pwc-archive/papers-with-abstracts huggingface dataset:
Concept Drift and Anomaly Detection in Graph Streams
Authors: Daniele Zambon, Cesare Alippi, Lorenzo Livi
Date: 2017-06-21 00:00:00
Tasks: Anomaly Detection, Change Detection

Graph representations offer powerful and intuitive ways to describe data in a
multitude of application domains. Here, we consider stochastic processes
generating graphs and propose a methodology for detecting changes in
stationarity of such processes. The methodology is general and considers a
process generating attributed graphs with a variable number of vertices/edges,
without the need to assume one-to-one correspondence between vertices at
different time steps. The methodology acts by embedding every graph of the
stream into a vector domain, where a conventional multivariate change detection
procedure can be easily applied. We ground the soundness of our proposal by
proving several theoretical results. In addition, we provide a specific
implementation of the methodology and evaluate its effectiveness on several
detection problems involving attributed graphs representing biological
molecules and drawings. Experimental results are contrasted with respect to
suitable baseline methods, demonstrating the effectiveness of our approach.
A Two-Stage Method for Text Line Detection in Historical Documents
Authors: Tobias Grüning, Gundram Leifert, Tobias Strauß, Johannes Michael, Roger Labahn
Date: 2018-02-09 00:00:00
Tasks: Clustering, Data Augmentation, Line Detection, Vocal Bursts Valence Prediction

This work presents a two-stage text line detection method for historical documents. Each detected text line is represented by its baseline. In a first stage, a deep neural network called ARU-Net labels pixels to belong to one of the three classes: baseline, separator or other. The separator class marks beginning and end of each text line. The ARU-Net is trainable from scratch with manageably few manually annotated example images (less than 50). This is achieved by utilizing data augmentation strategies. The network predictions are used as input for the second stage which performs a bottom-up clustering to build baselines. The developed method is capable of handling complex layouts as well as curved and arbitrarily oriented text lines. It substantially outperforms current state-of-the-art approaches. For example, for the complex track of the cBAD: ICDAR2017 Competition on Baseline Detection the F-value is increased from 0.859 to 0.922. The framework to train and run the ARU-Net is open source.
Large Scale Constrained Linear Regression Revisited: Faster Algorithms via Preconditioning
Authors: Di Wang, Jinhui Xu
Date: 2018-02-09 00:00:00
Tasks: regression

In this paper, we revisit the large-scale constrained linear regression
problem and propose faster methods based on some recent developments in
sketching and optimization. Our algorithms combine (accelerated) mini-batch SGD
with a new method called two-step preconditioning to achieve an approximate
solution with a time complexity lower than that of the state-of-the-art
techniques for the low precision case. Our idea can also be extended to the
high precision case, which gives an alternative implementation to the Iterative
Hessian Sketch (IHS) method with significantly improved time complexity.
Experiments on benchmark and synthetic datasets suggest that our methods indeed
outperform existing ones considerably in both the low and high precision cases.
Learning Localized Spatio-Temporal Models From Streaming Data
Authors: Muhammad Osama, Dave Zachariah, Thomas B. Schön
Date: 2018-02-09 00:00:00
Proceeding: ICML 2018 7

We address the problem of predicting spatio-temporal processes with temporal
patterns that vary across spatial regions, when data is obtained as a stream.
That is, when the training dataset is augmented sequentially. Specifically, we
develop a localized spatio-temporal covariance model of the process that can
capture spatially varying temporal periodicities in the data. We then apply a
covariance-fitting methodology to learn the model parameters which yields a
predictor that can be updated sequentially with each new data point. The
proposed method is evaluated using both synthetic and real climate data which
demonstrate its ability to accurately predict data missing in spatial regions
over time.
Shapes Characterization on Address Event Representation Using Histograms of Oriented Events and an Extended LBP Approach
Authors: Pablo Negri
Date: 2018-02-09 00:00:00

