Feng, Shijie Xiao, Jiwen Lu, Zhang Yi, Shuicheng Yan
Date: 2017-09-25 00:00:00
Tasks: Clustering, valid

In this paper, we present a deep extension of Sparse Subspace Clustering,
termed Deep Sparse Subspace Clustering (DSSC). Regularized by the unit sphere
distribution assumption for the learned deep features, DSSC can infer a new
data affinity matrix by simultaneously satisfying the sparsity principle of SSC
and the nonlinearity given by neural networks. One of the appealing advantages
brought by DSSC is: when original real-world data do not meet the
class-specific linear subspace distribution assumption, DSSC can employ neural
networks to make the assumption valid with its hierarchical nonlinear
transformations. To the best of our knowledge, this is among the first deep
learning based subspace clustering methods. Extensive experiments are conducted
on four real-world datasets to show the proposed DSSC is significantly superior
to 12 existing methods for subspace clustering.
Robust Associative Memories Naturally Occuring From Recurrent Hebbian Networks Under Noise
Authors: Eliott Coyac, Vincent Gripon, Charlotte Langlais, Claude Berrou
Date: 2017-09-25 00:00:00

The brain is a noisy system subject to energy constraints. These facts are
rarely taken into account when modelling artificial neural networks. In this
paper, we are interested in demonstrating that those factors can actually lead
to the appearance of robust associative memories. We first propose a simplified
model of noise in the brain, taking into account synaptic noise and
interference from neurons external to the network. When coarsely quantized, we
show that this noise can be reduced to insertions and erasures. We take a
neural network with recurrent modifiable connections, and subject it to noisy
external inputs. We introduce an energy usage limitation principle in the
network as well as consolidated Hebbian learning, resulting in an incremental
processing of inputs. We show that the connections naturally formed correspond
to state-of-the-art binary sparse associative memories.
"Let me convince you to buy my product ... ": A Case Study of an Automated Persuasive System for Fashion Products
Authors: Vitobha Munigala, Srikanth Tamilselvam, Anush Sankaran
Date: 2017-09-25 00:00:00
Tasks: Sentence

Persuasivenes is a creative art aimed at making people believe in certain set
of beliefs. Many a times, such creativity is about adapting richness of one
domain into another to strike a chord with the target audience. In this
research, we present PersuAIDE! - A persuasive system based on linguistic
creativity to transform given sentence to generate various forms of persuading
sentences. These various forms cover multiple focus of persuasion such as
memorability and sentiment. For a given simple product line, the algorithm is
composed of several steps including: (i) select an appropriate well-known
expression for the target domain to add memorability, (ii) identify keywords
and entities in the given sentence and expression and transform it to produce
creative persuading sentence, and (iii) adding positive or negative sentiment
for further persuasion. The persuasive conversion were manually verified using
qualitative results and the effectiveness of the proposed approach is
empirically discussed.
Multi-level Chaotic Maps for 3D Textured Model Encryption
Authors: Xin Jin, Shuyun Zhu, Le Wu, Geng Zhao, Xiao-Dong Li, Quan Zhou, Huimin Lu
Date: 2017-09-25 00:00:00

With rapid progress of Virtual Reality and Augmented Reality technologies, 3D contents are the next widespread media in many applications. Thus, the protection of 3D models is primarily important. Encryption of 3D models is essential to maintain confidentiality. Previous work on encryption of 3D surface model often consider the point clouds, the meshes and the textures individually. In this work, a multi-level chaotic maps models for 3D textured encryption was presented by observing the different contributions for recognizing cipher 3D models between vertices (point cloud), polygons and textures. For vertices which make main contribution for recognizing, we use high level 3D Lu chaotic map to encrypt them. For polygons and textures which make relatively smaller contributions for recognizing, we use 2D Arnold's cat map and 1D Logistic map to encrypt them, respectively. The experimental results show that our method can get similar performance with the other method use the same high level chaotic map for point cloud, polygons and textures, while we use less time. Besides, our method can resist more method of attacks such as statistic attack, brute-force attack, correlation attack.
Realizing Half-Diminished Reality from Video Stream of Manipulating Objects
Authors: Hayato Okumoto, Mitsuo Yoshida, Kyoji Umemura
Date: 2017-09-25 00:00:00

When we watch a video, in which human hands manipulate objects, these hands
may obscure some parts of those objects. We are willing to make clear how the
objects are manipulated by making the image of hands semi-transparent, and
showing the complete images of the hands and the object. By carefully choosing
a Half-Diminished Reality method, this paper proposes a method that can process
the video in real time and verifies that the proposed method works well.
Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning
Authors: Jun Inoue, Yoriyuki Yamagata, Yuqi Chen, Christopher M. Poskitt, Jun Sun
Date: 2017-09-15 00:00:00
Tasks: Anomaly Detection, BIG-bench Machine Learning, Time Series, Time Series Analysis

