pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Organ Cancer Classification and Survival Analysis
Authors: Stefan Bauer, Nicolas Carion, Peter Schüffler, Thomas Fuchs, Peter Wild, Joachim M. Buhmann
Date: 2016-06-02 00:00:00
Tasks: Cancer Classification, Classification, General Classification, Nuclei Classification, Survival Analysis, Transfer Learning

Accurate and robust cell nuclei classification is the cornerstone for a wider
range of tasks in digital and Computational Pathology. However, most machine
learning systems require extensive labeling from expert pathologists for each
individual problem at hand, with no or limited abilities for knowledge transfer
between datasets and organ sites. In this paper we implement and evaluate a
variety of deep neural network models and model ensembles for nuclei
classification in renal cell cancer (RCC) and prostate cancer (PCa). We propose
a convolutional neural network system based on residual learning which
significantly improves over the state-of-the-art in cell nuclei classification.
Finally, we show that the combination of tissue types during training increases
not only classification accuracy but also overall survival analysis.
A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images
Authors: David Vázquez, Jorge Bernal, F. Javier Sánchez, Gloria Fernández-Esparrach, Antonio M. López, Adriana Romero, Michal Drozdzal, Aaron Courville
Date: 2016-12-02 00:00:00
Tasks: Scene Segmentation, Segmentation, Semantic Segmentation

Colorectal cancer (CRC) is the third cause of cancer death worldwide.
Currently, the standard approach to reduce CRC-related mortality is to perform
regular screening in search for polyps and colonoscopy is the screening tool of
choice. The main limitations of this screening procedure are polyp miss-rate
and inability to perform visual assessment of polyp malignancy. These drawbacks
can be reduced by designing Decision Support Systems (DSS) aiming to help
clinicians in the different stages of the procedure by providing endoluminal
scene segmentation. Thus, in this paper, we introduce an extended benchmark of
colonoscopy image, with the hope of establishing a new strong benchmark for
colonoscopy image analysis research. We provide new baselines on this dataset
by training standard fully convolutional networks (FCN) for semantic
segmentation and significantly outperforming, without any further
post-processing, prior results in endoluminal scene segmentation.
A Survey of Stealth Malware: Attacks, Mitigation Measures, and Steps Toward Autonomous Open World Solutions
Authors: Ethan M. Rudd, Andras Rozsa, Manuel Günther, Terrance E. Boult
Date: 2016-03-19 00:00:00
Tasks: BIG-bench Machine Learning

As our professional, social, and financial existences become increasingly
digitized and as our government, healthcare, and military infrastructures rely
more on computer technologies, they present larger and more lucrative targets
for malware. Stealth malware in particular poses an increased threat because it
is specifically designed to evade detection mechanisms, spreading dormant, in
the wild for extended periods of time, gathering sensitive information or
positioning itself for a high-impact zero-day attack. Policing the growing
attack surface requires the development of efficient anti-malware solutions
with improved generalization to detect novel types of malware and resolve these
occurrences with as little burden on human experts as possible. In this paper,
we survey malicious stealth technologies as well as existing solutions for
detecting and categorizing these countermeasures autonomously. While machine
learning offers promising potential for increasingly autonomous solutions with
improved generalization to new malware types, both at the network level and at
the host level, our findings suggest that several flawed assumptions inherent
to most recognition algorithms prevent a direct mapping between the stealth
malware recognition problem and a machine learning solution. The most notable
of these flawed assumptions is the closed world assumption: that no sample
belonging to a class outside of a static training set will appear at query
time. We present a formalized adaptive open world framework for stealth malware
recognition and relate it mathematically to research from other machine
learning domains.
Decision Theory in an Algebraic Setting
Authors: Maurizio Negri
Date: 2016-12-02 00:00:00

