pwc-archive/papers-with-abstracts huggingface dataset:
Kickstarting Deep Reinforcement Learning
Authors: Simon Schmitt, Jonathan J. Hudson, Augustin Zidek, Simon Osindero, Carl Doersch, Wojciech M. Czarnecki, Joel Z. Leibo, Heinrich Kuttler, Andrew Zisserman, Karen Simonyan, S. M. Ali Eslami
Date: 2018-03-10 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present a method for using previously-trained 'teacher' agents to
kickstart the training of a new 'student' agent. To this end, we leverage ideas
from policy distillation and population based training. Our method places no
constraints on the architecture of the teacher or student agents, and it
regulates itself to allow the students to surpass their teachers in
performance. We show that, on a challenging and computationally-intensive
multi-task benchmark (DMLab-30), kickstarted training improves the data
efficiency of new agents, making it significantly easier to iterate on their
design. We also show that the same kickstarting pipeline can allow a single
student agent to leverage multiple 'expert' teachers which specialize on
individual tasks. In this setting kickstarting yields surprisingly large gains,
with the kickstarted agent matching the performance of an agent trained from
scratch in almost 10x fewer steps, and surpassing its final performance by 42
percent. Kickstarting is conceptually simple and can easily be incorporated
into reinforcement learning experiments.
Learning and analyzing vector encoding of symbolic representations
Authors: Roland Fernandez, Asli Celikyilmaz, Rishabh Singh, Paul Smolensky
Date: 2018-03-10 00:00:00

We present a formal language with expressions denoting general symbol
structures and queries which access information in those structures. A
sequence-to-sequence network processing this language learns to encode symbol
structures and query them. The learned representation (approximately) shares a
simple linearity property with theoretical techniques for performing this task.
Graph-based Clustering under Differential Privacy
Authors: Rafael Pinot, Anne Morvan, Florian Yger, Cédric Gouy-Pailler, Jamal Atif
Date: 2018-03-10 00:00:00
Tasks: Clustering

In this paper, we present the first differentially private clustering method
for arbitrary-shaped node clusters in a graph. This algorithm takes as input
only an approximate Minimum Spanning Tree (MST) $\mathcal{T}$ released under
weight differential privacy constraints from the graph. Then, the underlying
nonconvex clustering partition is successfully recovered from cutting optimal
cuts on $\mathcal{T}$. As opposed to existing methods, our algorithm is
theoretically well-motivated. Experiments support our theoretical findings.
Fire detection in a still image using colour information
Authors: Oluwarotimi Giwa, Abdsamad Benkrid
Date: 2018-03-10 00:00:00
Tasks: Clustering, Fire Detection

Colour analysis is a crucial step in image-based fire detection algorithms.
Many of the proposed fire detection algorithms in a still image are prone to
false alarms caused by objects with a colour similar to fire. To design a
colour-based system with a better false alarm rate, a new
colour-differentiating conversion matrix, efficient on images of high colour
complexity, is proposed. The elements of this conversion matrix are obtained by
performing K-medoids clustering and Particle Swarm Optimisation procedures on a
fire sample image with a background of high fire-colour similarity. The
proposed conversion matrix is then used to construct two new fire colour
detection frameworks. The first detection method is a two-stage non-linear
image transformation framework, while the second is a direct transformation of
an image with the proposed conversion matrix. A performance comparison of the
proposed methods with alternate methods in the literature was carried out.
Experimental results indicate that the linear image transformation method
outperforms other methods regarding false alarm rate while the non-linear
two-stage image transformation method has the best performance on the F-score
metric and provides a better trade-off between missed detection and false alarm
rate.
Face2Text: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptions
Authors: Albert Gatt, Marc Tanti, Adrian Muscat, Patrizia Paggio, Reuben A. Farrugia, Claudia Borg, Kenneth P. Camilleri, Mike Rosner, Lonneke van der Plas
Date: 2018-03-10 00:00:00
Proceeding: LREC 2018 5
Tasks: Image Description, Image to text

