pwc-archive/papers-with-abstracts huggingface dataset:
Smart Device based Initial Movement Detection of Cyclists using Convolutional Neuronal Networks
Authors: Jan Schneegans, Maarten Bieshaar
Date: 2018-08-08 00:00:00

For future traffic scenarios, we envision interconnected traffic
participants, who exchange information about their current state, e.g.,
position, their predicted intentions, allowing to act in a cooperative manner.
Vulnerable road users (VRUs), e.g., pedestrians and cyclists, will be equipped
with smart device that can be used to detect their intentions and transmit
these detected intention to approaching cars such that their drivers can be
warned. In this article, we focus on detecting the initial movement of cyclist
using smart devices. Smart devices provide the necessary sensors, namely
accelerometer and gyroscope, and therefore pose an excellent instrument to
detect movement transitions (e.g., waiting to moving) fast. Convolutional
Neural Networks prove to be the state-of-the-art solution for many problems
with an ever increasing range of applications. Therefore, we model the initial
movement detection as a classification problem. In terms of Organic Computing
(OC) it be seen as a step towards self-awareness and self-adaptation. We apply
residual network architectures to the task of detecting the initial starting
movement of cyclists.
Starting Movement Detection of Cyclists Using Smart Devices
Authors: Maarten Bieshaar, Malte Depping, Jan Schneegans, Bernhard Sick
Date: 2018-08-08 00:00:00
Tasks: Activity Recognition, feature selection, Human Activity Recognition

In near future, vulnerable road users (VRUs) such as cyclists and pedestrians
will be equipped with smart devices and wearables which are capable to
communicate with intelligent vehicles and other traffic participants. Road
users are then able to cooperate on different levels, such as in cooperative
intention detection for advanced VRU protection. Smart devices can be used to
detect intentions, e.g., an occluded cyclist intending to cross the road, to
warn vehicles of VRUs, and prevent potential collisions. This article presents
a human activity recognition approach to detect the starting movement of
cyclists wearing smart devices. We propose a novel two-stage feature selection
procedure using a score specialized for robust starting detection reducing the
false positive detections and leading to understandable and interpretable
features. The detection is modelled as a classification problem and realized by
means of a machine learning classifier. We introduce an auxiliary class, that
models starting movements and allows to integrate early movement indicators,
i.e., body part movements indicating future behaviour. In this way we improve
the robustness and reduce the detection time of the classifier. Our empirical
studies with real-world data originating from experiments which involve 49 test
subjects and consists of 84 starting motions show that we are able to detect
the starting movements early. Our approach reaches an F1-score of 67 % within
0.33 s after the first movement of the bicycle wheel. Investigations concerning
the device wearing location show that for devices worn in the trouser pocket
the detector has less false detections and detects starting movements faster on
average. We found that we can further improve the results when we train
distinct classifiers for different wearing locations.
Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging
Authors: Akshay Chaudhari, Zhongnan Fang, Jin Hyung Lee, Garry Gold, Brian Hargreaves
Date: 2018-08-07 00:00:00
Tasks: Super-Resolution

Obtaining magnetic resonance images (MRI) with high resolution and generating
quantitative image-based biomarkers for assessing tissue biochemistry is
crucial in clinical and research applications. How- ever, acquiring
quantitative biomarkers requires high signal-to-noise ratio (SNR), which is at
odds with high-resolution in MRI, especially in a single rapid sequence. In
this paper, we demonstrate how super-resolution can be utilized to maintain
adequate SNR for accurate quantification of the T2 relaxation time biomarker,
while simultaneously generating high- resolution images. We compare the
efficacy of resolution enhancement using metrics such as peak SNR and
structural similarity. We assess accuracy of cartilage T2 relaxation times by
comparing against a standard reference method. Our evaluation suggests that SR
can successfully maintain high-resolution and generate accurate biomarkers for
accelerating MRI scans and enhancing the value of clinical and research MRI.
Statistical Windows in Testing for the Initial Distribution of a Reversible Markov Chain
Authors: Quentin Berthet, Varun Kanade
Date: 2018-08-06 00:00:00
Tasks: Two-sample testing