In decision theory an act is a function from a set of conditions to the set
of real numbers. The set of conditions is a partition in some algebra of
events. The expected value of an act can be calculated when a probability
measure is given. We adopt an algebraic point of view by substituting the
algebra of events with a finite distributive lattice and the probability
measure with a lattice valuation. We introduce a partial order on acts that
generalizes the dominance relation and show that the set of acts is a lattice
with respect to this order. Finally we analyze some different kinds of
comparison between acts, without supposing a common set of conditions for the
acts to be compared.
When to Reset Your Keys: Optimal Timing of Security Updates via Learning
Authors: Zizhan Zheng, Ness B. Shroff, Prasant Mohapatra
Date: 2016-12-01 00:00:00

Cybersecurity is increasingly threatened by advanced and persistent attacks.
As these attacks are often designed to disable a system (or a critical
resource, e.g., a user account) repeatedly, it is crucial for the defender to
keep updating its security measures to strike a balance between the risk of
being compromised and the cost of security updates. Moreover, these decisions
often need to be made with limited and delayed feedback due to the stealthy
nature of advanced attacks. In addition to targeted attacks, such an optimal
timing policy under incomplete information has broad applications in
cybersecurity. Examples include key rotation, password change, application of
patches, and virtual machine refreshing. However, rigorous studies of optimal
timing are rare. Further, existing solutions typically rely on a pre-defined
attack model that is known to the defender, which is often not the case in
practice. In this work, we make an initial effort towards achieving optimal
timing of security updates in the face of unknown stealthy attacks. We consider
a variant of the influential FlipIt game model with asymmetric feedback and
unknown attack time distribution, which provides a general model to consecutive
security updates. The defender's problem is then modeled as a time associative
bandit problem with dependent arms. We derive upper confidence bound based
learning policies that achieve low regret compared with optimal periodic
defense strategies that can only be derived when attack time distributions are
known.
A semidefinite program for unbalanced multisection in the stochastic block model
Authors: Amelia Perry, Alexander S. Wein
Date: 2015-07-20 00:00:00
Tasks: Community Detection, Stochastic Block Model

We propose a semidefinite programming (SDP) algorithm for community detection
in the stochastic block model, a popular model for networks with latent
community structure. We prove that our algorithm achieves exact recovery of the
latent communities, up to the information-theoretic limits determined by Abbe
and Sandon (2015). Our result extends prior SDP approaches by allowing for many
communities of different sizes. By virtue of a semidefinite approach, our
algorithms succeed against a semirandom variant of the stochastic block model,
guaranteeing a form of robustness and generalization. We further explore how
semirandom models can lend insight into both the strengths and limitations of
SDPs in this setting.
A Randomized Approach to Efficient Kernel Clustering
Authors: Farhad Pourkamali-Anaraki, Stephen Becker
Date: 2016-08-26 00:00:00
Tasks: Clustering

Kernel-based K-means clustering has gained popularity due to its simplicity
and the power of its implicit non-linear representation of the data. A dominant
concern is the memory requirement since memory scales as the square of the
number of data points. We provide a new analysis of a class of approximate
kernel methods that have more modest memory requirements, and propose a
specific one-pass randomized kernel approximation followed by standard K-means
on the transformed data. The analysis and experiments suggest the method is
accurate, while requiring drastically less memory than standard kernel K-means
and significantly less memory than Nystrom based approximations.
Long-Term Trends in the Public Perception of Artificial Intelligence
Authors: Ethan Fast, Eric Horvitz
Date: 2016-09-16 00:00:00

Analyses of text corpora over time can reveal trends in beliefs, interest,
and sentiment about a topic. We focus on views expressed about artificial
intelligence (AI) in the New York Times over a 30-year period. General
interest, awareness, and discussion about AI has waxed and waned since the
field was founded in 1956. We present a set of measures that captures levels of
engagement, measures of pessimism and optimism, the prevalence of specific
hopes and concerns, and topics that are linked to discussions about AI over
decades. We find that discussion of AI has increased sharply since 2009, and
that these discussions have been consistently more optimistic than pessimistic.
However, when we examine specific concerns, we find that worries of loss of
control of AI, ethical concerns for AI, and the negative impact of AI on work
have grown in recent years. We also find that hopes for AI in healthcare and
education have increased over time.
Cognitive Deep Machine Can Train Itself
Authors: András Lőrincz, Máté Csákvári, Áron Fóthi, Zoltán Ádám Milacski, András Sárkány, Zoltán Tősér
Date: 2016-12-02 00:00:00
Tasks: Outlier Detection