Address Event Representation is a thriving technology that could change
digital image processing paradigm. This paper proposes a methodology to
characterize the shape of objects using the streaming of asynchronous events. A
new descriptor that enhances spikes connectivity is associated with two
oriented histogram based representations. This paper uses these features to
develop both a non-supervised and a supervised multi-classification framework
to recognize poker symbols from the Poker-DVS public dataset. The
aforementioned framework, which uses a very limited number of events and a
simple class modeling, yields results that challenge more sophisticated
methodologies proposed by the state of the art. A feature family based on
context shapes is applied to the more challenging 2015 Poker-DVS dataset with a
supervised classifier obtaining an accuracy of 98.5 %. The system is also
applied to the MNIST-DVS dataset yielding an accuracy of 94.6 % and 96.3 % on
digit recognition, for scales 4 and 8 respectively.
A Corpus for Modeling Word Importance in Spoken Dialogue Transcripts
Authors: Sushant Kafle, Matt Huenerfauth
Date: 2018-01-29 00:00:00
Proceeding: LREC 2018 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Motivated by a project to create a system for people who are deaf or hard-of-hearing that would use automatic speech recognition (ASR) to produce real-time text captions of spoken English during in-person meetings with hearing individuals, we have augmented a transcript of the Switchboard conversational dialogue corpus with an overlay of word-importance annotations, with a numeric score for each word, to indicate its importance to the meaning of each dialogue turn. Further, we demonstrate the utility of this corpus by training an automatic word importance labeling model; our best performing model has an F-score of 0.60 in an ordinal 6-class word-importance classification task with an agreement (concordance correlation coefficient) of 0.839 with the human annotators (agreement score between annotators is 0.89). Finally, we discuss our intended future applications of this resource, particularly for the task of evaluating ASR performance, i.e. creating metrics that predict ASR-output caption text usability for DHH users better thanWord Error Rate (WER).
MRI Tumor Segmentation with Densely Connected 3D CNN
Authors: Lele Chen, Yue Wu, Adora M. DSouza, Anas Z. Abidin, Axel Wismuller, Chenliang Xu
Date: 2018-01-18 00:00:00
Tasks: Tumor Segmentation

Glioma is one of the most common and aggressive types of primary brain
tumors. The accurate segmentation of subcortical brain structures is crucial to
the study of gliomas in that it helps the monitoring of the progression of
gliomas and aids the evaluation of treatment outcomes. However, the large
amount of required human labor makes it difficult to obtain the manually
segmented Magnetic Resonance Imaging (MRI) data, limiting the use of precise
quantitative measurements in the clinical practice. In this work, we try to
address this problem by developing a 3D Convolutional Neural Network~(3D CNN)
based model to automatically segment gliomas. The major difficulty of our
segmentation model comes with the fact that the location, structure, and shape
of gliomas vary significantly among different patients. In order to accurately
classify each voxel, our model captures multi-scale contextual information by
extracting features from two scales of receptive fields. To fully exploit the
tumor structure, we propose a novel architecture that hierarchically segments
different lesion regions of the necrotic and non-enhancing tumor~(NCR/NET),
peritumoral edema~(ED) and GD-enhancing tumor~(ET). Additionally, we utilize
densely connected convolutional blocks to further boost the performance. We
train our model with a patch-wise training schema to mitigate the class
imbalance problem. The proposed method is validated on the BraTS 2017 dataset
and it achieves Dice scores of 0.72, 0.83 and 0.81 for the complete tumor,
tumor core and enhancing tumor, respectively. These results are comparable to
the reported state-of-the-art results, and our method is better than existing
3D-based methods in terms of compactness, time and space efficiency.
Predicting Audio Advertisement Quality
Authors: Samaneh Ebrahimi, Hossein Vahabi, Matthew Prockup, Oriol Nieto
Date: 2018-02-09 00:00:00
Tasks: Rhythm

