pwc-archive/papers-with-abstracts huggingface dataset:
A Robust Background Initialization Algorithm with Superpixel Motion Detection
Authors: Zhe Xu, Biao Min, Ray C. C. Cheung
Date: 2018-05-17 00:00:00
Tasks: Clustering, Motion Detection, Superpixels

Scene background initialization allows the recovery of a clear image without
foreground objects from a video sequence, which is generally the first step in
many computer vision and video processing applications. The process may be
strongly affected by some challenges such as illumination changes, foreground
cluttering, intermittent movement, etc. In this paper, a robust background
initialization approach based on superpixel motion detection is proposed. Both
spatial and temporal characteristics of frames are adopted to effectively
eliminate foreground objects. A subsequence with stable illumination condition
is first selected for background estimation. Images are segmented into
superpixels to preserve spatial texture information and foreground objects are
eliminated by superpixel motion filtering process. A low-complexity
density-based clustering is then performed to generate reliable background
candidates for final background determination. The approach has been evaluated
on SBMnet dataset and it achieves a performance superior or comparable to other
state-of-the-art works with faster processing speed. Moreover, in those complex
and dynamic categories, the algorithm produces the best results showing the
robustness against very challenging scenarios.
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
Authors: Samet Akcay, Amir Atapour-Abarghouei, Toby P. Breckon
Date: 2018-05-17 00:00:00
Tasks: Anomaly Detection, Decoder, Generative Adversarial Network, Semi-supervised Anomaly Detection, Supervised Anomaly Detection

Anomaly detection is a classical problem in computer vision, namely the
determination of the normal from the abnormal when datasets are highly biased
towards one class (normal) due to the insufficient sample size of the other
class (abnormal). While this can be addressed as a supervised learning problem,
a significantly more challenging problem is that of detecting the
unknown/unseen anomaly case that takes us instead into the space of a
one-class, semi-supervised learning paradigm. We introduce such a novel anomaly
detection model, by using a conditional generative adversarial network that
jointly learns the generation of high-dimensional image space and the inference
of latent space. Employing encoder-decoder-encoder sub-networks in the
generator network enables the model to map the input image to a lower dimension
vector, which is then used to reconstruct the generated output image. The use
of the additional encoder network maps this generated image to its latent
representation. Minimizing the distance between these images and the latent
vectors during training aids in learning the data distribution for the normal
samples. As a result, a larger distance metric from this learned data
distribution at inference time is indicative of an outlier from that
distribution - an anomaly. Experimentation over several benchmark datasets,
from varying domains, shows the model efficacy and superiority over previous
state-of-the-art approaches.
Language Expansion In Text-Based Games
Authors: Ghulam Ahmed Ansari, Sagar J P, Sarath Chandar, Balaraman Ravindran
Date: 2018-05-17 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), text-based games

Text-based games are suitable test-beds for designing agents that can learn
by interaction with the environment in the form of natural language text. Very
recently, deep reinforcement learning based agents have been successfully
applied for playing text-based games. In this paper, we explore the possibility
of designing a single agent to play several text-based games and of expanding
the agent's vocabulary using the vocabulary of agents trained for multiple
games. To this extent, we explore the application of recently proposed policy
distillation method for video games to the text-based game setting. We also use
text-based games as a test-bed to analyze and hence understand policy
distillation approach in detail.
PDNet: Semantic Segmentation integrated with a Primal-Dual Network for Document binarization
Authors: Kalyan Ram Ayyalasomayajula, Filip Malmberg, Anders Brun
Date: 2018-01-26 00:00:00
Tasks: Binarization, Semantic Segmentation