Machine learning is making substantial progress in diverse applications. The
success is mostly due to advances in deep learning. However, deep learning can
make mistakes and its generalization abilities to new tasks are questionable.
We ask when and how one can combine network outputs, when (i) details of the
observations are evaluated by learned deep components and (ii) facts and
confirmation rules are available in knowledge based systems. We show that in
limited contexts the required number of training samples can be low and
self-improvement of pre-trained networks in more general context is possible.
We argue that the combination of sparse outlier detection with deep components
that can support each other diminish the fragility of deep methods, an
important requirement for engineering applications. We argue that supervised
learning of labels may be fully eliminated under certain conditions: a
component based architecture together with a knowledge based system can train
itself and provide high quality answers. We demonstrate these concepts on the
State Farm Distracted Driver Detection benchmark. We argue that the view of the
Study Panel (2016) may overestimate the requirements on `years of focused
research' and `careful, unique construction' for `AI systems'.
What is the right way to represent document images?
Authors: Gabriela Csurka, Diane Larlus, Albert Gordo, Jon Almazan
Date: 2016-03-03 00:00:00
Tasks: Clustering, Retrieval

In this article we study the problem of document image representation based
on visual features. We propose a comprehensive experimental study that compares
three types of visual document image representations: (1) traditional so-called
shallow features, such as the RunLength and the Fisher-Vector descriptors, (2)
deep features based on Convolutional Neural Networks, and (3) features
extracted from hybrid architectures that take inspiration from the two previous
ones.
  We evaluate these features in several tasks (i.e. classification, clustering,
and retrieval) and in different setups (e.g. domain transfer) using several
public and in-house datasets. Our results show that deep features generally
outperform other types of features when there is no domain shift and the new
task is closely related to the one used to train the model. However, when a
large domain or task shift is present, the Fisher-Vector shallow features
generalize better and often obtain the best results.
Automated assessment of non-native learner essays: Investigating the role of linguistic features
Authors: Sowmya Vajjala
Date: 2016-12-02 00:00:00

Automatic essay scoring (AES) refers to the process of scoring free text
responses to given prompts, considering human grader scores as the gold
standard. Writing such essays is an essential component of many language and
aptitude exams. Hence, AES became an active and established area of research,
and there are many proprietary systems used in real life applications today.
However, not much is known about which specific linguistic features are useful
for prediction and how much of this is consistent across datasets. This article
addresses that by exploring the role of various linguistic features in
automatic essay scoring using two publicly available datasets of non-native
English essays written in test taking scenarios. The linguistic properties are
modeled by encoding lexical, syntactic, discourse and error types of learner
language in the feature set. Predictive models are then developed using these
features on both datasets and the most predictive features are compared. While
the results show that the feature set used results in good predictive models
with both datasets, the question "what are the most predictive features?" has a
different answer for each dataset.
Probabilistic Neural Programs
Authors: Kenton W. Murray, Jayant Krishnamurthy
Date: 2016-12-02 00:00:00
Tasks: Program induction, Question Answering

We present probabilistic neural programs, a framework for program induction
that permits flexible specification of both a computational model and inference
algorithm while simultaneously enabling the use of deep neural networks.
Probabilistic neural programs combine a computation graph for specifying a
neural network with an operator for weighted nondeterministic choice. Thus, a
program describes both a collection of decisions as well as the neural network
architecture used to make each one. We evaluate our approach on a challenging
diagram question answering task where probabilistic neural programs correctly
execute nearly twice as many programs as a baseline model.
Efficiently Creating 3D Training Data for Fine Hand Pose Estimation
Authors: Markus Oberweger, Gernot Riegler, Paul Wohlhart, Vincent Lepetit
Date: 2016-05-11 00:00:00
Proceeding: CVPR 2016 6
Tasks: Hand Pose Estimation, Pose Estimation