In this paper, we propose and evaluate the application of unsupervised
machine learning to anomaly detection for a Cyber-Physical System (CPS). We
compare two methods: Deep Neural Networks (DNN) adapted to time series data
generated by a CPS, and one-class Support Vector Machines (SVM). These methods
are evaluated against data from the Secure Water Treatment (SWaT) testbed, a
scaled-down but fully operational raw water purification plant. For both
methods, we first train detectors using a log generated by SWaT operating under
normal conditions. Then, we evaluate the performance of both methods using a
log generated by SWaT operating under 36 different attack scenarios. We find
that our DNN generates fewer false positives than our one-class SVM while our
SVM detects slightly more anomalies. Overall, our DNN has a slightly better F
measure than our SVM. We discuss the characteristics of the DNN and one-class
SVM used in this experiment, and compare the advantages and disadvantages of
the two methods.
Pose-driven Deep Convolutional Model for Person Re-identification
Authors: Chi Su, Jianing Li, Shiliang Zhang, Junliang Xing, Wen Gao, Qi Tian
Date: 2017-09-25 00:00:00
Proceeding: ICCV 2017 10
Tasks: Person Re-Identification

Feature extraction and matching are two crucial components in person
Re-Identification (ReID). The large pose deformations and the complex view
variations exhibited by the captured person images significantly increase the
difficulty of learning and matching of the features from person images. To
overcome these difficulties, in this work we propose a Pose-driven Deep
Convolutional (PDC) model to learn improved feature extraction and matching
models from end to end. Our deep architecture explicitly leverages the human
part cues to alleviate the pose variations and learn robust feature
representations from both the global image and different local parts. To match
the features from global human body and local body parts, a pose driven feature
weighting sub-network is further designed to learn adaptive feature fusions.
Extensive experimental analyses and results on three popular datasets
demonstrate significant performance improvements of our model over all
published state-of-the-art methods.
Predicting Video Saliency with Object-to-Motion CNN and Two-layer Convolutional LSTM
Authors: Lai Jiang, Mai Xu, Zulin Wang
Date: 2017-09-19 00:00:00
Tasks: Saliency Prediction, Video Saliency Prediction

Over the past few years, deep neural networks (DNNs) have exhibited great
success in predicting the saliency of images. However, there are few works that
apply DNNs to predict the saliency of generic videos. In this paper, we propose
a novel DNN-based video saliency prediction method. Specifically, we establish
a large-scale eye-tracking database of videos (LEDOV), which provides
sufficient data to train the DNN models for predicting video saliency. Through
the statistical analysis of our LEDOV database, we find that human attention is
normally attracted by objects, particularly moving objects or the moving parts
of objects. Accordingly, we propose an object-to-motion convolutional neural
network (OM-CNN) to learn spatio-temporal features for predicting the
intra-frame saliency via exploring the information of both objectness and
object motion. We further find from our database that there exists a temporal
correlation of human attention with a smooth saliency transition across video
frames. Therefore, we develop a two-layer convolutional long short-term memory
(2C-LSTM) network in our DNN-based method, using the extracted features of
OM-CNN as the input. Consequently, the inter-frame saliency maps of videos can
be generated, which consider the transition of attention across video frames.
Finally, the experimental results show that our method advances the
state-of-the-art in video saliency prediction.
Aspect-augmented Adversarial Networks for Domain Adaptation
Authors: Yuan Zhang, Regina Barzilay, Tommi Jaakkola
Date: 2017-01-01 00:00:00
Proceeding: TACL 2017 1
Tasks: Domain Adaptation, General Classification, Sentence, Transfer Learning

We introduce a neural method for transfer learning between two (source and
target) classification tasks or aspects over the same domain. Rather than
training on target labels, we use a few keywords pertaining to source and
target aspects indicating sentence relevance instead of document class labels.
Documents are encoded by learning to embed and softly select relevant sentences
in an aspect-dependent manner. A shared classifier is trained on the source
encoded documents and labels, and applied to target encoded documents. We
ensure transfer through aspect-adversarial training so that encoded documents
are, as sets, aspect-invariant. Experimental results demonstrate that our
approach outperforms different baselines and model variants on two datasets,
yielding an improvement of 27% on a pathology dataset and 5% on a review
dataset.
Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN
Authors: Xiangteng He, Yuxin Peng, Junjie Zhao
Date: 2017-09-25 00:00:00
Tasks: Classification, Fine-Grained Image Classification, General Classification, image-classification, Image Classification

Discriminative localization is essential for fine-grained image
classification task, which devotes to recognizing hundreds of subcategories in
the same basic-level category. Reflecting on discriminative regions of objects,
key differences among different subcategories are subtle and local. Existing
methods generally adopt a two-stage learning framework: The first stage is to
localize the discriminative regions of objects, and the second is to encode the
discriminative features for training classifiers. However, these methods
generally have two limitations: (1) Separation of the two-stage learning is
time-consuming. (2) Dependence on object and parts annotations for
discriminative localization learning leads to heavily labor-consuming labeling.
It is highly challenging to address these two important limitations
simultaneously. Existing methods only focus on one of them. Therefore, this
paper proposes the discriminative localization approach via saliency-guided
Faster R-CNN to address the above two limitations at the same time, and our
main novelties and advantages are: (1) End-to-end network based on Faster R-CNN
is designed to simultaneously localize discriminative regions and encode
discriminative features, which accelerates classification speed. (2)
Saliency-guided localization learning is proposed to localize the
discriminative region automatically, avoiding labor-consuming labeling. Both
are jointly employed to simultaneously accelerate classification speed and
eliminate dependence on object and parts annotations. Comparing with the
state-of-the-art methods on the widely-used CUB-200-2011 dataset, our approach
achieves both the best classification accuracy and efficiency.