pwc-archive/papers-with-abstracts huggingface dataset:
High Voltage Insulator Surface Evaluation Using Image Processing
Authors: Damira Pernebayeva, Mehdi Bagheri, Alex Pappachen James
Date: 2017-08-19 00:00:00
Tasks: Vocal Bursts Intensity Prediction

High voltage insulators are widely deployed in power systems to isolate the
live- and dead-part of overhead lines as well as to support the power line
conductors mechanically. Permanent, secure and safe operation of power
transmission lines require that the high voltage insulators are inspected and
monitor, regularly. Severe environment conditions will influence insulator
surface and change creepage distance. Consequently, power utilities and
transmission companies face significant problem in operation due to insulator
damage or contamination. In this study, a new technique is developed for
real-time inspection of insulator and estimating the snow, ice and water over
the insulator surface which can be a potential risk of operation breakdown. To
examine the proposed system, practical experiment is conducted using ceramic
insulator for capturing the images with snow, ice and wet surface conditions.
Gabor and Standard deviation filters are utilized for image feature extraction.
The best achieved recognition accuracy rate was 87% using statistical approach
the Standard deviation.
Visual Forecasting by Imitating Dynamics in Natural Sequences
Authors: Kuo-Hao Zeng, William B. Shen, De-An Huang, Min Sun, Juan Carlos Niebles
Date: 2017-08-19 00:00:00
Proceeding: ICCV 2017 10
Tasks: Action Anticipation, Reinforcement Learning

We introduce a general framework for visual forecasting, which directly
imitates visual sequences without additional supervision. As a result, our
model can be applied at several semantic levels and does not require any domain
knowledge or handcrafted features. We achieve this by formulating visual
forecasting as an inverse reinforcement learning (IRL) problem, and directly
imitate the dynamics in natural sequences from their raw pixel values. The key
challenge is the high-dimensional and continuous state-action space that
prohibits the application of previous IRL algorithms. We address this
computational bottleneck by extending recent progress in model-free imitation
with trainable deep feature representations, which (1) bypasses the exhaustive
state-action pair visits in dynamic programming by using a dual formulation and
(2) avoids explicit state sampling at gradient computation using a deep feature
reparametrization. This allows us to apply IRL at scale and directly imitate
the dynamics in high-dimensional continuous visual sequences from the raw pixel
values. We evaluate our approach at three different level-of-abstraction, from
low level pixels to higher level semantics: future frame generation, action
anticipation, visual story forecasting. At all levels, our approach outperforms
existing methods.
Applying Deep Bidirectional LSTM and Mixture Density Network for Basketball Trajectory Prediction
Authors: Yu Zhao, Rennong Yang, Guillaume Chevalier, Rajiv Shah, Rob Romijnders
Date: 2017-08-19 00:00:00
Tasks: Time Series, Time Series Analysis, Trajectory Prediction

Data analytics helps basketball teams to create tactics. However, manual data
collection and analytics are costly and ineffective. Therefore, we applied a
deep bidirectional long short-term memory (BLSTM) and mixture density network
(MDN) approach. This model is not only capable of predicting a basketball
trajectory based on real data, but it also can generate new trajectory samples.
It is an excellent application to help coaches and players decide when and
where to shoot. Its structure is particularly suitable for dealing with time
series problems. BLSTM receives forward and backward information at the same
time, while stacking multiple BLSTMs further increases the learning ability of
the model. Combined with BLSTMs, MDN is used to generate a multi-modal
distribution of outputs. Thus, the proposed model can, in principle, represent
arbitrary conditional probability distributions of output variables. We tested
our model with two experiments on three-pointer datasets from NBA SportVu data.
In the hit-or-miss classification experiment, the proposed model outperformed
other models in terms of the convergence speed and accuracy. In the trajectory
generation experiment, eight model-generated trajectories at a given time
closely matched real trajectories.
Analysing Soccer Games with Clustering and Conceptors
Authors: Olivia Michael, Oliver Obst, Falk Schmidsberger, Frieder Stolzenburg
Date: 2017-08-19 00:00:00
Tasks: Clustering

