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1703.04986 | Veronika Cheplygina | Veronika Cheplygina and Lauge S{\o}rensen and David M. J. Tax and
Marleen de Bruijne and Marco Loog | Label Stability in Multiple Instance Learning | Published at MICCAI 2015 | null | 10.1007/978-3-319-24553-9_66 | null | cs.CV stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We address the problem of \emph{instance label stability} in multiple
instance learning (MIL) classifiers. These classifiers are trained only on
globally annotated images (bags), but often can provide fine-grained
annotations for image pixels or patches (instances). This is interesting for
computer aided diagnosis (CAD) and other medical image analysis tasks for which
only a coarse labeling is provided. Unfortunately, the instance labels may be
unstable. This means that a slight change in training data could potentially
lead to abnormalities being detected in different parts of the image, which is
undesirable from a CAD point of view. Despite MIL gaining popularity in the CAD
literature, this issue has not yet been addressed. We investigate the stability
of instance labels provided by several MIL classifiers on 5 different datasets,
of which 3 are medical image datasets (breast histopathology, diabetic
retinopathy and computed tomography lung images). We propose an unsupervised
measure to evaluate instance stability, and demonstrate that a
performance-stability trade-off can be made when comparing MIL classifiers.
| [
{
"version": "v1",
"created": "Wed, 15 Mar 2017 07:46:18 GMT"
}
] | 2017-03-16T00:00:00 | [
[
"Cheplygina",
"Veronika",
""
],
[
"Sørensen",
"Lauge",
""
],
[
"Tax",
"David M. J.",
""
],
[
"de Bruijne",
"Marleen",
""
],
[
"Loog",
"Marco",
""
]
] | TITLE: Label Stability in Multiple Instance Learning
ABSTRACT: We address the problem of \emph{instance label stability} in multiple
instance learning (MIL) classifiers. These classifiers are trained only on
globally annotated images (bags), but often can provide fine-grained
annotations for image pixels or patches (instances). This is interesting for
computer aided diagnosis (CAD) and other medical image analysis tasks for which
only a coarse labeling is provided. Unfortunately, the instance labels may be
unstable. This means that a slight change in training data could potentially
lead to abnormalities being detected in different parts of the image, which is
undesirable from a CAD point of view. Despite MIL gaining popularity in the CAD
literature, this issue has not yet been addressed. We investigate the stability
of instance labels provided by several MIL classifiers on 5 different datasets,
of which 3 are medical image datasets (breast histopathology, diabetic
retinopathy and computed tomography lung images). We propose an unsupervised
measure to evaluate instance stability, and demonstrate that a
performance-stability trade-off can be made when comparing MIL classifiers.
| no_new_dataset | 0.952662 |
1703.05060 | Dave Zachariah | Dave Zachariah and Petre Stoica and Thomas B. Sch\"on | Online Learning for Distribution-Free Prediction | null | null | null | null | cs.LG stat.CO stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We develop an online learning method for prediction, which is important in
problems with large and/or streaming data sets. We formulate the learning
approach using a covariance-fitting methodology, and show that the resulting
predictor has desirable computational and distribution-free properties: It is
implemented online with a runtime that scales linearly in the number of
samples; has a constant memory requirement; avoids local minima problems; and
prunes away redundant feature dimensions without relying on restrictive
assumptions on the data distribution. In conjunction with the split conformal
approach, it also produces distribution-free prediction confidence intervals in
a computationally efficient manner. The method is demonstrated on both real and
synthetic datasets.
| [
{
"version": "v1",
"created": "Wed, 15 Mar 2017 10:20:32 GMT"
}
] | 2017-03-16T00:00:00 | [
[
"Zachariah",
"Dave",
""
],
[
"Stoica",
"Petre",
""
],
[
"Schön",
"Thomas B.",
""
]
] | TITLE: Online Learning for Distribution-Free Prediction
ABSTRACT: We develop an online learning method for prediction, which is important in
problems with large and/or streaming data sets. We formulate the learning
approach using a covariance-fitting methodology, and show that the resulting
predictor has desirable computational and distribution-free properties: It is
implemented online with a runtime that scales linearly in the number of
samples; has a constant memory requirement; avoids local minima problems; and
prunes away redundant feature dimensions without relying on restrictive
assumptions on the data distribution. In conjunction with the split conformal
approach, it also produces distribution-free prediction confidence intervals in
a computationally efficient manner. The method is demonstrated on both real and
synthetic datasets.