While many recent hand pose estimation methods critically rely on a training
set of labelled frames, the creation of such a dataset is a challenging task
that has been overlooked so far. As a result, existing datasets are limited to
a few sequences and individuals, with limited accuracy, and this prevents these
methods from delivering their full potential. We propose a semi-automated
method for efficiently and accurately labeling each frame of a hand depth video
with the corresponding 3D locations of the joints: The user is asked to provide
only an estimate of the 2D reprojections of the visible joints in some
reference frames, which are automatically selected to minimize the labeling
work by efficiently optimizing a sub-modular loss function. We then exploit
spatial, temporal, and appearance constraints to retrieve the full 3D poses of
the hand over the complete sequence. We show that this data can be used to
train a recent state-of-the-art hand pose estimation method, leading to
increased accuracy. The code and dataset can be found on our website
https://cvarlab.icg.tugraz.at/projects/hand_detection/
Hands Deep in Deep Learning for Hand Pose Estimation
Authors: Markus Oberweger, Paul Wohlhart, Vincent Lepetit
Date: 2015-02-24 00:00:00
Tasks: Deep Learning, Hand Pose Estimation, Pose Estimation

We introduce and evaluate several architectures for Convolutional Neural
Networks to predict the 3D joint locations of a hand given a depth map. We
first show that a prior on the 3D pose can be easily introduced and
significantly improves the accuracy and reliability of the predictions. We also
show how to use context efficiently to deal with ambiguities between fingers.
These two contributions allow us to significantly outperform the
state-of-the-art on several challenging benchmarks, both in terms of accuracy
and computation times.
Identifying and Categorizing Anomalies in Retinal Imaging Data
Authors: Philipp Seeböck, Sebastian Waldstein, Sophie Klimscha, Bianca S. Gerendas, René Donner, Thomas Schlegl, Ursula Schmidt-Erfurth, Georg Langs
Date: 2016-12-02 00:00:00
Tasks: Clustering, Management, Prognosis

The identification and quantification of markers in medical images is
critical for diagnosis, prognosis and management of patients in clinical
practice. Supervised- or weakly supervised training enables the detection of
findings that are known a priori. It does not scale well, and a priori
definition limits the vocabulary of markers to known entities reducing the
accuracy of diagnosis and prognosis. Here, we propose the identification of
anomalies in large-scale medical imaging data using healthy examples as a
reference. We detect and categorize candidates for anomaly findings untypical
for the observed data. A deep convolutional autoencoder is trained on healthy
retinal images. The learned model generates a new feature representation, and
the distribution of healthy retinal patches is estimated by a one-class support
vector machine. Results demonstrate that we can identify pathologic regions in
images without using expert annotations. A subsequent clustering categorizes
findings into clinically meaningful classes. In addition the learned features
outperform standard embedding approaches in a classification task.
Fractal Dimension Pattern Based Multiresolution Analysis for Rough Estimator of Person-Dependent Audio Emotion Recognition
Authors: Miao Cheng, Ah Chung Tsoi
Date: 2016-07-01 00:00:00
Tasks: Audio Emotion Recognition, Emotion Recognition

As a general means of expression, audio analysis and recognition has
attracted much attentions for its wide applications in real-life world. Audio
emotion recognition (AER) attempts to understand emotional states of human with
the given utterance signals, and has been studied abroad for its further
development on friendly human-machine interfaces. Distinguish from other
existing works, the person-dependent patterns of audio emotions are conducted,
and fractal dimension features are calculated for acoustic feature extraction.
Furthermore, it is able to efficiently learn intrinsic characteristics of
auditory emotions, while the utterance features are learned from fractal
dimensions of each sub-bands. Experimental results show the proposed method is
able to provide comparative performance for audio emotion recognition.
Predicting Patient State-of-Health using Sliding Window and Recurrent Classifiers
Authors: Adam McCarthy, Christopher K. I. Williams
Date: 2016-12-02 00:00:00
Tasks: Time Series, Time Series Analysis