We present a new approach for identifying situations and behaviours, which we
call "moves", from soccer games in the 2D simulation league. Being able to
identify key situations and behaviours are useful capabilities for analysing
soccer matches, anticipating opponent behaviours to aid selection of
appropriate tactics, and also as a prerequisite for automatic learning of
behaviours and policies. To support a wide set of strategies, our goal is to
identify situations from data, in an unsupervised way without making use of
pre-defined soccer specific concepts such as "pass" or "dribble". The recurrent
neural networks we use in our approach act as a high-dimensional projection of
the recent history of a situation on the field. Similar situations, i.e., with
similar histories, are found by clustering of network states. The same networks
are also used to learn so-called conceptors, that are lower-dimensional
manifolds that describe trajectories through a high-dimensional state space
that enable situation-specific predictions from the same neural network. With
the proposed approach, we can segment games into sequences of situations that
are learnt in an unsupervised way, and learn conceptors that are useful for the
prediction of the near future of the respective situation.
BetaRun Soccer Simulation League Team: Variety, Complexity, and Learning
Authors: Olivia Michael, Oliver Obst
Date: 2017-03-12 00:00:00
Tasks: BIG-bench Machine Learning

RoboCup offers a set of benchmark problems for Artificial Intelligence in
form of official world championships since 1997. The most tactical advanced and
richest in terms of behavioural complexity of these is the 2D Soccer Simulation
League, a simulated robotic soccer competition. BetaRun is a new attempt
combining both machine learning and manual programming approaches, with the
ultimate goal to arrive at a team that is trained entirely from observing and
playing games, and a new development based on agent2D.
Discovery of Visual Semantics by Unsupervised and Self-Supervised Representation Learning
Authors: Gustav Larsson
Date: 2017-08-19 00:00:00
Tasks: Clustering, Colorization, Image Colorization, Representation Learning, Semantic Segmentation, Unsupervised Pre-training

The success of deep learning in computer vision is rooted in the ability of
deep networks to scale up model complexity as demanded by challenging visual
tasks. As complexity is increased, so is the need for large amounts of labeled
data to train the model. This is associated with a costly human annotation
effort. To address this concern, with the long-term goal of leveraging the
abundance of cheap unlabeled data, we explore methods of unsupervised
"pre-training." In particular, we propose to use self-supervised automatic
image colorization.
  We show that traditional methods for unsupervised learning, such as
layer-wise clustering or autoencoders, remain inferior to supervised
pre-training. In search for an alternative, we develop a fully automatic image
colorization method. Our method sets a new state-of-the-art in revitalizing old
black-and-white photography, without requiring human effort or expertise.
Additionally, it gives us a method for self-supervised representation learning.
In order for the model to appropriately re-color a grayscale object, it must
first be able to identify it. This ability, learned entirely self-supervised,
can be used to improve other visual tasks, such as classification and semantic
segmentation. As a future direction for self-supervision, we investigate if
multiple proxy tasks can be combined to improve generalization. This turns out
to be a challenging open problem. We hope that our contributions to this
endeavor will provide a foundation for future efforts in making
self-supervision compete with supervised pre-training.
Reinforcement Learning with a Corrupted Reward Channel
Authors: Tom Everitt, Victoria Krakovna, Laurent Orseau, Marcus Hutter, Shane Legg
Date: 2017-05-23 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

No real-world reward function is perfect. Sensory errors and software bugs
may result in RL agents observing higher (or lower) rewards than they should.
For example, a reinforcement learning agent may prefer states where a sensory
error gives it the maximum reward, but where the true reward is actually small.
We formalise this problem as a generalised Markov Decision Problem called
Corrupt Reward MDP. Traditional RL methods fare poorly in CRMDPs, even under
strong simplifying assumptions and when trying to compensate for the possibly
corrupt rewards. Two ways around the problem are investigated. First, by giving
the agent richer data, such as in inverse reinforcement learning and
semi-supervised reinforcement learning, reward corruption stemming from
systematic sensory errors may sometimes be completely managed. Second, by using
randomisation to blunt the agent's optimisation, reward corruption can be
partially managed under some assumptions.
Measuring the Effect of Discourse Relations on Blog Summarization
Authors: Shamima Mithun, Leila Kosseim
Date: 2017-08-19 00:00:00
Proceeding: IJCNLP 2013 10
Tasks: Articles, Opinion Summarization, Text Summarization