The past few years have witnessed renewed interest in NLP tasks at the interface between vision and language. One intensively-studied problem is that of automatically generating text from images. In this paper, we extend this problem to the more specific domain of face description. Unlike scene descriptions, face descriptions are more fine-grained and rely on attributes extracted from the image, rather than objects and relations. Given that no data exists for this task, we present an ongoing crowdsourcing study to collect a corpus of descriptions of face images taken `in the wild'. To gain a better understanding of the variation we find in face description and the possible issues that this may raise, we also conducted an annotation study on a subset of the corpus. Primarily, we found descriptions to refer to a mixture of attributes, not only physical, but also emotional and inferential, which is bound to create further challenges for current image-to-text methods.
On Cognitive Preferences and the Plausibility of Rule-based Models
Authors: Johannes Fürnkranz, Tomáš Kliegr, Heiko Paulheim
Date: 2018-03-04 00:00:00

It is conventional wisdom in machine learning and data mining that logical
models such as rule sets are more interpretable than other models, and that
among such rule-based models, simpler models are more interpretable than more
complex ones. In this position paper, we question this latter assumption by
focusing on one particular aspect of interpretability, namely the plausibility
of models. Roughly speaking, we equate the plausibility of a model with the
likeliness that a user accepts it as an explanation for a prediction. In
particular, we argue that, all other things being equal, longer explanations
may be more convincing than shorter ones, and that the predominant bias for
shorter models, which is typically necessary for learning powerful
discriminative models, may not be suitable when it comes to user acceptance of
the learned models. To that end, we first recapitulate evidence for and against
this postulate, and then report the results of an evaluation in a
crowd-sourcing study based on about 3.000 judgments. The results do not reveal
a strong preference for simple rules, whereas we can observe a weak preference
for longer rules in some domains. We then relate these results to well-known
cognitive biases such as the conjunction fallacy, the representative heuristic,
or the recogition heuristic, and investigate their relation to rule length and
plausibility.
Good Features to Correlate for Visual Tracking
Authors: Erhan Gundogdu, A. Aydin Alatan
Date: 2017-04-20 00:00:00
Tasks: General Classification, Object, Object Tracking, Visual Object Tracking, Visual Tracking

During the recent years, correlation filters have shown dominant and
spectacular results for visual object tracking. The types of the features that
are employed in these family of trackers significantly affect the performance
of visual tracking. The ultimate goal is to utilize robust features invariant
to any kind of appearance change of the object, while predicting the object
location as properly as in the case of no appearance change. As the deep
learning based methods have emerged, the study of learning features for
specific tasks has accelerated. For instance, discriminative visual tracking
methods based on deep architectures have been studied with promising
performance. Nevertheless, correlation filter based (CFB) trackers confine
themselves to use the pre-trained networks which are trained for object
classification problem. To this end, in this manuscript the problem of learning
deep fully convolutional features for the CFB visual tracking is formulated. In
order to learn the proposed model, a novel and efficient backpropagation
algorithm is presented based on the loss function of the network. The proposed
learning framework enables the network model to be flexible for a custom
design. Moreover, it alleviates the dependency on the network trained for
classification. Extensive performance analysis shows the efficacy of the
proposed custom design in the CFB tracking framework. By fine-tuning the
convolutional parts of a state-of-the-art network and integrating this model to
a CFB tracker, which is the top performing one of VOT2016, 18% increase is
achieved in terms of expected average overlap, and tracking failures are
decreased by 25%, while maintaining the superiority over the state-of-the-art
methods in OTB-2013 and OTB-2015 tracking datasets.
Genaue modellbasierte Identifikation von gynäkologischen Katheterpfaden für die MRT-bildgestützte Brachytherapie
Authors: Andre Mastmeyer
Date: 2018-02-27 00:00:00
Tasks: Computed Tomography (CT)

