pwc-archive/papers-with-abstracts huggingface dataset:
Steganographic Generative Adversarial Networks
Authors: Denis Volkhonskiy, Ivan Nazarov, Evgeny Burnaev
Date: 2017-03-16 00:00:00
Tasks: Steganalysis

Steganography is collection of methods to hide secret information ("payload") within non-secret information "container"). Its counterpart, Steganalysis, is the practice of determining if a message contains a hidden payload, and recovering it if possible. Presence of hidden payloads is typically detected by a binary classifier. In the present study, we propose a new model for generating image-like containers based on Deep Convolutional Generative Adversarial Networks (DCGAN). This approach allows to generate more setganalysis-secure message embedding using standard steganography algorithms. Experiment results demonstrate that the new model successfully deceives the steganography analyzer, and for this reason, can be used in steganographic applications.
Malicious URL Detection using Machine Learning: A Survey
Authors: Doyen Sahoo, Chenghao Liu, Steven C. H. Hoi
Date: 2017-01-25 00:00:00
Tasks: BIG-bench Machine Learning, Survey

Malicious URL, a.k.a. malicious website, is a common and serious threat to cybersecurity. Malicious URLs host unsolicited content (spam, phishing, drive-by exploits, etc.) and lure unsuspecting users to become victims of scams (monetary loss, theft of private information, and malware installation), and cause losses of billions of dollars every year. It is imperative to detect and act on such threats in a timely manner. Traditionally, this detection is done mostly through the usage of blacklists. However, blacklists cannot be exhaustive, and lack the ability to detect newly generated malicious URLs. To improve the generality of malicious URL detectors, machine learning techniques have been explored with increasing attention in recent years. This article aims to provide a comprehensive survey and a structural understanding of Malicious URL Detection techniques using machine learning. We present the formal formulation of Malicious URL Detection as a machine learning task, and categorize and review the contributions of literature studies that addresses different dimensions of this problem (feature representation, algorithm design, etc.). Further, this article provides a timely and comprehensive survey for a range of different audiences, not only for machine learning researchers and engineers in academia, but also for professionals and practitioners in cybersecurity industry, to help them understand the state of the art and facilitate their own research and practical applications. We also discuss practical issues in system design, open research challenges, and point out some important directions for future research.
Using Reinforcement Learning for Demand Response of Domestic Hot Water Buffers: a Real-Life Demonstration
Authors: Oscar De Somer, Ana Soares, Tristan Kuijpers, Koen Vossen, Koen Vanthournout, Fred Spiessens
Date: 2017-03-16 00:00:00
Tasks: Decision Making, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

This paper demonstrates a data-driven control approach for demand response in
real-life residential buildings. The objective is to optimally schedule the
heating cycles of the Domestic Hot Water (DHW) buffer to maximize the
self-consumption of the local photovoltaic (PV) production. A model-based
reinforcement learning technique is used to tackle the underlying sequential
decision-making problem. The proposed algorithm learns the stochastic occupant
behavior, predicts the PV production and takes into account the dynamics of the
system. A real-life experiment with six residential buildings is performed
using this algorithm. The results show that the self-consumption of the PV
production is significantly increased, compared to the default thermostat
control.
High Dimensional Low Rank plus Sparse Matrix Decomposition
Authors: Mostafa Rahmani, George Atia
Date: 2015-02-01 00:00:00
Tasks: Clustering, Small Data Image Classification, Vocal Bursts Intensity Prediction

This paper is concerned with the problem of low rank plus sparse matrix
decomposition for big data. Conventional algorithms for matrix decomposition
use the entire data to extract the low-rank and sparse components, and are
based on optimization problems with complexity that scales with the dimension
of the data, which limits their scalability. Furthermore, existing randomized
approaches mostly rely on uniform random sampling, which is quite inefficient
for many real world data matrices that exhibit additional structures (e.g.
clustering). In this paper, a scalable subspace-pursuit approach that
transforms the decomposition problem to a subspace learning problem is
proposed. The decomposition is carried out using a small data sketch formed
from sampled columns/rows. Even when the data is sampled uniformly at random,
it is shown that the sufficient number of sampled columns/rows is roughly
O(r\mu), where \mu is the coherency parameter and r the rank of the low rank
component. In addition, adaptive sampling algorithms are proposed to address
the problem of column/row sampling from structured data. We provide an analysis
of the proposed method with adaptive sampling and show that adaptive sampling
makes the required number of sampled columns/rows invariant to the distribution
of the data. The proposed approach is amenable to online implementation and an
online scheme is proposed.
Learning Summary Statistic for Approximate Bayesian Computation via Deep Neural Network
Authors: Bai Jiang, Tung-Yu Wu, Charles Zheng, Wing H. Wong
Date: 2015-10-08 00:00:00
Tasks: Computational Efficiency