Bedside monitors in Intensive Care Units (ICUs) frequently sound incorrectly,
slowing response times and desensitising nurses to alarms (Chambrin, 2001),
causing true alarms to be missed (Hug et al., 2011). We compare sliding window
predictors with recurrent predictors to classify patient state-of-health from
ICU multivariate time series; we report slightly improved performance for the
RNN for three out of four targets.
Centrog Feature technique for vehicle type recognition at day and night times
Authors: Martins E. Irhebhude, Philip O. Odion, Darius T. Chinyio
Date: 2016-12-02 00:00:00

This work proposes a feature-based technique to recognize vehicle types
within day and night times. Support vector machine (SVM) classifier is applied
on image histogram and CENsus Transformed histogRam Oriented Gradient (CENTROG)
features in order to classify vehicle types during the day and night. Thermal
images were used for the night time experiments. Although thermal images suffer
from low image resolution, lack of colour and poor texture information, they
offer the advantage of being unaffected by high intensity light sources such as
vehicle headlights which tend to render normal images unsuitable for night time
image capturing and subsequent analysis. Since contour is useful in shape based
categorisation and the most distinctive feature within thermal images, CENTROG
is used to capture this feature information and is used within the experiments.
The experimental results so obtained were compared with those obtained by
employing the CENsus TRansformed hISTogram (CENTRIST). Experimental results
revealed that CENTROG offers better recognition accuracies for both day and
night times vehicle types recognition.
A General Framework for Density Based Time Series Clustering Exploiting a Novel Admissible Pruning Strategy
Authors: Nurjahan Begum, Liudmila Ulanova, Hoang Anh Dau, Jun Wang, Eamonn Keogh
Date: 2016-12-02 00:00:00
Tasks: Astronomy, Clustering, Dynamic Time Warping, Outlier Detection, Time Series, Time Series Analysis, Time Series Clustering

Time Series Clustering is an important subroutine in many higher-level data
mining analyses, including data editing for classifiers, summarization, and
outlier detection. It is well known that for similarity search the superiority
of Dynamic Time Warping (DTW) over Euclidean distance gradually diminishes as
we consider ever larger datasets. However, as we shall show, the same is not
true for clustering. Clustering time series under DTW remains a computationally
expensive operation. In this work, we address this issue in two ways. We
propose a novel pruning strategy that exploits both the upper and lower bounds
to prune off a very large fraction of the expensive distance calculations. This
pruning strategy is admissible and gives us provably identical results to the
brute force algorithm, but is at least an order of magnitude faster. For
datasets where even this level of speedup is inadequate, we show that we can
use a simple heuristic to order the unavoidable calculations in a
most-useful-first ordering, thus casting the clustering into an anytime
framework. We demonstrate the utility of our ideas with both single and
multidimensional case studies in the domains of astronomy, speech physiology,
medicine and entomology. In addition, we show the generality of our clustering
framework to other domains by efficiently obtaining semantically significant
clusters in protein sequences using the Edit Distance, the discrete data
analogue of DTW.
Machine Learning for Dental Image Analysis
Authors: Young-jun Yu
Date: 2016-11-30 00:00:00
Tasks: BIG-bench Machine Learning

In order to study the application of artificial intelligence (AI) to dental
imaging, we applied AI technology to classify a set of panoramic radiographs
using (a) a convolutional neural network (CNN) which is a form of an artificial
neural network (ANN), (b) representative image cognition algorithms that
implement scale-invariant feature transform (SIFT), and (c) histogram of
oriented gradients (HOG).
M2CAI Workflow Challenge: Convolutional Neural Networks with Time Smoothing and Hidden Markov Model for Video Frames Classification
Authors: Rémi Cadène, Thomas Robert, Nicolas Thome, Matthieu Cord
Date: 2016-10-18 00:00:00
Tasks: General Classification

Our approach is among the three best to tackle the M2CAI Workflow challenge.
The latter consists in recognizing the operation phase for each frames of
endoscopic videos. In this technical report, we compare several classification
models and temporal smoothing methods. Our submitted solution is a fine tuned
Residual Network-200 on 80% of the training set with temporal smoothing using
simple temporal averaging of the predictions and a Hidden Markov Model modeling
the sequence.