| no_new_dataset | 0.948346 |
1703.05061 | Matthias Ochs | Matthias Ochs, Henry Bradler and Rudolf Mester | Learning Rank Reduced Interpolation with Principal Component Analysis | Accepted at Intelligent Vehicles Symposium (IV), Los Angeles, USA,
June 2017 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In computer vision most iterative optimization algorithms, both sparse and
dense, rely on a coarse and reliable dense initialization to bootstrap their
optimization procedure. For example, dense optical flow algorithms profit
massively in speed and robustness if they are initialized well in the basin of
convergence of the used loss function. The same holds true for methods as
sparse feature tracking when initial flow or depth information for new features
at arbitrary positions is needed. This makes it extremely important to have
techniques at hand that allow to obtain from only very few available
measurements a dense but still approximative sketch of a desired 2D structure
(e.g. depth maps, optical flow, disparity maps, etc.). The 2D map is regarded
as sample from a 2D random process. The method presented here exploits the
complete information given by the principal component analysis (PCA) of that
process, the principal basis and its prior distribution. The method is able to
determine a dense reconstruction from sparse measurement. When facing
situations with only very sparse measurements, typically the number of
principal components is further reduced which results in a loss of
expressiveness of the basis. We overcome this problem and inject prior
knowledge in a maximum a posterior (MAP) approach. We test our approach on the
KITTI and the virtual KITTI datasets and focus on the interpolation of depth
maps for driving scenes. The evaluation of the results show good agreement to
the ground truth and are clearly better than results of interpolation by the
nearest neighbor method which disregards statistical information.
| [
{
"version": "v1",
"created": "Wed, 15 Mar 2017 10:22:21 GMT"
}
] | 2017-03-16T00:00:00 | [
[
"Ochs",
"Matthias",
""
],
[
"Bradler",
"Henry",
""
],
[
"Mester",
"Rudolf",
""
]
] | TITLE: Learning Rank Reduced Interpolation with Principal Component Analysis
ABSTRACT: In computer vision most iterative optimization algorithms, both sparse and
dense, rely on a coarse and reliable dense initialization to bootstrap their
optimization procedure. For example, dense optical flow algorithms profit
massively in speed and robustness if they are initialized well in the basin of
convergence of the used loss function. The same holds true for methods as
sparse feature tracking when initial flow or depth information for new features
at arbitrary positions is needed. This makes it extremely important to have
techniques at hand that allow to obtain from only very few available
measurements a dense but still approximative sketch of a desired 2D structure
(e.g. depth maps, optical flow, disparity maps, etc.). The 2D map is regarded
as sample from a 2D random process. The method presented here exploits the
complete information given by the principal component analysis (PCA) of that
process, the principal basis and its prior distribution. The method is able to
determine a dense reconstruction from sparse measurement. When facing
situations with only very sparse measurements, typically the number of
principal components is further reduced which results in a loss of
expressiveness of the basis. We overcome this problem and inject prior
knowledge in a maximum a posterior (MAP) approach. We test our approach on the
KITTI and the virtual KITTI datasets and focus on the interpolation of depth
maps for driving scenes. The evaluation of the results show good agreement to
the ground truth and are clearly better than results of interpolation by the
nearest neighbor method which disregards statistical information.
| no_new_dataset | 0.945096 |
1703.05065 | Matthias Ochs | Henry Bradler, Matthias Ochs and Rudolf Mester | Joint Epipolar Tracking (JET): Simultaneous optimization of epipolar
geometry and feature correspondences | Accepted at IEEE Winter Conference on Applications of Computer Vision
(WACV), Santa Rosa, USA, March 2017 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Traditionally, pose estimation is considered as a two step problem. First,
feature correspondences are determined by direct comparison of image patches,
or by associating feature descriptors. In a second step, the relative pose and
the coordinates of corresponding points are estimated, most often by minimizing
the reprojection error (RPE). RPE optimization is based on a loss function that
is merely aware of the feature pixel positions but not of the underlying image
intensities. In this paper, we propose a sparse direct method which introduces
a loss function that allows to simultaneously optimize the unscaled relative
pose, as well as the set of feature correspondences directly considering the
image intensity values. Furthermore, we show how to integrate statistical prior
information on the motion into the optimization process. This constructive
inclusion of a Bayesian bias term is particularly efficient in application
cases with a strongly predictable (short term) dynamic, e.g. in a driving
scenario. In our experiments, we demonstrate that the JET algorithm we propose
outperforms the classical reprojection error optimization on two synthetic
datasets and on the KITTI dataset. The JET algorithm runs in real-time on a
single CPU thread.