Binarization of digital documents is the task of classifying each pixel in an
image of the document as belonging to the background (parchment/paper) or
foreground (text/ink). Historical documents are often subjected to
degradations, that make the task challenging. In the current work a deep neural
network architecture is proposed that combines a fully convolutional network
with an unrolled primal-dual network that can be trained end-to-end to achieve
state of the art binarization on four out of seven datasets. Document
binarization is formulated as an energy minimization problem. A fully
convolutional neural network is trained for semantic segmentation of pixels
that provides labeling cost associated with each pixel. This cost estimate is
refined along the edges to compensate for any over or under estimation of the
foreground class using a primal-dual approach. We provide necessary overview on
proximal operator that facilitates theoretical underpinning required to train a
primal-dual network using a gradient descent algorithm. Numerical instabilities
encountered due to the recurrent nature of primal-dual approach are handled. We
provide experimental results on document binarization competition dataset along
with network changes and hyperparameter tuning required for stability and
performance of the network. The network when pre-trained on synthetic dataset
performs better as per the competition metrics.
Classifying medical relations in clinical text via convolutional neural networks
Authors: Bin He, Yi Guan, Rui Dai
Date: 2018-05-17 00:00:00
Tasks: Classification, General Classification, Relation, Relation Classification

Deep learning research on relation classification has achieved solid
performance in the general domain. This study proposes a convolutional neural
network (CNN) architecture with a multi-pooling operation for medical relation
classification on clinical records and explores a loss function with a
category-level constraint matrix. Experiments using the 2010 i2b2/VA relation
corpus demonstrate these models, which do not depend on any external features,
outperform previous single-model methods and our best model is competitive with
the existing ensemble-based method.
Evolutionary RL for Container Loading
Authors: S Saikia, R Verma, P Agarwal, G Shroff, L Vig, A Srinivasan
Date: 2018-05-17 00:00:00
Tasks: Combinatorial Optimization, Reinforcement Learning, Reinforcement Learning (RL)

Loading the containers on the ship from a yard, is an impor- tant part of
port operations. Finding the optimal sequence for the loading of containers, is
known to be computationally hard and is an example of combinatorial
optimization, which leads to the application of simple heuristics in practice.
In this paper, we propose an approach which uses a mix of Evolutionary
Strategies and Reinforcement Learning (RL) tech- niques to find an
approximation of the optimal solution. The RL based agent uses the Policy
Gradient method, an evolutionary reward strategy and a Pool of good
(not-optimal) solutions to find the approximation. We find that the RL agent
learns near-optimal solutions that outperforms the heuristic solutions. We also
observe that the RL agent assisted with a pool generalizes better for unseen
problems than an RL agent without a pool. We present our results on synthetic
data as well as on subsets of real-world problems taken from container
terminal. The results validate that our approach does comparatively better than
the heuristics solutions available, and adapts to unseen problems better.
Translation of Algorithmic Descriptions of Discrete Functions to SAT with Applications to Cryptanalysis Problems
Authors: Alexander Semenov, Ilya Otpuschennikov, Irina Gribanova, Oleg Zaikin, Stepan Kochemazov
Date: 2018-05-17 00:00:00
Tasks: Cryptanalysis, Translation

In the present paper, we propose a technology for translating algorithmic descriptions of discrete functions to SAT. The proposed technology is aimed at applications in algebraic cryptanalysis. We describe how cryptanalysis problems are reduced to SAT in such a way that it should be perceived as natural by the cryptographic community. In~the theoretical part of the paper we justify the main principles of general reduction to SAT for discrete functions from a class containing the majority of functions employed in cryptography. Then, we describe the Transalg software tool developed based on these principles with SAT-based cryptanalysis specifics in mind. We demonstrate the results of applications of Transalg to construction of a number of attacks on various cryptographic functions. Some of the corresponding attacks are state of the art. We compare the functional capabilities of the proposed tool with that of other domain-specific software tools which can be used to reduce cryptanalysis problems to SAT, and also with the CBMC system widely employed in symbolic verification. The paper also presents vast experimental data, obtained using the SAT solvers that took first places at the SAT competitions in the recent several years.
Single Shot Active Learning using Pseudo Annotators
Authors: Yazhou Yang, Marco Loog
Date: 2018-05-17 00:00:00
Tasks: Active Learning