The work presented in this paper attempts to evaluate and quantify the use of
discourse relations in the context of blog summarization and compare their use
to more traditional and factual texts. Specifically, we measured the usefulness
of 6 discourse relations - namely comparison, contingency, illustration,
attribution, topic-opinion, and attributive for the task of text summarization
from blogs. We have evaluated the effect of each relation using the TAC 2008
opinion summarization dataset and compared them with the results with the DUC
2007 dataset. The results show that in both textual genres, contingency,
comparison, and illustration relations provide a significant improvement on
summarization content; while attribution, topic-opinion, and attributive
relations do not provide a consistent and significant improvement. These
results indicate that, at least for summarization, discourse relations are just
as useful for informal and affective texts as for more traditional news
articles.
ClaC: Semantic Relatedness of Words and Phrases
Authors: Reda Siblini, Leila Kosseim
Date: 2017-08-19 00:00:00
Proceeding: SEMEVAL 2013 6
Tasks: Semantic Similarity, Semantic Textual Similarity

The measurement of phrasal semantic relatedness is an important metric for
many natural language processing applications. In this paper, we present three
approaches for measuring phrasal semantics, one based on a semantic network
model, another on a distributional similarity model, and a hybrid between the
two. Our hybrid approach achieved an F-measure of 77.4% on the task of
evaluating the semantic similarity of words and compositional phrases.
On the Contribution of Discourse Structure on Text Complexity Assessment
Authors: Elnaz Davoodi, Leila Kosseim
Date: 2017-08-19 00:00:00
Proceeding: WS 2016 9
Tasks: feature selection

This paper investigates the influence of discourse features on text
complexity assessment. To do so, we created two data sets based on the Penn
Discourse Treebank and the Simple English Wikipedia corpora and compared the
influence of coherence, cohesion, surface, lexical and syntactic features to
assess text complexity.
  Results show that with both data sets coherence features are more correlated
to text complexity than the other types of features. In addition, feature
selection revealed that with both data sets the top most discriminating feature
is a coherence feature.
The CLaC Discourse Parser at CoNLL-2016
Authors: Majid Laali, Andre Cianflone, Leila Kosseim
Date: 2017-08-19 00:00:00
Proceeding: CONLL 2016 8
Tasks: BIG-bench Machine Learning, Discourse Parsing

This paper describes our submission "CLaC" to the CoNLL-2016 shared task on
shallow discourse parsing. We used two complementary approaches for the task. A
standard machine learning approach for the parsing of explicit relations, and a
deep learning approach for non-explicit relations. Overall, our parser achieves
an F1-score of 0.2106 on the identification of discourse relations (0.3110 for
explicit relations and 0.1219 for non-explicit relations) on the blind
CoNLL-2016 test set.
CLaC @ QATS: Quality Assessment for Text Simplification
Authors: Elnaz Davoodi, Leila Kosseim
Date: 2017-08-19 00:00:00
Tasks: Language Modeling, Language Modelling, Sentence, Text Simplification

This paper describes our approach to the 2016 QATS quality assessment shared
task. We trained three independent Random Forest classifiers in order to assess
the quality of the simplified texts in terms of grammaticality, meaning
preservation and simplicity. We used the language model of Google-Ngram as
feature to predict the grammaticality. Meaning preservation is predicted using
two complementary approaches based on word embedding and WordNet synonyms. A
wider range of features including TF-IDF, sentence length and frequency of cue
phrases are used to evaluate the simplicity aspect. Overall, the accuracy of
the system ranges from 33.33% for the overall aspect to 58.73% for
grammaticality.
Fast Gaussian Process Regression for Big Data
Authors: Sourish Das, Sasanka Roy, Rajiv Sambasivan
Date: 2015-09-17 00:00:00
Tasks: Gaussian Processes, regression