| [
{
"version": "v1",
"created": "Wed, 15 Mar 2017 10:30:21 GMT"
}
] | 2017-03-16T00:00:00 | [
[
"Bradler",
"Henry",
""
],
[
"Ochs",
"Matthias",
""
],
[
"Mester",
"Rudolf",
""
]
] | TITLE: Joint Epipolar Tracking (JET): Simultaneous optimization of epipolar
geometry and feature correspondences
ABSTRACT: Traditionally, pose estimation is considered as a two step problem. First,
feature correspondences are determined by direct comparison of image patches,
or by associating feature descriptors. In a second step, the relative pose and
the coordinates of corresponding points are estimated, most often by minimizing
the reprojection error (RPE). RPE optimization is based on a loss function that
is merely aware of the feature pixel positions but not of the underlying image
intensities. In this paper, we propose a sparse direct method which introduces
a loss function that allows to simultaneously optimize the unscaled relative
pose, as well as the set of feature correspondences directly considering the
image intensity values. Furthermore, we show how to integrate statistical prior
information on the motion into the optimization process. This constructive
inclusion of a Bayesian bias term is particularly efficient in application
cases with a strongly predictable (short term) dynamic, e.g. in a driving
scenario. In our experiments, we demonstrate that the JET algorithm we propose
outperforms the classical reprojection error optimization on two synthetic
datasets and on the KITTI dataset. The JET algorithm runs in real-time on a
single CPU thread.
| no_new_dataset | 0.947039 |
1703.05082 | Fabricio Murai | Fabricio Murai, Diogo Renn\'o, Bruno Ribeiro, Gisele L. Pappa, Don
Towsley, Krista Gile | Selective Harvesting over Networks | 28 pages, 9 figures | null | null | null | cs.SI cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Active search (AS) on graphs focuses on collecting certain labeled nodes
(targets) given global knowledge of the network topology and its edge weights
under a query budget. However, in most networks, nodes, topology and edge
weights are all initially unknown. We introduce selective harvesting, a variant
of AS where the next node to be queried must be chosen among the neighbors of
the current queried node set; the available training data for deciding which
node to query is restricted to the subgraph induced by the queried set (and
their node attributes) and their neighbors (without any node or edge
attributes). Therefore, selective harvesting is a sequential decision problem,
where we must decide which node to query at each step. A classifier trained in
this scenario suffers from a tunnel vision effect: without recourse to
independent sampling, the urge to query promising nodes forces classifiers to
gather increasingly biased training data, which we show significantly hurts the
performance of AS methods and standard classifiers. We find that it is possible
to collect a much larger set of targets by using multiple classifiers, not by
combining their predictions as an ensemble, but switching between classifiers
used at each step, as a way to ease the tunnel vision effect. We discover that
switching classifiers collects more targets by (a) diversifying the training
data and (b) broadening the choices of nodes that can be queried next. This
highlights an exploration, exploitation, and diversification trade-off in our
problem that goes beyond the exploration and exploitation duality found in
classic sequential decision problems. From these observations we propose D3TS,
a method based on multi-armed bandits for non-stationary stochastic processes
that enforces classifier diversity, matching or exceeding the performance of
competing methods on seven real network datasets in our evaluation.