Online audio advertising is a particular form of advertising used abundantly
in online music streaming services. In these platforms, which tend to host tens
of thousands of unique audio advertisements (ads), providing high quality ads
ensures a better user experience and results in longer user engagement.
Therefore, the automatic assessment of these ads is an important step toward
audio ads ranking and better audio ads creation. In this paper we propose one
way to measure the quality of the audio ads using a proxy metric called Long
Click Rate (LCR), which is defined by the amount of time a user engages with
the follow-up display ad (that is shown while the audio ad is playing) divided
by the impressions. We later focus on predicting the audio ad quality using
only acoustic features such as harmony, rhythm, and timbre of the audio,
extracted from the raw waveform. We discuss how the characteristics of the
sound can be connected to concepts such as the clarity of the audio ad message,
its trustworthiness, etc. Finally, we propose a new deep learning model for
audio ad quality prediction, which outperforms the other discussed models
trained on hand-crafted features. To the best of our knowledge, this is the
first large-scale audio ad quality prediction study.
Nature vs. Nurture: The Role of Environmental Resources in Evolutionary Deep Intelligence
Authors: Audrey G. Chung, Paul Fieguth, Alexander Wong
Date: 2018-02-09 00:00:00

Evolutionary deep intelligence synthesizes highly efficient deep neural
networks architectures over successive generations. Inspired by the nature
versus nurture debate, we propose a study to examine the role of external
factors on the network synthesis process by varying the availability of
simulated environmental resources. Experimental results were obtained for
networks synthesized via asexual evolutionary synthesis (1-parent) and sexual
evolutionary synthesis (2-parent, 3-parent, and 5-parent) using a 10% subset of
the MNIST dataset. Results show that a lower environmental factor model
resulted in a more gradual loss in performance accuracy and decrease in storage
size. This potentially allows significantly reduced storage size with minimal
to no drop in performance accuracy, and the best networks were synthesized
using the lowest environmental factor models.
Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems
Authors: Stefano V. Albrecht, Peter Stone
Date: 2017-09-23 00:00:00

Much research in artificial intelligence is concerned with the development of
autonomous agents that can interact effectively with other agents. An important
aspect of such agents is the ability to reason about the behaviours of other
agents, by constructing models which make predictions about various properties
of interest (such as actions, goals, beliefs) of the modelled agents. A variety
of modelling approaches now exist which vary widely in their methodology and
underlying assumptions, catering to the needs of the different sub-communities
within which they were developed and reflecting the different practical uses
for which they are intended. The purpose of the present article is to provide a
comprehensive survey of the salient modelling methods which can be found in the
literature. The article concludes with a discussion of open problems which may
form the basis for fruitful future research.
Bayesian inference for bivariate ranks
Authors: Simon Guillotte, François Perron, Johan Segers
Date: 2018-02-09 00:00:00
Tasks: Bayesian Inference, Recommendation Systems

A recommender system based on ranks is proposed, where an expert's ranking of
a set of objects and a user's ranking of a subset of those objects are combined
to make a prediction of the user's ranking of all objects. The rankings are
assumed to be induced by latent continuous variables corresponding to the
grades assigned by the expert and the user to the objects. The dependence
between the expert and user grades is modelled by a copula in some parametric
family. Given a prior distribution on the copula parameter, the user's complete
ranking is predicted by the mode of the posterior predictive distribution of
the user's complete ranking conditional on the expert's complete and the user's
incomplete rankings. Various Markov chain Monte-Carlo algorithms are proposed
to approximate the predictive distribution or only its mode. The predictive
distribution can be obtained exactly for the Farlie-Gumbel-Morgenstern copula
family, providing a benchmark for the approximation accuracy of the algorithms.
The method is applied to the MovieLens 100k dataset with a Gaussian copula
modelling dependence between the expert's and user's grades.
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
Authors: Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Volodymir Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, Koray Kavukcuoglu
Date: 2018-02-05 00:00:00
Proceeding: ICML 2018 7
Tasks: Atari Games, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this work we aim to solve a large collection of tasks using a single
reinforcement learning agent with a single set of parameters. A key challenge
is to handle the increased amount of data and extended training time. We have
developed a new distributed agent IMPALA (Importance Weighted Actor-Learner
Architecture) that not only uses resources more efficiently in single-machine
training but also scales to thousands of machines without sacrificing data
efficiency or resource utilisation. We achieve stable learning at high
throughput by combining decoupled acting and learning with a novel off-policy
correction method called V-trace. We demonstrate the effectiveness of IMPALA
for multi-task reinforcement learning on DMLab-30 (a set of 30 tasks from the
DeepMind Lab environment (Beattie et al., 2016)) and Atari-57 (all available
Atari games in Arcade Learning Environment (Bellemare et al., 2013a)). Our
results show that IMPALA is able to achieve better performance than previous
agents with less data, and crucially exhibits positive transfer between tasks
as a result of its multi-task approach.
Embodied Evolution in Collective Robotics: A Review
Authors: Nicolas Bredeche, Evert Haasdijk, Abraham Prieto
Date: 2017-09-26 00:00:00