Gaussian Processes are widely used for regression tasks. A known limitation
in the application of Gaussian Processes to regression tasks is that the
computation of the solution requires performing a matrix inversion. The
solution also requires the storage of a large matrix in memory. These factors
restrict the application of Gaussian Process regression to small and moderate
size data sets. We present an algorithm that combines estimates from models
developed using subsets of the data obtained in a manner similar to the
bootstrap. The sample size is a critical parameter for this algorithm.
Guidelines for reasonable choices of algorithm parameters, based on detailed
experimental study, are provided. Various techniques have been proposed to
scale Gaussian Processes to large scale regression tasks. The most appropriate
choice depends on the problem context. The proposed method is most appropriate
for problems where an additive model works well and the response depends on a
small number of features. The minimax rate of convergence for such problems is
attractive and we can build effective models with a small subset of the data.
The Stochastic Variational Gaussian Process and the Sparse Gaussian Process are
also appropriate choices for such problems. These methods pick a subset of data
based on theoretical considerations. The proposed algorithm uses bagging and
random sampling. Results from experiments conducted as part of this study
indicate that the algorithm presented in this work can be as effective as these
methods. Model stacking can be used to combine the model developed with the
proposed method with models from other methods for large scale regression such
as Gradient Boosted Trees. This can yield performance gains.
Learning Adversary-Resistant Deep Neural Networks
Authors: Qinglong Wang, Wenbo Guo, Kaixuan Zhang, Alexander G. Ororbia II, Xinyu Xing, Xue Liu, C. Lee Giles
Date: 2016-12-05 00:00:00
Tasks: Autonomous Vehicles

Deep neural networks (DNNs) have proven to be quite effective in a vast array
of machine learning tasks, with recent examples in cyber security and
autonomous vehicles. Despite the superior performance of DNNs in these
applications, it has been recently shown that these models are susceptible to a
particular type of attack that exploits a fundamental flaw in their design.
This attack consists of generating particular synthetic examples referred to as
adversarial samples. These samples are constructed by slightly manipulating
real data-points in order to "fool" the original DNN model, forcing it to
mis-classify previously correctly classified samples with high confidence.
Addressing this flaw in the model is essential if DNNs are to be used in
critical applications such as those in cyber security.
  Previous work has provided various learning algorithms to enhance the
robustness of DNN models, and they all fall into the tactic of "security
through obscurity". This means security can be guaranteed only if one can
obscure the learning algorithms from adversaries. Once the learning technique
is disclosed, DNNs protected by these defense mechanisms are still susceptible
to adversarial samples. In this work, we investigate this issue shared across
previous research work and propose a generic approach to escalate a DNN's
resistance to adversarial samples. More specifically, our approach integrates a
data transformation module with a DNN, making it robust even if we reveal the
underlying learning algorithm. To demonstrate the generality of our proposed
approach and its potential for handling cyber security applications, we
evaluate our method and several other existing solutions on datasets publicly
available. Our results indicate that our approach typically provides superior
classification performance and resistance in comparison with state-of-art
solutions.
Semi-supervised Conditional GANs
Authors: Kumar Sricharan, Raja Bala, Matthew Shreve, Hui Ding, Kumar Saketh, Jin Sun
Date: 2017-08-19 00:00:00

We introduce a new model for building conditional generative models in a
semi-supervised setting to conditionally generate data given attributes by
adapting the GAN framework. The proposed semi-supervised GAN (SS-GAN) model
uses a pair of stacked discriminators to learn the marginal distribution of the
data, and the conditional distribution of the attributes given the data
respectively. In the semi-supervised setting, the marginal distribution (which
is often harder to learn) is learned from the labeled + unlabeled data, and the
conditional distribution is learned purely from the labeled data. Our
experimental results demonstrate that this model performs significantly better
compared to existing semi-supervised conditional GAN models.
Statistical Anomaly Detection via Composite Hypothesis Testing for Markov Models
Authors: Jing Zhang, Ioannis Ch. Paschalidis
Date: 2017-02-27 00:00:00
Tasks: Anomaly Detection, Two-sample testing