German text, english abstract: Mortality in gynecologic cancers, including
cervical, ovarian, vaginal and vulvar cancers, is more than 6% internationally
[1]. In many countries external radiotherapy is supplemented by brachytherapy
with high locally administered doses as standard. The superior ability of
magnetic resonance imaging (MRI) to differentiate soft tissue has led to an
increasing use of this imaging technique in the intraoperative planning and
implementation of brachytherapy. A technical challenge associated with the use
of MRI imaging for brachytherapy - in contrast to computed tomography (CT)
imaging - is the dark-diffuse appearance and thus difficult identification of
the catheter paths in the resulting images. This problem is addressed by the
precise method described herein of tracing the catheters from the catheter tip.
The average identification time for a single catheter path was three seconds on
a standard PC. Segmentation time, accuracy and precision are promising
indicators of the value of this method for the clinical application of
image-guided gynecological brachytherapy. After surgery (OP), the healthy
surrounding tissue of the tumor is usually irradiated. This reduces the risk of
leaving behind residual cells that would likely cause a recurrence of the
cancer or the formation of metastases - secondary tumors elsewhere in the body.
In the case of a tumor on the cervix or prostate, the operation is minimally
invasive, ie. the removal of the cancer and the irradiation are performed
cost-effectively and risk-avoiding by keyhole surgery instead of open surgery.
We Built a Fake News & Click-bait Filter: What Happened Next Will Blow Your Mind!
Authors: Georgi Karadzhov, Pepa Gencheva, Preslav Nakov, Ivan Koychev
Date: 2018-03-10 00:00:00
Tasks: Author Profiling

It is completely amazing! Fake news and click-baits have totally invaded the
cyber space. Let us face it: everybody hates them for three simple reasons.
Reason #2 will absolutely amaze you. What these can achieve at the time of
election will completely blow your mind! Now, we all agree, this cannot go on,
you know, somebody has to stop it. So, we did this research on fake
news/click-bait detection and trust us, it is totally great research, it really
is! Make no mistake. This is the best research ever! Seriously, come have a
look, we have it all: neural networks, attention mechanism, sentiment lexicons,
author profiling, you name it. Lexical features, semantic features, we
absolutely have it all. And we have totally tested it, trust us! We have
results, and numbers, really big numbers. The best numbers ever! Oh, and
analysis, absolutely top notch analysis. Interested? Come read the shocking
truth about fake news and click-bait in the Bulgarian cyber space. You won't
believe what we have found!
Rough extreme learning machine: a new classification method based on uncertainty measure
Authors: Lin Feng, Shuliang Xu, Feilong Wang, Shenglan Liu
Date: 2017-10-30 00:00:00
Tasks: Attribute, General Classification

Extreme learning machine (ELM) is a new single hidden layer feedback neural
network. The weights of the input layer and the biases of neurons in hidden
layer are randomly generated, the weights of the output layer can be
analytically determined. ELM has been achieved good results for a large number
of classification tasks. In this paper, a new extreme learning machine called
rough extreme learning machine (RELM) was proposed. RELM uses rough set to
divide data into upper approximation set and lower approximation set, and the
two approximation sets are utilized to train upper approximation neurons and
lower approximation neurons. In addition, an attribute reduction is executed in
this algorithm to remove redundant attributes. The experimental results showed,
comparing with the comparison algorithms, RELM can get a better accuracy and
repeatability in most cases, RELM can not only maintain the advantages of fast
speed, but also effectively cope with the classification task for
high-dimensional data.
Sky detection and log illumination refinement for PDE-based hazy image contrast enhancement
Authors: Uche A. Nnolim
Date: 2017-12-28 00:00:00
Tasks: Image Enhancement