This paper provides an overview of evolutionary robotics techniques applied
to on-line distributed evolution for robot collectives -- namely, embodied
evolution. It provides a definition of embodied evolution as well as a thorough
description of the underlying concepts and mechanisms. The paper also presents
a comprehensive summary of research published in the field since its inception
(1999-2017), providing various perspectives to identify the major trends. In
particular, we identify a shift from considering embodied evolution as a
parallel search method within small robot collectives (fewer than 10 robots) to
embodied evolution as an on-line distributed learning method for designing
collective behaviours in swarm-like collectives. The paper concludes with a
discussion of applications and open questions, providing a milestone for past
and an inspiration for future research.
Unbiased Sparse Subspace Clustering By Selective Pursuit
Authors: Hanno Ackermann, Michael Ying Yang, Bodo Rosenhahn
Date: 2016-09-16 00:00:00
Tasks: Clustering, Motion Segmentation, Segmentation

Sparse subspace clustering (SSC) is an elegant approach for unsupervised
segmentation if the data points of each cluster are located in linear
subspaces. This model applies, for instance, in motion segmentation if some
restrictions on the camera model hold. SSC requires that problems based on the
$l_1$-norm are solved to infer which points belong to the same subspace. If
these unknown subspaces are well-separated this algorithm is guaranteed to
succeed. The algorithm rests upon the assumption that points on the same
subspace are well spread. The question what happens if this condition is
violated has not yet been investigated. In this work, the effect of particular
distributions on the same subspace will be analyzed. It will be shown that SSC
fails to infer correct labels if points on the same subspace fall into more
than one cluster.
Temporally Object-based Video Co-Segmentation
Authors: Michael Ying Yang, Matthias Reso, Jun Tang, Wentong Liao, Bodo Rosenhahn
Date: 2018-02-09 00:00:00
Tasks: Object, Segmentation

In this paper, we propose an unsupervised video object co-segmentation
framework based on the primary object proposals to extract the common
foreground object(s) from a given video set. In addition to the objectness
attributes and motion coherence our framework exploits the temporal consistency
of the object-like regions between adjacent frames to enrich the set of
original object proposals. We call the enriched proposal sets temporal proposal
streams, as they are composed of the most similar proposals from each frame
augmented with predicted proposals using temporally consistent superpixel
information. The temporal proposal streams represent all the possible region
tubes of the objects. Therefore, we formulate a graphical model to select a
proposal stream for each object in which the pairwise potentials consist of the
appearance dissimilarity between different streams in the same video and also
the similarity between the streams in different videos. This model is suitable
for single (multiple) foreground objects in two (more) videos, which can be
solved by any existing energy minimization method. We evaluate our proposed
framework by comparing it to other video co-segmentation algorithms. Our method
achieves improved performance on state-of-the-art benchmark datasets.
Slice Sampling Particle Belief Propagation
Authors: Oliver Mueller, Michael Ying Yang, Bodo Rosenhahn
Date: 2018-02-09 00:00:00
Tasks: Denoising, Image Denoising

Inference in continuous label Markov random fields is a challenging task. We
use particle belief propagation (PBP) for solving the inference problem in
continuous label space. Sampling particles from the belief distribution is
typically done by using Metropolis-Hastings Markov chain Monte Carlo methods
which involves sampling from a proposal distribution. This proposal
distribution has to be carefully designed depending on the particular model and
input data to achieve fast convergence. We propose to avoid dependence on a
proposal distribution by introducing a slice sampling based PBP algorithm. The
proposed approach shows superior convergence performance on an image denoising
toy example. Our findings are validated on a challenging relational 2D feature
tracking application.
Augmented Reality needle ablation guidance tool for Irreversible Electroporation in the pancreas
Authors: Timur Kuzhagaliyev, Neil T. Clancy, Mirek Janatka, Kevin Tchaka, Francisco Vasconcelos, Matthew J. Clarkson, Kurinchi Gurusamy, David J. Hawkes, Brian Davidson, Danail Stoyanov
Date: 2018-02-09 00:00:00