Standard myopic active learning assumes that human annotations are always
obtainable whenever new samples are selected. This, however, is unrealistic in
many real-world applications where human experts are not readily available at
all times. In this paper, we consider the single shot setting: all the required
samples should be chosen in a single shot and no human annotation can be
exploited during the selection process. We propose a new method, Active
Learning through Random Labeling (ALRL), which substitutes single human
annotator for multiple, what we will refer to as, pseudo annotators. These
pseudo annotators always provide uniform and random labels whenever new
unlabeled samples are queried. This random labeling enables standard active
learning algorithms to also exhibit the exploratory behavior needed for single
shot active learning. The exploratory behavior is further enhanced by selecting
the most representative sample via minimizing nearest neighbor distance between
unlabeled samples and queried samples. Experiments on real-world datasets
demonstrate that the proposed method outperforms several state-of-the-art
approaches.
ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R
Authors: Marvin N. Wright, Andreas Ziegler
Date: 2015-08-18 00:00:00
Tasks: General Classification, regression

We introduce the C++ application and R package ranger. The software is a fast
implementation of random forests for high dimensional data. Ensembles of
classification, regression and survival trees are supported. We describe the
implementation, provide examples, validate the package with a reference
implementation, and compare runtime and memory usage with other
implementations. The new software proves to scale best with the number of
features, samples, trees, and features tried for splitting. Finally, we show
that ranger is the fastest and most memory efficient implementation of random
forests to analyze data on the scale of a genome-wide association study.
Structural Feature Selection for Event Logs
Authors: Markku Hinkka, Teemu Lehto, Keijo Heljanko, Alexander Jung
Date: 2017-10-08 00:00:00
Tasks: BIG-bench Machine Learning, Classification, feature selection, General Classification

We consider the problem of classifying business process instances based on
structural features derived from event logs. The main motivation is to provide
machine learning based techniques with quick response times for interactive
computer assisted root cause analysis. In particular, we create structural
features from process mining such as activity and transition occurrence counts,
and ordering of activities to be evaluated as potential features for
classification. We show that adding such structural features increases the
amount of information thus potentially increasing classification accuracy.
However, there is an inherent trade-off as using too many features leads to too
long run-times for machine learning classification models. One way to improve
the machine learning algorithms' run-time is to only select a small number of
features by a feature selection algorithm. However, the run-time required by
the feature selection algorithm must also be taken into account. Also, the
classification accuracy should not suffer too much from the feature selection.
The main contributions of this paper are as follows: First, we propose and
compare six different feature selection algorithms by means of an experimental
setup comparing their classification accuracy and achievable response times.
Second, we discuss the potential use of feature selection results for computer
assisted root cause analysis as well as the properties of different types of
structural features in the context of feature selection.
Deep-learning Based Modeling of Fault Detachment Stability for Power Grid
Authors: Haotian Cui, Xianggen Liu, Yanhao Huang
Date: 2018-05-17 00:00:00
Tasks: Deep Learning

The project intends to model the stability of power system with a deep
learning algorithm to the problem, aiming to delay the removal of the fault.
The so-called "fail-delay cut-off" refers to the occurrence of N-1 backup
protection action on the backbone network of the system, resulting in longer
time for the removal of the fault. In practice, through the analysis and
calculation of a large number of online data, we have found that the N-1
failure system of the main protection action will not be unstable, which is
also a guarantee of the operation mode arrangement. In the case of the N-1
backup protection action, there is an approximately 2.5% probability that the
system will be destabilized. Therefore, research is needed to improve the
operating arrangement.
Detecting Adversarial Samples for Deep Neural Networks through Mutation Testing
Authors: Jingyi Wang, Jun Sun, Peixin Zhang, Xinyu Wang
Date: 2018-05-14 00:00:00