Approximate Bayesian Computation (ABC) methods are used to approximate
posterior distributions in models with unknown or computationally intractable
likelihoods. Both the accuracy and computational efficiency of ABC depend on
the choice of summary statistic, but outside of special cases where the optimal
summary statistics are known, it is unclear which guiding principles can be
used to construct effective summary statistics. In this paper we explore the
possibility of automating the process of constructing summary statistics by
training deep neural networks to predict the parameters from artificially
generated data: the resulting summary statistics are approximately posterior
means of the parameters. With minimal model-specific tuning, our method
constructs summary statistics for the Ising model and the moving-average model,
which match or exceed theoretically-motivated summary statistics in terms of
the accuracies of the resulting posteriors.
Global and Local Information Based Deep Network for Skin Lesion Segmentation
Authors: Jin Qi, Miao Le, Chunming Li, Ping Zhou
Date: 2017-03-16 00:00:00
Tasks: Data Augmentation, Lesion Segmentation, Skin Lesion Segmentation

With a large influx of dermoscopy images and a growing shortage of
dermatologists, automatic dermoscopic image analysis plays an essential role in
skin cancer diagnosis. In this paper, a new deep fully convolutional neural
network (FCNN) is proposed to automatically segment melanoma out of skin images
by end-to-end learning with only pixels and labels as inputs. Our proposed FCNN
is capable of using both local and global information to segment melanoma by
adopting skipping layers. The public benchmark database consisting of 150
validation images, 600 test images and 2000 training images in the melanoma
detection challenge 2017 at International Symposium Biomedical Imaging 2017 is
used to test the performance of our algorithm. All large size images (for
example, $4000\times 6000$ pixels) are reduced to much smaller images with
$384\times 384$ pixels (more than 10 times smaller). We got and submitted
preliminary results to the challenge without any pre or post processing. The
performance of our proposed method could be further improved by data
augmentation and by avoiding image size reduction.
Neobility at SemEval-2017 Task 1: An Attention-based Sentence Similarity Model
Authors: Wenli Zhuang, Ernie Chang
Date: 2017-03-16 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Cross-Lingual Semantic Textual Similarity, Semantic Textual Similarity, Sentence, Sentence Similarity, STS

This paper describes a neural-network model which performed competitively
(top 6) at the SemEval 2017 cross-lingual Semantic Textual Similarity (STS)
task. Our system employs an attention-based recurrent neural network model that
optimizes the sentence similarity. In this paper, we describe our participation
in the multilingual STS task which measures similarity across English, Spanish,
and Arabic.
Do Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition?
Authors: Pooya Khorrami, Tom Le Paine, Thomas S. Huang
Date: 2015-10-10 00:00:00

Despite being the appearance-based classifier of choice in recent years,
relatively few works have examined how much convolutional neural networks
(CNNs) can improve performance on accepted expression recognition benchmarks
and, more importantly, examine what it is they actually learn. In this work,
not only do we show that CNNs can achieve strong performance, but we also
introduce an approach to decipher which portions of the face influence the
CNN's predictions. First, we train a zero-bias CNN on facial expression data
and achieve, to our knowledge, state-of-the-art performance on two expression
recognition benchmarks: the extended Cohn-Kanade (CK+) dataset and the Toronto
Face Dataset (TFD). We then qualitatively analyze the network by visualizing
the spatial patterns that maximally excite different neurons in the
convolutional layers and show how they resemble Facial Action Units (FAUs).
Finally, we use the FAU labels provided in the CK+ dataset to verify that the
FAUs observed in our filter visualizations indeed align with the subject's
facial movements.
Sequential Local Learning for Latent Graphical Models
Authors: Sejun Park, Eunho Yang, Jinwoo Shin
Date: 2017-03-12 00:00:00
Tasks: Novel Concepts