We study the problem of hypothesis testing between two discrete
distributions, where we only have access to samples after the action of a known
reversible Markov chain, playing the role of noise. We derive
instance-dependent minimax rates for the sample complexity of this problem, and
show how its dependence in time is related to the spectral properties of the
Markov chain. We show that there exists a wide statistical window, in terms of
sample complexity for hypothesis testing between different pairs of initial
distributions. We illustrate these results in several concrete examples.
X-GANs: Image Reconstruction Made Easy for Extreme Cases
Authors: Longfei Liu, Sheng Li, Yisong Chen, Guoping Wang
Date: 2018-08-06 00:00:00
Tasks: Denoising, Image Compression, Image Denoising, Image Inpainting, Image Reconstruction, Image Restoration

Image reconstruction including image restoration and denoising is a
challenging problem in the field of image computing. We present a new method,
called X-GANs, for reconstruction of arbitrary corrupted resource based on a
variant of conditional generative adversarial networks (conditional GANs). In
our method, a novel generator and multi-scale discriminators are proposed, as
well as the combined adversarial losses, which integrate a VGG perceptual loss,
an adversarial perceptual loss, and an elaborate corresponding point loss
together based on the analysis of image feature. Our conditional GANs have
enabled a variety of applications in image reconstruction, including image
denoising, image restoration from quite a sparse sampling, image inpainting,
image recovery from the severely polluted block or even color-noise dominated
images, which are extreme cases and haven't been addressed in the status quo.
We have significantly improved the accuracy and quality of image
reconstruction. Extensive perceptual experiments on datasets ranging from human
faces to natural scenes demonstrate that images reconstructed by the presented
approach are considerably more realistic than alternative work. Our method can
also be extended to handle high-ratio image compression.
FaceOff: Anonymizing Videos in the Operating Rooms
Authors: Evangello Flouty, Odysseas Zisimopoulos, Danail Stoyanov
Date: 2018-08-06 00:00:00
Tasks: Face Detection

Video capture in the surgical operating room (OR) is increasingly possible
and has potential for use with computer assisted interventions (CAI), surgical
data science and within smart OR integration. Captured video innately carries
sensitive information that should not be completely visible in order to
preserve the patient's and the clinical teams' identities. When surgical video
streams are stored on a server, the videos must be anonymized prior to storage
if taken outside of the hospital. In this article, we describe how a deep
learning model, Faster R-CNN, can be used for this purpose and help to
anonymize video data captured in the OR. The model detects and blurs faces in
an effort to preserve anonymity. After testing an existing face detection
trained model, a new dataset tailored to the surgical environment, with faces
obstructed by surgical masks and caps, was collected for fine-tuning to achieve
higher face-detection rates in the OR. We also propose a temporal
regularisation kernel to improve recall rates. The fine-tuned model achieves a
face detection recall of 88.05 % and 93.45 % before and after applying
temporal-smoothing respectively.
Machine Learning Promoting Extreme Simplification of Spectroscopy Equipment
Authors: Jianchao Lee, Qiannan Duan, Sifan Bi, Ruen Luo, Yachao Lian, Hanqiang Liu, Ruixing Tian, Jiayuan Chen, Guodong Ma, Jinhong Gao, Zhaoyi Xu
Date: 2018-08-06 00:00:00
Tasks: BIG-bench Machine Learning

The spectroscopy measurement is one of main pathways for exploring and understanding the nature. Today, it seems that racing artificial intelligence will remould its styles. The algorithms contained in huge neural networks are capable of substituting many of expensive and complex components of spectrum instruments. In this work, we presented a smart machine learning strategy on the measurement of absorbance curves, and also initially verified that an exceedingly-simplified equipment is sufficient to meet the needs for this strategy. Further, with its simplicity, the setup is expected to infiltrate into many scientific areas in versatile forms.
A novel method for predicting and mapping the presence of sun glare using Google Street View
Authors: Xiaojiang Li, Bill Yang Cai, Waishan Qiu, Jinhua Zhao, Carlo Ratti
Date: 2018-08-05 00:00:00