Recently, it has been shown that deep neural networks (DNN) are subject to
attacks through adversarial samples. Adversarial samples are often crafted
through adversarial perturbation, i.e., manipulating the original sample with
minor modifications so that the DNN model labels the sample incorrectly. Given
that it is almost impossible to train perfect DNN, adversarial samples are
shown to be easy to generate. As DNN are increasingly used in safety-critical
systems like autonomous cars, it is crucial to develop techniques for defending
such attacks. Existing defense mechanisms which aim to make adversarial
perturbation challenging have been shown to be ineffective. In this work, we
propose an alternative approach. We first observe that adversarial samples are
much more sensitive to perturbations than normal samples. That is, if we impose
random perturbations on a normal and an adversarial sample respectively, there
is a significant difference between the ratio of label change due to the
perturbations. Observing this, we design a statistical adversary detection
algorithm called nMutant (inspired by mutation testing from software
engineering community). Our experiments show that nMutant effectively detects
most of the adversarial samples generated by recently proposed attacking
methods. Furthermore, we provide an error bound with certain statistical
significance along with the detection.
A Complementary Tracking Model with Multiple Features
Authors: Peng Gao, Yipeng Ma, Chao Li, Ke Song, Fei Wang, Liyi Xiao
Date: 2018-04-20 00:00:00
Tasks: model, Visual Tracking

Discriminative Correlation Filters based tracking algorithms exploiting
conventional handcrafted features have achieved impressive results both in
terms of accuracy and robustness. Template handcrafted features have shown
excellent performance, but they perform poorly when the appearance of target
changes rapidly such as fast motions and fast deformations. In contrast,
statistical handcrafted features are insensitive to fast states changes, but
they yield inferior performance in the scenarios of illumination variations and
background clutters. In this work, to achieve an efficient tracking
performance, we propose a novel visual tracking algorithm, named MFCMT, based
on a complementary ensemble model with multiple features, including Histogram
of Oriented Gradients (HOGs), Color Names (CNs) and Color Histograms (CHs).
Additionally, to improve tracking results and prevent targets drift, we
introduce an effective fusion method by exploiting relative entropy to coalesce
all basic response maps and get an optimal response. Furthermore, we suggest a
simple but efficient update strategy to boost tracking performance.
Comprehensive evaluations are conducted on two tracking benchmarks demonstrate
and the experimental results demonstrate that our method is competitive with
numerous state-of-the-art trackers. Our tracker achieves impressive performance
with faster speed on these benchmarks.
Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks
Authors: Florian Ziel, Rafal Weron
Date: 2018-05-17 00:00:00
Tasks: Variable Selection

We conduct an extensive empirical study on short-term electricity price
forecasting (EPF) to address the long-standing question if the optimal model
structure for EPF is univariate or multivariate. We provide evidence that
despite a minor edge in predictive performance overall, the multivariate
modeling framework does not uniformly outperform the univariate one across all
12 considered datasets, seasons of the year or hours of the day, and at times
is outperformed by the latter. This is an indication that combining advanced
structures or the corresponding forecasts from both modeling approaches can
bring a further improvement in forecasting accuracy. We show that this indeed
can be the case, even for a simple averaging scheme involving only two models.
Finally, we also analyze variable selection for the best performing
high-dimensional lasso-type models, thus provide guidelines to structuring
better performing forecasting model designs.
Extrapolation in NLP
Authors: Jeff Mitchell, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel
Date: 2018-05-17 00:00:00
Proceeding: WS 2018 6

We argue that extrapolation to examples outside the training space will often
be easier for models that capture global structures, rather than just maximise
their local fit to the training data. We show that this is true for two popular
models: the Decomposable Attention Model and word2vec.
Zero-Shot Object Detection by Hybrid Region Embedding
Authors: Berkan Demirel, Ramazan Gokberk Cinbis, Nazli Ikizler-Cinbis
Date: 2018-05-16 00:00:00
Tasks: Object, object-detection, Object Detection, Zero-Shot Object Detection