Learning parameters of latent graphical models (GM) is inherently much harder
than that of no-latent ones since the latent variables make the corresponding
log-likelihood non-concave. Nevertheless, expectation-maximization schemes are
popularly used in practice, but they are typically stuck in local optima. In
the recent years, the method of moments have provided a refreshing angle for
resolving the non-convex issue, but it is applicable to a quite limited class
of latent GMs. In this paper, we aim for enhancing its power via enlarging such
a class of latent GMs. To this end, we introduce two novel concepts, coined
marginalization and conditioning, which can reduce the problem of learning a
larger GM to that of a smaller one. More importantly, they lead to a sequential
learning framework that repeatedly increases the learning portion of given
latent GM, and thus covers a significantly broader and more complicated class
of loopy latent GMs which include convolutional and random regular models.
Semantic Change Detection with Hypermaps
Authors: Teppei Suzuki, Soma Shirakabe, Yudai Miyashita, Akio Nakamura, Yutaka Satoh, Hirokatsu Kataoka
Date: 2016-04-26 00:00:00
Tasks: Change Detection, Semantic Segmentation

Change detection is the study of detecting changes between two different
images of a scene taken at different times. By the detected change areas,
however, a human cannot understand how different the two images. Therefore, a
semantic understanding is required in the change detection research such as
disaster investigation. The paper proposes the concept of semantic change
detection, which involves intuitively inserting semantic meaning into detected
change areas. We mainly focus on the novel semantic segmentation in addition to
a conventional change detection approach. In order to solve this problem and
obtain a high-level of performance, we propose an improvement to the
hypercolumns representation, hereafter known as hypermaps, which effectively
uses convolutional maps obtained from convolutional neural networks (CNNs). We
also employ multi-scale feature representation captured by different image
patches. We applied our method to the TSUNAMI Panoramic Change Detection
dataset, and re-annotated the changed areas of the dataset via semantic
classes. The results show that our multi-scale hypermaps provided outstanding
performance on the re-annotated TSUNAMI dataset.
Refining Image Categorization by Exploiting Web Images and General Corpus
Authors: Yazhou Yao, Jian Zhang, Fumin Shen, Xian-Sheng Hua, Wankou Yang, Zhenmin Tang
Date: 2017-03-16 00:00:00
Tasks: Image Categorization

Studies show that refining real-world categories into semantic subcategories
contributes to better image modeling and classification. Previous image
sub-categorization work relying on labeled images and WordNet's hierarchy is
not only labor-intensive, but also restricted to classify images into NOUN
subcategories. To tackle these problems, in this work, we exploit general
corpus information to automatically select and subsequently classify web images
into semantic rich (sub-)categories. The following two major challenges are
well studied: 1) noise in the labels of subcategories derived from the general
corpus; 2) noise in the labels of images retrieved from the web. Specifically,
we first obtain the semantic refinement subcategories from the text perspective
and remove the noise by the relevance-based approach. To suppress the search
error induced noisy images, we then formulate image selection and classifier
learning as a multi-class multi-instance learning problem and propose to solve
the employed problem by the cutting-plane algorithm. The experiments show
significant performance gains by using the generated data of our way on both
image categorization and sub-categorization tasks. The proposed approach also
consistently outperforms existing weakly supervised and web-supervised
approaches.
Approximate Counting, the Lovasz Local Lemma and Inference in Graphical Models
Authors: Ankur Moitra
Date: 2016-10-14 00:00:00
Tasks: LEMMA