The sun glare is one of the major environmental hazards that cause traffic
accidents. Every year, many people died and injured in traffic accidents
related to sun glare. Providing accurate information about when and where sun
glare happens would be helpful to prevent sun glare caused traffic accidents
and save lives. In this study, we proposed to use publicly accessible Google
Street View (GSV) panorama images to estimate and predict the occurrence of sun
glare. GSV images have view sight similar to drivers, which would make GSV
images suitable for estimating the visibility of sun glare to drivers. A
recently developed convolutional neural network algorithm was used to segment
GSV images and predict obstructions on sun glare. Based on the predicted
obstructions for given locations, we further estimated the time windows of sun
glare by estimating the sun positions and the relative angles between drivers
and the sun for those locations. We conducted a case study in Cambridge,
Massachusetts, USA. Results show that the method can predict the presence of
sun glare precisely. The proposed method would provide an important tool for
drivers and traffic planners to mitigate the sun glare and decrease the
potential traffic accidents caused by the sun glare.
Application of Bounded Total Variation Denoising in Urban Traffic Analysis
Authors: Shanshan Tang, Haijun Yu
Date: 2018-08-04 00:00:00
Tasks: Clustering, Denoising, Traffic Prediction

While it is believed that denoising is not always necessary in many big data
applications, we show in this paper that denoising is helpful in urban traffic
analysis by applying the method of bounded total variation denoising to the
urban road traffic prediction and clustering problem. We propose two
easy-to-implement methods to estimate the noise strength parameter in the
denoising algorithm, and apply the denoising algorithm to GPS-based traffic
data from Beijing taxi system. For the traffic prediction problem, we combine
neural network and history matching method for roads randomly chosen from an
urban area of Beijing. Numerical experiments show that the predicting accuracy
is improved significantly by applying the proposed bounded total variation
denoising algorithm. We also test the algorithm on clustering problem, where a
recently developed clustering analysis method is applied to more than one
hundred urban road segments in Beijing based on their velocity profiles. Better
clustering result is obtained after denoising.
Visual Reasoning with Multi-hop Feature Modulation
Authors: Florian Strub, Mathieu Seurin, Ethan Perez, Harm de Vries, Jérémie Mary, Philippe Preux, Aaron Courville, Olivier Pietquin
Date: 2018-08-03 00:00:00
Proceeding: ECCV 2018 9
Tasks: Question Answering, Visual Dialog, Visual Question Answering, Visual Question Answering (VQA), Visual Reasoning

Recent breakthroughs in computer vision and natural language processing have
spurred interest in challenging multi-modal tasks such as visual
question-answering and visual dialogue. For such tasks, one successful approach
is to condition image-based convolutional network computation on language via
Feature-wise Linear Modulation (FiLM) layers, i.e., per-channel scaling and
shifting. We propose to generate the parameters of FiLM layers going up the
hierarchy of a convolutional network in a multi-hop fashion rather than all at
once, as in prior work. By alternating between attending to the language input
and generating FiLM layer parameters, this approach is better able to scale to
settings with longer input sequences such as dialogue. We demonstrate that
multi-hop FiLM generation achieves state-of-the-art for the short input
sequence task ReferIt --- on-par with single-hop FiLM generation --- while also
significantly outperforming prior state-of-the-art and single-hop FiLM
generation on the GuessWhat?! visual dialogue task.
Modeling Meaning Associated with Documental Entities: Introducing the Brussels Quantum Approach
Authors: Diederik Aerts, Massimiliano Sassoli de Bianchi, Sandro Sozzo, Tomas Veloz
Date: 2018-08-03 00:00:00