Object detection is considered as one of the most challenging problems in
computer vision, since it requires correct prediction of both classes and
locations of objects in images. In this study, we define a more difficult
scenario, namely zero-shot object detection (ZSD) where no visual training data
is available for some of the target object classes. We present a novel approach
to tackle this ZSD problem, where a convex combination of embeddings are used
in conjunction with a detection framework. For evaluation of ZSD methods, we
propose a simple dataset constructed from Fashion-MNIST images and also a
custom zero-shot split for the Pascal VOC detection challenge. The experimental
results suggest that our method yields promising results for ZSD.
Hungarian Layer: Logics Empowered Neural Architecture
Authors: Han Xiao, Yidong Chen, Xiaodong Shi
Date: 2017-12-07 00:00:00
Tasks: Sentence

Neural architecture is a purely numeric framework, which fits the data as a
continuous function. However, lacking of logic flow (e.g. \textit{if, for,
while}), traditional algorithms (e.g. \textit{Hungarian algorithm, A$^*$
searching, decision tress algorithm}) could not be embedded into this paradigm,
which limits the theories and applications. In this paper, we reform the
calculus graph as a dynamic process, which is guided by logic flow. Within our
novel methodology, traditional algorithms could empower numerical neural
network. Specifically, regarding the subject of sentence matching, we
reformulate this issue as the form of task-assignment, which is solved by
Hungarian algorithm. First, our model applies BiLSTM to parse the sentences.
Then Hungarian layer aligns the matching positions. Last, we transform the
matching results for soft-max regression by another BiLSTM. Extensive
experiments show that our model outperforms other state-of-the-art baselines
substantially.
Joint direct estimation of 3D geometry and 3D motion using spatio temporal gradients
Authors: Francisco Barranco, Cornelia Fermüller, Yiannis Aloimonos, Eduardo Ros
Date: 2018-05-17 00:00:00
Tasks: 3D geometry, Motion Estimation, Optical Flow Estimation

Conventional image motion based structure from motion methods first compute
optical flow, then solve for the 3D motion parameters based on the epipolar
constraint, and finally recover the 3D geometry of the scene. However, errors
in optical flow due to regularization can lead to large errors in 3D motion and
structure. This paper investigates whether performance and consistency can be
improved by avoiding optical flow estimation in the early stages of the
structure from motion pipeline, and it proposes a new direct method based on
image gradients (normal flow) only. The main idea lies in a reformulation of
the positive-depth constraint, which allows the use of well-known minimization
techniques to solve for 3D motion. The 3D motion estimate is then refined and
structure estimated adding a regularization based on depth. Experimental
comparisons on standard synthetic datasets and the real-world driving benchmark
dataset KITTI using three different optic flow algorithms show that the method
achieves better accuracy in all but one case. Furthermore, it outperforms
existing normal flow based 3D motion estimation techniques. Finally, the
recovered 3D geometry is shown to be also very accurate.
Testing for Conditional Mean Independence with Covariates through Martingale Difference Divergence
Authors: Ze Jin, Xiaohan Yan, David S. Matteson
Date: 2018-05-17 00:00:00

As a crucial problem in statistics is to decide whether additional variables
are needed in a regression model. We propose a new multivariate test to
investigate the conditional mean independence of Y given X conditioning on some
known effect Z, i.e., E(Y|X, Z) = E(Y|Z). Assuming that E(Y|Z) and Z are
linearly related, we reformulate an equivalent notion of conditional mean
independence through transformation, which is approximated in practice. We
apply the martingale difference divergence (Shao and Zhang, 2014) to measure
conditional mean dependence, and show that the estimation error from
approximation is negligible, as it has no impact on the asymptotic distribution
of the test statistic under some regularity assumptions. The implementation of
our test is demonstrated by both simulations and a financial data example.