Under Markovian assumptions, we leverage a Central Limit Theorem (CLT) for
the empirical measure in the test statistic of the composite hypothesis
Hoeffding test so as to establish weak convergence results for the test
statistic, and, thereby, derive a new estimator for the threshold needed by the
test. We first show the advantages of our estimator over an existing estimator
by conducting extensive numerical experiments. We find that our estimator
controls better for false alarms while maintaining satisfactory detection
probabilities. We then apply the Hoeffding test with our threshold estimator to
detecting anomalies in two distinct applications domains: one in communication
networks and the other in transportation networks. The former application seeks
to enhance cyber security and the latter aims at building smarter
transportation systems in cities.
Image Segmentation by Iterative Inference from Conditional Score Estimation
Authors: Adriana Romero, Michal Drozdzal, Akram Erraqabi, Simon Jégou, Yoshua Bengio
Date: 2017-05-21 00:00:00
Proceeding: ICLR 2018 1
Tasks: Denoising, Image Segmentation, Semantic Segmentation

Inspired by the combination of feedforward and iterative computations in the
virtual cortex, and taking advantage of the ability of denoising autoencoders
to estimate the score of a joint distribution, we propose a novel approach to
iterative inference for capturing and exploiting the complex joint distribution
of output variables conditioned on some input variables. This approach is
applied to image pixel-wise segmentation, with the estimated conditional score
used to perform gradient ascent towards a mode of the estimated conditional
distribution. This extends previous work on score estimation by denoising
autoencoders to the case of a conditional distribution, with a novel use of a
corrupted feedforward predictor replacing Gaussian corruption. An advantage of
this approach over more classical ways to perform iterative inference for
structured outputs, like conditional random fields (CRFs), is that it is not
any more necessary to define an explicit energy function linking the output
variables. To keep computations tractable, such energy function
parametrizations are typically fairly constrained, involving only a few
neighbors of each of the output variables in each clique. We experimentally
find that the proposed iterative inference from conditional score estimation by
conditional denoising autoencoders performs better than comparable models based
on CRFs or those not using any explicit modeling of the conditional joint
distribution of outputs.
Data-Driven Tree Transforms and Metrics
Authors: Gal Mishne, Ronen Talmon, Israel Cohen, Ronald R. Coifman, Yuval Kluger
Date: 2017-08-18 00:00:00
Tasks: Clustering

We consider the analysis of high dimensional data given in the form of a
matrix with columns consisting of observations and rows consisting of features.
Often the data is such that the observations do not reside on a regular grid,
and the given order of the features is arbitrary and does not convey a notion
of locality. Therefore, traditional transforms and metrics cannot be used for
data organization and analysis. In this paper, our goal is to organize the data
by defining an appropriate representation and metric such that they respect the
smoothness and structure underlying the data. We also aim to generalize the
joint clustering of observations and features in the case the data does not
fall into clear disjoint groups. For this purpose, we propose multiscale
data-driven transforms and metrics based on trees. Their construction is
implemented in an iterative refinement procedure that exploits the
co-dependencies between features and observations. Beyond the organization of a
single dataset, our approach enables us to transfer the organization learned
from one dataset to another and to integrate several datasets together. We
present an application to breast cancer gene expression analysis: learning
metrics on the genes to cluster the tumor samples into cancer sub-types and
validating the joint organization of both the genes and the samples. We
demonstrate that using our approach to combine information from multiple gene
expression cohorts, acquired by different profiling technologies, improves the
clustering of tumor samples.
The Natural Stories Corpus
Authors: Richard Futrell, Edward Gibson, Hal Tily, Idan Blank, Anastasia Vishnevetsky, Steven T. Piantadosi, Evelina Fedorenko
Date: 2017-08-18 00:00:00
Proceeding: LREC 2018 5

It is now a common practice to compare models of human language processing by
predicting participant reactions (such as reading times) to corpora consisting
of rich naturalistic linguistic materials. However, many of the corpora used in
these studies are based on naturalistic text and thus do not contain many of
the low-frequency syntactic constructions that are often required to
distinguish processing theories. Here we describe a new corpus consisting of
English texts edited to contain many low-frequency syntactic constructions
while still sounding fluent to native speakers. The corpus is annotated with
hand-corrected parse trees and includes self-paced reading time data. Here we
give an overview of the content of the corpus and release the data.