This report presents the results of a sky detection technique used to improve
the performance of a previously developed partial differential equation
(PDE)-based hazy image enhancement algorithm. Additionally, a proposed
alternative method utilizes a function for log illumination refinement to
improve de-hazing results while avoiding over-enhancement of sky or homogeneous
regions. The algorithms were tested with several benchmark and calibration
images and compared with several standard algorithms from the literature.
Results indicate that the algorithms yield mostly consistent results and
surpasses several of the other algorithms in terms of colour and contrast
enhancement in addition to improved edge visibility.
Driving Scene Perception Network: Real-time Joint Detection, Depth Estimation and Semantic Segmentation
Authors: Liangfu Chen, Zeng Yang, Jianjun Ma, Zheng Luo
Date: 2018-03-10 00:00:00
Tasks: Autonomous Driving, Depth Estimation, GPU, Image Segmentation, Multi-Task Learning, object-detection, Object Detection, Segmentation, Semantic Segmentation

As the demand for enabling high-level autonomous driving has increased in
recent years and visual perception is one of the critical features to enable
fully autonomous driving, in this paper, we introduce an efficient approach for
simultaneous object detection, depth estimation and pixel-level semantic
segmentation using a shared convolutional architecture. The proposed network
model, which we named Driving Scene Perception Network (DSPNet), uses
multi-level feature maps and multi-task learning to improve the accuracy and
efficiency of object detection, depth estimation and image segmentation tasks
from a single input image. Hence, the resulting network model uses less than
850 MiB of GPU memory and achieves 14.0 fps on NVIDIA GeForce GTX 1080 with a
1024x512 input image, and both precision and efficiency have been improved over
combination of single tasks.
A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data
Authors: Abien Fred Agarap
Date: 2017-09-10 00:00:00
Tasks: Binary Classification, General Classification, Image Classification, Intrusion Detection, Speech Recognition, Text Classification

Gated Recurrent Unit (GRU) is a recently-developed variation of the long
short-term memory (LSTM) unit, both of which are types of recurrent neural
network (RNN). Through empirical evidence, both models have been proven to be
effective in a wide variety of machine learning tasks such as natural language
processing (Wen et al., 2015), speech recognition (Chorowski et al., 2015), and
text classification (Yang et al., 2016). Conventionally, like most neural
networks, both of the aforementioned RNN variants employ the Softmax function
as its final output layer for its prediction, and the cross-entropy function
for computing its loss. In this paper, we present an amendment to this norm by
introducing linear support vector machine (SVM) as the replacement for Softmax
in the final output layer of a GRU model. Furthermore, the cross-entropy
function shall be replaced with a margin-based function. While there have been
similar studies (Alalshekmubarak & Smith, 2013; Tang, 2013), this proposal is
primarily intended for binary classification on intrusion detection using the
2013 network traffic data from the honeypot systems of Kyoto University.
Results show that the GRU-SVM model performs relatively higher than the
conventional GRU-Softmax model. The proposed model reached a training accuracy
of ~81.54% and a testing accuracy of ~84.15%, while the latter was able to
reach a training accuracy of ~63.07% and a testing accuracy of ~70.75%. In
addition, the juxtaposition of these two final output layers indicate that the
SVM would outperform Softmax in prediction time - a theoretical implication
which was supported by the actual training and testing time in the study.
Speech Recognition: Keyword Spotting Through Image Recognition
Authors: Sanjay Krishna Gouda, Salil Kanetkar, David Harrison, Manfred K. Warmuth
Date: 2018-03-10 00:00:00
Tasks: image-classification, Image Classification, Keyword Spotting, speech-recognition, Speech Recognition

The problem of identifying voice commands has always been a challenge due to the presence of noise and variability in speed, pitch, etc. We will compare the efficacies of several neural network architectures for the speech recognition problem. In particular, we will build a model to determine whether a one second audio clip contains a particular word (out of a set of 10), an unknown word, or silence. The models to be implemented are a CNN recommended by the Tensorflow Speech Recognition tutorial, a low-latency CNN, and an adversarially trained CNN. The result is a demonstration of how to convert a problem in audio recognition to the better-studied domain of image classification, where the powerful techniques of convolutional neural networks are fully developed. Additionally, we demonstrate the applicability of the technique of Virtual Adversarial Training (VAT) to this problem domain, functioning as a powerful regularizer with promising potential future applications.
Newton: Gravitating Towards the Physical Limits of Crossbar Acceleration
Authors: Anirban Nag, Ali Shafiee, Rajeev Balasubramonian, Vivek Srikumar, Naveen Muralimanohar
Date: 2018-03-10 00:00:00