We show that the Brussels operational-realistic approach to quantum physics
and quantum cognition offers a fundamental strategy for modeling the meaning
associated with collections of documental entities. To do so, we take the World
Wide Web as a paradigmatic example and emphasize the importance of
distinguishing the Web, made of printed documents, from a more abstract meaning
entity, which we call the Quantum Web, or QWeb, where the former is considered
to be the collection of traces that can be left by the latter, in specific
measurements, similarly to how a non-spatial quantum entity, like an electron,
can leave localized traces of impact on a detection screen. The double-slit
experiment is extensively used to illustrate the rationale of the modeling,
which is guided by how physicists constructed quantum theory to describe the
behavior of the microscopic entities. We also emphasize that the superposition
principle and the associated interference effects are not sufficient to model
all experimental probabilistic data, like those obtained by counting the
relative number of documents containing certain words and co-occurrences of
words. For this, additional effects, like context effects, must also be taken
into consideration.
SMOTE: Synthetic Minority Over-sampling Technique
Authors: N. V. Chawla, K. W. Bowyer, L. O. Hall, W. P. Kegelmeyer
Date: 2011-06-09 00:00:00

An approach to the construction of classifiers from imbalanced datasets is
described. A dataset is imbalanced if the classification categories are not
approximately equally represented. Often real-world data sets are predominately
composed of "normal" examples with only a small percentage of "abnormal" or
"interesting" examples. It is also the case that the cost of misclassifying an
abnormal (interesting) example as a normal example is often much higher than
the cost of the reverse error. Under-sampling of the majority (normal) class
has been proposed as a good means of increasing the sensitivity of a classifier
to the minority class. This paper shows that a combination of our method of
over-sampling the minority (abnormal) class and under-sampling the majority
(normal) class can achieve better classifier performance (in ROC space) than
only under-sampling the majority class. This paper also shows that a
combination of our method of over-sampling the minority class and
under-sampling the majority class can achieve better classifier performance (in
ROC space) than varying the loss ratios in Ripper or class priors in Naive
Bayes. Our method of over-sampling the minority class involves creating
synthetic minority class examples. Experiments are performed using C4.5, Ripper
and a Naive Bayes classifier. The method is evaluated using the area under the
Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
Building medical image classifiers with very limited data using segmentation networks
Authors: Ken C. L. Wong, Tanveer Syeda-Mahmood, Mehdi Moradi
Date: 2018-08-15 00:00:00
Tasks: Classification, General Classification, Medical Image Analysis, Transfer Learning

Deep learning has shown promising results in medical image analysis, however,
the lack of very large annotated datasets confines its full potential. Although
transfer learning with ImageNet pre-trained classification models can alleviate
the problem, constrained image sizes and model complexities can lead to
unnecessary increase in computational cost and decrease in performance. As many
common morphological features are usually shared by different classification
tasks of an organ, it is greatly beneficial if we can extract such features to
improve classification with limited samples. Therefore, inspired by the idea of
curriculum learning, we propose a strategy for building medical image
classifiers using features from segmentation networks. By using a segmentation
network pre-trained on similar data as the classification task, the machine can
first learn the simpler shape and structural concepts before tackling the
actual classification problem which usually involves more complicated concepts.
Using our proposed framework on a 3D three-class brain tumor type
classification problem, we achieved 82% accuracy on 191 testing samples with 91
training samples. When applying to a 2D nine-class cardiac semantic level
classification problem, we achieved 86% accuracy on 263 testing samples with
108 training samples. Comparisons with ImageNet pre-trained classifiers and
classifiers trained from scratch are presented.
Recycle-GAN: Unsupervised Video Retargeting
Authors: Aayush Bansal, Shugao Ma, Deva Ramanan, Yaser Sheikh
Date: 2018-08-15 00:00:00
Proceeding: ECCV 2018 9
Tasks: Face to Face Translation, Translation, Video Generation