In this paper we introduce a new approach for approximately counting in
bounded degree systems with higher-order constraints. Our main result is an
algorithm to approximately count the number of solutions to a CNF formula
$\Phi$ when the width is logarithmic in the maximum degree. This closes an
exponential gap between the known upper and lower bounds.
  Moreover our algorithm extends straightforwardly to approximate sampling,
which shows that under Lov\'asz Local Lemma-like conditions it is not only
possible to find a satisfying assignment, it is also possible to generate one
approximately uniformly at random from the set of all satisfying assignments.
Our approach is a significant departure from earlier techniques in approximate
counting, and is based on a framework to bootstrap an oracle for computing
marginal probabilities on individual variables. Finally, we give an application
of our results to show that it is algorithmically possible to sample from the
posterior distribution in an interesting class of graphical models.
Legal Question Answering using Ranking SVM and Deep Convolutional Neural Network
Authors: Phong-Khac Do, Huy-Tien Nguyen, Chien-Xuan Tran, Minh-Tien Nguyen, Minh-Le Nguyen
Date: 2017-03-16 00:00:00
Tasks: Information Retrieval, Question Answering, Retrieval

This paper presents a study of employing Ranking SVM and Convolutional Neural
Network for two missions: legal information retrieval and question answering in
the Competition on Legal Information Extraction/Entailment. For the first task,
our proposed model used a triple of features (LSI, Manhattan, Jaccard), and is
based on paragraph level instead of article level as in previous studies. In
fact, each single-paragraph article corresponds to a particular paragraph in a
huge multiple-paragraph article. For the legal question answering task,
additional statistical features from information retrieval task integrated into
Convolutional Neural Network contribute to higher accuracy.
End-to-end optimization of goal-driven and visually grounded dialogue systems
Authors: Florian Strub, Harm de Vries, Jeremie Mary, Bilal Piot, Aaron Courville, Olivier Pietquin
Date: 2017-03-15 00:00:00
Tasks: Decoder, Deep Reinforcement Learning, Dialogue Management, Management, Reinforcement Learning, Visual Question Answering (VQA)

End-to-end design of dialogue systems has recently become a popular research
topic thanks to powerful tools such as encoder-decoder architectures for
sequence-to-sequence learning. Yet, most current approaches cast human-machine
dialogue management as a supervised learning problem, aiming at predicting the
next utterance of a participant given the full history of the dialogue. This
vision is too simplistic to render the intrinsic planning problem inherent to
dialogue as well as its grounded nature, making the context of a dialogue
larger than the sole history. This is why only chit-chat and question answering
tasks have been addressed so far using end-to-end architectures. In this paper,
we introduce a Deep Reinforcement Learning method to optimize visually grounded
task-oriented dialogues, based on the policy gradient algorithm. This approach
is tested on a dataset of 120k dialogues collected through Mechanical Turk and
provides encouraging results at solving both the problem of generating natural
dialogues and the task of discovering a specific object in a complex picture.
Large Scale Evolution of Convolutional Neural Networks Using Volunteer Computing
Authors: Travis Desell
Date: 2017-03-15 00:00:00
Tasks: Distributed Computing, L2 Regularization

This work presents a new algorithm called evolutionary exploration of
augmenting convolutional topologies (EXACT), which is capable of evolving the
structure of convolutional neural networks (CNNs). EXACT is in part modeled
after the neuroevolution of augmenting topologies (NEAT) algorithm, with
notable exceptions to allow it to scale to large scale distributed computing
environments and evolve networks with convolutional filters. In addition to
multithreaded and MPI versions, EXACT has been implemented as part of a BOINC
volunteer computing project, allowing large scale evolution. During a period of
two months, over 4,500 volunteered computers on the Citizen Science Grid
trained over 120,000 CNNs and evolved networks reaching 98.32% test data
accuracy on the MNIST handwritten digits dataset. These results are even
stronger as the backpropagation strategy used to train the CNNs was fairly
rudimentary (ReLU units, L2 regularization and Nesterov momentum) and these
were initial test runs done without refinement of the backpropagation
hyperparameters. Further, the EXACT evolutionary strategy is independent of the
method used to train the CNNs, so they could be further improved by advanced
techniques like elastic distortions, pretraining and dropout. The evolved
networks are also quite interesting, showing "organic" structures and
significant differences from standard human designed architectures.
Improving Policy Gradient by Exploring Under-appreciated Rewards
Authors: Ofir Nachum, Mohammad Norouzi, Dale Schuurmans
Date: 2016-11-28 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