| [
{
"version": "v1",
"created": "Wed, 15 Mar 2017 11:17:02 GMT"
}
] | 2017-03-16T00:00:00 | [
[
"Murai",
"Fabricio",
""
],
[
"Rennó",
"Diogo",
""
],
[
"Ribeiro",
"Bruno",
""
],
[
"Pappa",
"Gisele L.",
""
],
[
"Towsley",
"Don",
""
],
[
"Gile",
"Krista",
""
]
] | TITLE: Selective Harvesting over Networks
ABSTRACT: Active search (AS) on graphs focuses on collecting certain labeled nodes
(targets) given global knowledge of the network topology and its edge weights
under a query budget. However, in most networks, nodes, topology and edge
weights are all initially unknown. We introduce selective harvesting, a variant
of AS where the next node to be queried must be chosen among the neighbors of
the current queried node set; the available training data for deciding which
node to query is restricted to the subgraph induced by the queried set (and
their node attributes) and their neighbors (without any node or edge
attributes). Therefore, selective harvesting is a sequential decision problem,
where we must decide which node to query at each step. A classifier trained in
this scenario suffers from a tunnel vision effect: without recourse to
independent sampling, the urge to query promising nodes forces classifiers to
gather increasingly biased training data, which we show significantly hurts the
performance of AS methods and standard classifiers. We find that it is possible
to collect a much larger set of targets by using multiple classifiers, not by
combining their predictions as an ensemble, but switching between classifiers
used at each step, as a way to ease the tunnel vision effect. We discover that
switching classifiers collects more targets by (a) diversifying the training
data and (b) broadening the choices of nodes that can be queried next. This
highlights an exploration, exploitation, and diversification trade-off in our
problem that goes beyond the exploration and exploitation duality found in
classic sequential decision problems. From these observations we propose D3TS,
a method based on multi-armed bandits for non-stationary stochastic processes
that enforces classifier diversity, matching or exceeding the performance of
competing methods on seven real network datasets in our evaluation.
| no_new_dataset | 0.950411 |
1703.05126 | Pengfei Zuo | Pengfei Zuo, Yu Hua, Cong Wang, Wen Xia, Shunde Cao, Yukun Zhou,
Yuanyuan Sun | Bandwidth-efficient Storage Services for Mitigating Side Channel Attack | null | null | null | null | cs.CR cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Data deduplication is able to effectively identify and eliminate redundant
data and only maintain a single copy of files and chunks. Hence, it is widely
used in cloud storage systems to save storage space and network bandwidth.
However, the occurrence of deduplication can be easily identified by monitoring
and analyzing network traffic, which leads to the risk of user privacy leakage.
The attacker can carry out a very dangerous side channel attack, i.e.,
learn-the-remaining-information (LRI) attack, to reveal users' privacy
information by exploiting the side channel of network traffic in deduplication.
Existing work addresses the LRI attack at the cost of the high bandwidth
efficiency of deduplication. In order to address this problem, we propose a
simple yet effective scheme, called randomized redundant chunk scheme (RRCS),
to significantly mitigate the risk of the LRI attack while maintaining the high
bandwidth efficiency of deduplication. The basic idea behind RRCS is to add
randomized redundant chunks to mix up the real deduplication states of files
used for the LRI attack, which effectively obfuscates the view of the attacker,
who attempts to exploit the side channel of network traffic for the LRI attack.
Our security analysis shows that RRCS could significantly mitigate the risk of
the LRI attack. We implement the RRCS prototype and evaluate it by using three
large-scale real-world datasets. Experimental results demonstrate the
efficiency and efficacy of RRCS.
| [
{
"version": "v1",
"created": "Wed, 15 Mar 2017 12:45:17 GMT"
}
] | 2017-03-16T00:00:00 | [
[
"Zuo",
"Pengfei",
""
],
[
"Hua",
"Yu",
""
],
[
"Wang",
"Cong",
""
],
[
"Xia",
"Wen",
""
],
[
"Cao",
"Shunde",
""
],
[
"Zhou",
"Yukun",
""
],
[
"Sun",
"Yuanyuan",
""
]
] | TITLE: Bandwidth-efficient Storage Services for Mitigating Side Channel Attack
ABSTRACT: Data deduplication is able to effectively identify and eliminate redundant
data and only maintain a single copy of files and chunks. Hence, it is widely
used in cloud storage systems to save storage space and network bandwidth.
However, the occurrence of deduplication can be easily identified by monitoring
and analyzing network traffic, which leads to the risk of user privacy leakage.
The attacker can carry out a very dangerous side channel attack, i.e.,
learn-the-remaining-information (LRI) attack, to reveal users' privacy
information by exploiting the side channel of network traffic in deduplication.
Existing work addresses the LRI attack at the cost of the high bandwidth
efficiency of deduplication. In order to address this problem, we propose a
simple yet effective scheme, called randomized redundant chunk scheme (RRCS),
to significantly mitigate the risk of the LRI attack while maintaining the high
bandwidth efficiency of deduplication. The basic idea behind RRCS is to add
randomized redundant chunks to mix up the real deduplication states of files
used for the LRI attack, which effectively obfuscates the view of the attacker,
who attempts to exploit the side channel of network traffic for the LRI attack.