We introduce a data-driven approach for unsupervised video retargeting that
translates content from one domain to another while preserving the style native
to a domain, i.e., if contents of John Oliver's speech were to be transferred
to Stephen Colbert, then the generated content/speech should be in Stephen
Colbert's style. Our approach combines both spatial and temporal information
along with adversarial losses for content translation and style preservation.
In this work, we first study the advantages of using spatiotemporal constraints
over spatial constraints for effective retargeting. We then demonstrate the
proposed approach for the problems where information in both space and time
matters such as face-to-face translation, flower-to-flower, wind and cloud
synthesis, sunrise and sunset.
A Simple Convolutional Generative Network for Next Item Recommendation
Authors: Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose, Xiangnan He
Date: 2018-08-15 00:00:00
Tasks: Recommendation Systems

Convolutional Neural Networks (CNNs) have been recently introduced in the
domain of session-based next item recommendation. An ordered collection of past
items the user has interacted with in a session (or sequence) are embedded into
a 2-dimensional latent matrix, and treated as an image. The convolution and
pooling operations are then applied to the mapped item embeddings. In this
paper, we first examine the typical session-based CNN recommender and show that
both the generative model and network architecture are suboptimal when modeling
long-range dependencies in the item sequence. To address the issues, we
introduce a simple, but very effective generative model that is capable of
learning high-level representation from both short- and long-range item
dependencies. The network architecture of the proposed model is formed of a
stack of \emph{holed} convolutional layers, which can efficiently increase the
receptive fields without relying on the pooling operation. Another contribution
is the effective use of residual block structure in recommender systems, which
can ease the optimization for much deeper networks. The proposed generative
model attains state-of-the-art accuracy with less training time in the next
item recommendation task. It accordingly can be used as a powerful
recommendation baseline to beat in future, especially when there are long
sequences of user feedback.
Deep Learning using K-space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection
Authors: Ilkay Oksuz, Bram Ruijsink, Esther Puyol-Anton, Aurelien Bustin, Gastao Cruz, Claudia Prieto, Daniel Rueckert, Julia A. Schnabel, Andrew P. King
Date: 2018-08-15 00:00:00
Tasks: Data Augmentation, Image Quality Assessment, imbalanced classification

Quality assessment of medical images is essential for complete automation of
image processing pipelines. For large population studies such as the UK
Biobank, artefacts such as those caused by heart motion are problematic and
manual identification is tedious and time-consuming. Therefore, there is an
urgent need for automatic image quality assessment techniques. In this paper,
we propose a method to automatically detect the presence of motion-related
artefacts in cardiac magnetic resonance (CMR) images. As this is a highly
imbalanced classification problem (due to the high number of good quality
images compared to the low number of images with motion artefacts), we propose
a novel k-space based training data augmentation approach in order to address
this problem. Our method is based on 3D spatio-temporal Convolutional Neural
Networks, and is able to detect 2D+time short axis images with motion artefacts
in less than 1ms. We test our algorithm on a subset of the UK Biobank dataset
consisting of 3465 CMR images and achieve not only high accuracy in detection
of motion artefacts, but also high precision and recall. We compare our
approach to a range of state-of-the-art quality assessment methods.
Using Regular Languages to Explore the Representational Capacity of Recurrent Neural Architectures
Authors: Abhijit Mahalunkar, John D. Kelleher
Date: 2018-08-15 00:00:00
Tasks: Benchmarking

The presence of Long Distance Dependencies (LDDs) in sequential data poses
significant challenges for computational models. Various recurrent neural
architectures have been designed to mitigate this issue. In order to test these
state-of-the-art architectures, there is growing need for rich benchmarking
datasets. However, one of the drawbacks of existing datasets is the lack of
experimental control with regards to the presence and/or degree of LDDs. This
lack of control limits the analysis of model performance in relation to the
specific challenge posed by LDDs. One way to address this is to use synthetic
data having the properties of subregular languages. The degree of LDDs within
the generated data can be controlled through the k parameter, length of the
generated strings, and by choosing appropriate forbidden strings. In this
paper, we explore the capacity of different RNN extensions to model LDDs, by
evaluating these models on a sequence of SPk synthesized datasets, where each
subsequent dataset exhibits a longer degree of LDD. Even though SPk are simple
languages, the presence of LDDs does have significant impact on the performance
of recurrent neural architectures, thus making them prime candidate in
benchmarking tasks.