This paper presents a novel form of policy gradient for model-free
reinforcement learning (RL) with improved exploration properties. Current
policy-based methods use entropy regularization to encourage undirected
exploration of the reward landscape, which is ineffective in high dimensional
spaces with sparse rewards. We propose a more directed exploration strategy
that promotes exploration of under-appreciated reward regions. An action
sequence is considered under-appreciated if its log-probability under the
current policy under-estimates its resulting reward. The proposed exploration
strategy is easy to implement, requiring small modifications to an
implementation of the REINFORCE algorithm. We evaluate the approach on a set of
algorithmic tasks that have long challenged RL methods. Our approach reduces
hyper-parameter sensitivity and demonstrates significant improvements over
baseline methods. Our algorithm successfully solves a benchmark multi-digit
addition task and generalizes to long sequences. This is, to our knowledge, the
first time that a pure RL method has solved addition using only reward
feedback.
Aggregation of Classifiers: A Justifiable Information Granularity Approach
Authors: Tien Thanh Nguyen, Xuan Cuong Pham, Alan Wee-Chung Liew, Witold Pedrycz
Date: 2017-03-15 00:00:00
Tasks: Diversity

In this study, we introduce a new approach to combine multi-classifiers in an
ensemble system. Instead of using numeric membership values encountered in
fixed combining rules, we construct interval membership values associated with
each class prediction at the level of meta-data of observation by using
concepts of information granules. In the proposed method, uncertainty
(diversity) of findings produced by the base classifiers is quantified by
interval-based information granules. The discriminative decision model is
generated by considering both the bounds and the length of the obtained
intervals. We select ten and then fifteen learning algorithms to build a
heterogeneous ensemble system and then conducted the experiment on a number of
UCI datasets. The experimental results demonstrate that the proposed approach
performs better than the benchmark algorithms including six fixed combining
methods, one trainable combining method, AdaBoost, Bagging, and Random
Subspace.
Machine learning approach for early detection of autism by combining questionnaire and home video screening
Authors: Halim Abbas, Ford Garberson, Eric Glover, Dennis P. Wall
Date: 2017-03-15 00:00:00
Tasks: BIG-bench Machine Learning, Feature Engineering, feature selection

Existing screening tools for early detection of autism are expensive,
cumbersome, time-intensive, and sometimes fall short in predictive value. In
this work, we apply Machine Learning (ML) to gold standard clinical data
obtained across thousands of children at risk for autism spectrum disorders to
create a low-cost, quick, and easy to apply autism screening tool that performs
as well or better than most widely used standardized instruments. This new tool
combines two screening methods into a single assessment, one based on short,
structured parent-report questionnaires and the other on tagging key behaviors
from short, semi-structured home videos of children. To overcome the scarcity,
sparsity, and imbalance of training data, we apply creative feature selection,
feature engineering, and novel feature encoding techniques. We allow for
inconclusive determination where appropriate in order to boost screening
accuracy when conclusive. We demonstrate a significant accuracy improvement
over standard screening tools in a clinical study sample of 162 children.
3D Vision Guided Robotic Charging Station for Electric and Plug-in Hybrid Vehicles
Authors: Justinas Miseikis, Matthias Ruther, Bernhard Walzel, Mario Hirz, Helmut Brunner
Date: 2017-03-15 00:00:00
Tasks: Motion Planning

Electric vehicles (EVs) and plug-in hybrid vehicles (PHEVs) are rapidly
gaining popularity on our roads. Besides a comparatively high purchasing price,
the main two problems limiting their use are the short driving range and
inconvenient charging process. In this paper we address the following by
presenting an automatic robot-based charging station with 3D vision guidance
for plugging and unplugging the charger. First of all, the whole system concept
consisting of a 3D vision system, an UR10 robot and a charging station is
presented. Then we show the shape-based matching methods used to successfully
identify and get the exact pose of the charging port. The same approach is used
to calibrate the camera-robot system by using just known structure of the
connector plug and no additional markers. Finally, a three-step robot motion
planning procedure for plug-in is presented and functionality is demonstrated
in a series of successful experiments.