Our security analysis shows that RRCS could significantly mitigate the risk of
the LRI attack. We implement the RRCS prototype and evaluate it by using three
large-scale real-world datasets. Experimental results demonstrate the
efficiency and efficacy of RRCS.
| no_new_dataset | 0.942665 |
1703.05230 | Vincent Andrearczyk | Vincent Andrearczyk and Paul F. Whelan | Texture segmentation with Fully Convolutional Networks | 13 pages, 4 figures, 3 tables | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the last decade, deep learning has contributed to advances in a wide range
computer vision tasks including texture analysis. This paper explores a new
approach for texture segmentation using deep convolutional neural networks,
sharing important ideas with classic filter bank based texture segmentation
methods. Several methods are developed to train Fully Convolutional Networks to
segment textures in various applications. We show in particular that these
networks can learn to recognize and segment a type of texture, e.g. wood and
grass from texture recognition datasets (no training segmentation). We
demonstrate that Fully Convolutional Networks can learn from repetitive
patterns to segment a particular texture from a single image or even a part of
an image. We take advantage of these findings to develop a method that is
evaluated on a series of supervised and unsupervised experiments and improve
the state of the art on the Prague texture segmentation datasets.
| [
{
"version": "v1",
"created": "Wed, 15 Mar 2017 16:14:52 GMT"
}
] | 2017-03-16T00:00:00 | [
[
"Andrearczyk",
"Vincent",
""
],
[
"Whelan",
"Paul F.",
""
]
] | TITLE: Texture segmentation with Fully Convolutional Networks
ABSTRACT: In the last decade, deep learning has contributed to advances in a wide range
computer vision tasks including texture analysis. This paper explores a new
approach for texture segmentation using deep convolutional neural networks,
sharing important ideas with classic filter bank based texture segmentation
methods. Several methods are developed to train Fully Convolutional Networks to
segment textures in various applications. We show in particular that these
networks can learn to recognize and segment a type of texture, e.g. wood and
grass from texture recognition datasets (no training segmentation). We
demonstrate that Fully Convolutional Networks can learn from repetitive
patterns to segment a particular texture from a single image or even a part of
an image. We take advantage of these findings to develop a method that is
evaluated on a series of supervised and unsupervised experiments and improve
the state of the art on the Prague texture segmentation datasets.
| no_new_dataset | 0.951188 |
1703.05267 | Tim Weninger PhD | Maria Glenski, Corey Pennycuff, Tim Weninger | Consumers and Curators: Browsing and Voting Patterns on Reddit | 16 pages, 12 figures, 2 tables | null | null | null | cs.SI cs.HC | http://creativecommons.org/licenses/by/4.0/ | As crowd-sourced curation of news and information become the norm, it is
important to understand not only how individuals consume information through
social news Web sites, but also how they contribute to their ranking systems.
In the present work, we introduce and make available a new dataset containing
the activity logs that recorded all activity for 309 Reddit users for one year.
Using this newly collected data, we present findings that highlight the
browsing and voting behavior of the study's participants. We find that most
users do not read the article that they vote on, and that, in total, 73% of
posts were rated (ie, upvoted or downvoted) without first viewing the content.
We also show evidence of cognitive fatigue in the browsing sessions of users
that are most likely to vote.
| [
{
"version": "v1",
"created": "Wed, 15 Mar 2017 17:06:31 GMT"
}
] | 2017-03-16T00:00:00 | [
[
"Glenski",
"Maria",
""
],
[
"Pennycuff",
"Corey",
""
],
[
"Weninger",
"Tim",
""
]
] | TITLE: Consumers and Curators: Browsing and Voting Patterns on Reddit
ABSTRACT: As crowd-sourced curation of news and information become the norm, it is
important to understand not only how individuals consume information through
social news Web sites, but also how they contribute to their ranking systems.
In the present work, we introduce and make available a new dataset containing
the activity logs that recorded all activity for 309 Reddit users for one year.
Using this newly collected data, we present findings that highlight the
browsing and voting behavior of the study's participants. We find that most
users do not read the article that they vote on, and that, in total, 73% of
posts were rated (ie, upvoted or downvoted) without first viewing the content.