Many recent works have designed accelerators for Convolutional Neural
Networks (CNNs). While digital accelerators have relied on near data
processing, analog accelerators have further reduced data movement by
performing in-situ computation. Recent works take advantage of highly parallel
analog in-situ computation in memristor crossbars to accelerate the many
vector-matrix multiplication operations in CNNs. However, these in-situ
accelerators have two significant short-comings that we address in this work.
First, the ADCs account for a large fraction of chip power and area. Second,
these accelerators adopt a homogeneous design where every resource is
provisioned for the worst case. By addressing both problems, the new
architecture, Newton, moves closer to achieving optimal energy-per-neuron for
crossbar accelerators.
  We introduce multiple new techniques that apply at different levels of the
tile hierarchy. Two of the techniques leverage heterogeneity: one adapts ADC
precision based on the requirements of every sub-computation (with zero impact
on accuracy), and the other designs tiles customized for convolutions or
classifiers. Two other techniques rely on divide-and-conquer numeric algorithms
to reduce computations and ADC pressure. Finally, we place constraints on how a
workload is mapped to tiles, thus helping reduce resource provisioning in
tiles. For a wide range of CNN dataflows and structures, Newton achieves a 77%
decrease in power, 51% improvement in energy efficiency, and 2.2x higher
throughput/area, relative to the state-of-the-art ISAAC accelerator.
Influence of the Event Rate on Discrimination Abilities of Bankruptcy Prediction Models
Authors: Lili Zhang, Jennifer Priestley, Xuelei Ni
Date: 2018-03-10 00:00:00

In bankruptcy prediction, the proportion of events is very low, which is
often oversampled to eliminate this bias. In this paper, we study the influence
of the event rate on discrimination abilities of bankruptcy prediction models.
First the statistical association and significance of public records and
firmographics indicators with the bankruptcy were explored. Then the event rate
was oversampled from 0.12% to 10%, 20%, 30%, 40%, and 50%, respectively. Seven
models were developed, including Logistic Regression, Decision Tree, Random
Forest, Gradient Boosting, Support Vector Machine, Bayesian Network, and Neural
Network. Under different event rates, models were comprehensively evaluated and
compared based on Kolmogorov-Smirnov Statistic, accuracy, F1 score, Type I
error, Type II error, and ROC curve on the hold-out dataset with their best
probability cut-offs. Results show that Bayesian Network is the most
insensitive to the event rate, while Support Vector Machine is the most
sensitive.
Low Rank Variation Dictionary and Inverse Projection Group Sparse Representation Model for Breast Tumor Classification
Authors: Xiaohui Yang, Xiaoying Jiang, WenMing Wu, Juan Zhang, Dan Long, Funa Zhou, Yiming Xu
Date: 2018-03-10 00:00:00
Tasks: General Classification

Sparse representation classification achieves good results by addressing
recognition problem with sufficient training samples per subject. However, SRC
performs not very well for small sample data. In this paper, an
inverse-projection group sparse representation model is presented for breast
tumor classification, which is based on constructing low-rank variation
dictionary. The proposed low-rank variation dictionary tackles tumor
recognition problem from the viewpoint of detecting and using variations in
gene expression profiles of normal and patients, rather than directly using
these samples. The inverse projection group sparsity representation model is
constructed based on taking full using of exist samples and group effect of
microarray gene data. Extensive experiments on public breast tumor microarray
gene expression datasets demonstrate the proposed technique is competitive with
state-of-the-art methods. The results of Breast-1, Breast-2 and Breast-3
databases are 80.81%, 89.10% and 100% respectively, which are better than the
latest literature.