Irreversible electroporation (IRE) is a soft tissue ablation technique
suitable for treatment of inoperable tumours in the pancreas. The process
involves applying a high voltage electric field to the tissue containing the
mass using needle electrodes, leaving cancerous cells irreversibly damaged and
vulnerable to apoptosis. Efficacy of the treatment depends heavily on the
accuracy of needle placement and requires a high degree of skill from the
operator. In this paper, we describe an Augmented Reality (AR) system designed
to overcome the challenges associated with planning and guiding the needle
insertion process. Our solution, based on the HoloLens (Microsoft, USA)
platform, tracks the position of the headset, needle electrodes and ultrasound
(US) probe in space. The proof of concept implementation of the system uses
this tracking data to render real-time holographic guides on the HoloLens,
giving the user insight into the current progress of needle insertion and an
indication of the target needle trajectory. The operator's field of view is
augmented using visual guides and real-time US feed rendered on a holographic
plane, eliminating the need to consult external monitors. Based on these early
prototypes, we are aiming to develop a system that will lower the skill level
required for IRE while increasing overall accuracy of needle insertion and,
hence, the likelihood of successful treatment.
Unsupervised Deep Domain Adaptation for Pedestrian Detection
Authors: Lihang Liu, Weiyao Lin, Lisheng Wu, Yong Yu, Michael Ying Yang
Date: 2018-02-09 00:00:00
Tasks: Domain Adaptation, Pedestrian Detection, Unsupervised Domain Adaptation

This paper addresses the problem of unsupervised domain adaptation on the
task of pedestrian detection in crowded scenes. First, we utilize an iterative
algorithm to iteratively select and auto-annotate positive pedestrian samples
with high confidence as the training samples for the target domain. Meanwhile,
we also reuse negative samples from the source domain to compensate for the
imbalance between the amount of positive samples and negative samples. Second,
based on the deep network we also design an unsupervised regularizer to
mitigate influence from data noise. More specifically, we transform the last
fully connected layer into two sub-layers - an element-wise multiply layer and
a sum layer, and add the unsupervised regularizer to further improve the domain
adaptation accuracy. In experiments for pedestrian detection, the proposed
method boosts the recall value by nearly 30% while the precision stays almost
the same. Furthermore, we perform our method on standard domain adaptation
benchmarks on both supervised and unsupervised settings and also achieve
state-of-the-art results.
Identifying Clickbait: A Multi-Strategy Approach Using Neural Networks
Authors: Vaibhav Kumar, Dhruv Khattar, Siddhartha Gairola, Yash Kumar Lal, Vasudeva Varma
Date: 2017-10-04 00:00:00
Tasks: Articles, Clickbait Detection, Feature Engineering

Online media outlets, in a bid to expand their reach and subsequently
increase revenue through ad monetisation, have begun adopting clickbait
techniques to lure readers to click on articles. The article fails to fulfill
the promise made by the headline. Traditional methods for clickbait detection
have relied heavily on feature engineering which, in turn, is dependent on the
dataset it is built for. The application of neural networks for this task has
only been explored partially. We propose a novel approach considering all
information found in a social media post. We train a bidirectional LSTM with an
attention mechanism to learn the extent to which a word contributes to the
post's clickbait score in a differential manner. We also employ a Siamese net
to capture the similarity between source and target information. Information
gleaned from images has not been considered in previous approaches. We learn
image embeddings from large amounts of data using Convolutional Neural Networks
to add another layer of complexity to our model. Finally, we concatenate the
outputs from the three separate components, serving it as input to a fully
connected layer. We conduct experiments over a test corpus of 19538 social
media posts, attaining an F1 score of 65.37% on the dataset bettering the
previous state-of-the-art, as well as other proposed approaches, feature
engineering or otherwise.