We also show evidence of cognitive fatigue in the browsing sessions of users
that are most likely to vote.
| new_dataset | 0.958538 |
1511.06984 | Serena Yeung | Serena Yeung, Olga Russakovsky, Greg Mori, Li Fei-Fei | End-to-end Learning of Action Detection from Frame Glimpses in Videos | Update to version in CVPR 2016 proceedings | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work we introduce a fully end-to-end approach for action detection in
videos that learns to directly predict the temporal bounds of actions. Our
intuition is that the process of detecting actions is naturally one of
observation and refinement: observing moments in video, and refining hypotheses
about when an action is occurring. Based on this insight, we formulate our
model as a recurrent neural network-based agent that interacts with a video
over time. The agent observes video frames and decides both where to look next
and when to emit a prediction. Since backpropagation is not adequate in this
non-differentiable setting, we use REINFORCE to learn the agent's decision
policy. Our model achieves state-of-the-art results on the THUMOS'14 and
ActivityNet datasets while observing only a fraction (2% or less) of the video
frames.
| [
{
"version": "v1",
"created": "Sun, 22 Nov 2015 09:41:50 GMT"
},
{
"version": "v2",
"created": "Mon, 13 Mar 2017 07:33:15 GMT"
}
] | 2017-03-14T00:00:00 | [
[
"Yeung",
"Serena",
""
],
[
"Russakovsky",
"Olga",
""
],
[
"Mori",
"Greg",
""
],
[
"Fei-Fei",
"Li",
""
]
] | TITLE: End-to-end Learning of Action Detection from Frame Glimpses in Videos
ABSTRACT: In this work we introduce a fully end-to-end approach for action detection in
videos that learns to directly predict the temporal bounds of actions. Our
intuition is that the process of detecting actions is naturally one of
observation and refinement: observing moments in video, and refining hypotheses
about when an action is occurring. Based on this insight, we formulate our
model as a recurrent neural network-based agent that interacts with a video
over time. The agent observes video frames and decides both where to look next
and when to emit a prediction. Since backpropagation is not adequate in this
non-differentiable setting, we use REINFORCE to learn the agent's decision
policy. Our model achieves state-of-the-art results on the THUMOS'14 and
ActivityNet datasets while observing only a fraction (2% or less) of the video
frames.
| no_new_dataset | 0.949342 |
1606.00802 | Amirhossein Tavanaei | Amirhossein Tavanaei and Anthony S Maida | A Spiking Network that Learns to Extract Spike Signatures from Speech
Signals | Published in Neurocomputing Journal, Elsevier | Neurocomputing, 140:191-199, 2017 | 10.1016/j.neucom.2017.01.088 | null | cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Spiking neural networks (SNNs) with adaptive synapses reflect core properties
of biological neural networks. Speech recognition, as an application involving
audio coding and dynamic learning, provides a good test problem to study SNN
functionality. We present a simple, novel, and efficient nonrecurrent SNN that
learns to convert a speech signal into a spike train signature. The signature
is distinguishable from signatures for other speech signals representing
different words, thereby enabling digit recognition and discrimination in
devices that use only spiking neurons. The method uses a small, nonrecurrent
SNN consisting of Izhikevich neurons equipped with spike timing dependent
plasticity (STDP) and biologically realistic synapses. This approach introduces
an efficient and fast network without error-feedback training, although it does
require supervised training. The new simulation results produce discriminative
spike train patterns for spoken digits in which highly correlated spike trains
belong to the same category and low correlated patterns belong to different
categories. The proposed SNN is evaluated using a spoken digit recognition task
where a subset of the Aurora speech dataset is used. The experimental results
show that the network performs well in terms of accuracy rate and complexity.
| [
{
"version": "v1",
"created": "Thu, 2 Jun 2016 18:54:25 GMT"
},
{
"version": "v2",
"created": "Tue, 11 Oct 2016 19:27:43 GMT"
},
{
"version": "v3",
"created": "Sun, 12 Mar 2017 04:31:19 GMT"
}
] | 2017-03-14T00:00:00 | [
[
"Tavanaei",
"Amirhossein",
""
],
[
"Maida",
"Anthony S",
""
]
] | TITLE: A Spiking Network that Learns to Extract Spike Signatures from Speech
Signals
ABSTRACT: Spiking neural networks (SNNs) with adaptive synapses reflect core properties
of biological neural networks. Speech recognition, as an application involving
audio coding and dynamic learning, provides a good test problem to study SNN
functionality. We present a simple, novel, and efficient nonrecurrent SNN that
learns to convert a speech signal into a spike train signature. The signature
is distinguishable from signatures for other speech signals representing
different words, thereby enabling digit recognition and discrimination in
devices that use only spiking neurons. The method uses a small, nonrecurrent
SNN consisting of Izhikevich neurons equipped with spike timing dependent
plasticity (STDP) and biologically realistic synapses. This approach introduces
an efficient and fast network without error-feedback training, although it does
require supervised training. The new simulation results produce discriminative
spike train patterns for spoken digits in which highly correlated spike trains
belong to the same category and low correlated patterns belong to different
categories. The proposed SNN is evaluated using a spoken digit recognition task
where a subset of the Aurora speech dataset is used. The experimental results
show that the network performs well in terms of accuracy rate and complexity.
| no_new_dataset | 0.948251 |
1608.02097 | Su Zhu | Su Zhu, Kai Yu | Encoder-decoder with Focus-mechanism for Sequence Labelling Based Spoken
Language Understanding | 5 pages, 2 figures | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper investigates the framework of encoder-decoder with attention for
sequence labelling based spoken language understanding. We introduce
Bidirectional Long Short Term Memory - Long Short Term Memory networks
(BLSTM-LSTM) as the encoder-decoder model to fully utilize the power of deep
learning. In the sequence labelling task, the input and output sequences are
aligned word by word, while the attention mechanism cannot provide the exact
alignment. To address this limitation, we propose a novel focus mechanism for
encoder-decoder framework. Experiments on the standard ATIS dataset showed that
BLSTM-LSTM with focus mechanism defined the new state-of-the-art by
outperforming standard BLSTM and attention based encoder-decoder. Further
experiments also show that the proposed model is more robust to speech
recognition errors.
| [
{
"version": "v1",
"created": "Sat, 6 Aug 2016 11:41:05 GMT"
},
{
"version": "v2",
"created": "Mon, 13 Mar 2017 14:50:11 GMT"
}
] | 2017-03-14T00:00:00 | [
[
"Zhu",
"Su",
""
],
[
"Yu",
"Kai",
""
]
] | TITLE: Encoder-decoder with Focus-mechanism for Sequence Labelling Based Spoken
Language Understanding
ABSTRACT: This paper investigates the framework of encoder-decoder with attention for
sequence labelling based spoken language understanding. We introduce
Bidirectional Long Short Term Memory - Long Short Term Memory networks
(BLSTM-LSTM) as the encoder-decoder model to fully utilize the power of deep
learning. In the sequence labelling task, the input and output sequences are
aligned word by word, while the attention mechanism cannot provide the exact
alignment. To address this limitation, we propose a novel focus mechanism for
encoder-decoder framework. Experiments on the standard ATIS dataset showed that
BLSTM-LSTM with focus mechanism defined the new state-of-the-art by
outperforming standard BLSTM and attention based encoder-decoder. Further
experiments also show that the proposed model is more robust to speech
recognition errors.
| no_new_dataset | 0.948965 |
1610.00187 | Yuji Yoshimura | Yuji Yoshimura, Stanislav Sobolevsky, Juan N Bautista Hobin, Carlo
Ratti, Josep Blat | Urban association rules: uncovering linked trips for shopping behavior | 21 pages, 7 figures | null | null | null | physics.soc-ph cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this article, we introduce the method of urban association rules and its
uses for extracting frequently appearing combinations of stores that are
visited together to characterize shoppers' behaviors. The Apriori algorithm is
used to extract the association rules (i.e., if -> result) from customer
transaction datasets in a market-basket analysis. An application to our
large-scale and anonymized bank card transaction dataset enables us to output
linked trips for shopping all over the city: the method enables us to predict
the other shops most likely to be visited by a customer given a particular shop
that was already visited as an input. In addition, our methodology can consider
all transaction activities conducted by customers for a whole city in addition
to the location of stores dispersed in the city. This approach enables us to
uncover not only simple linked trips such as transition movements between
stores but also the edge weight for each linked trip in the specific district.
Thus, the proposed methodology can complement conventional research methods.
Enhancing understanding of people's shopping behaviors could be useful for city
authorities and urban practitioners for effective urban management. The results
also help individual retailers to rearrange their services by accommodating the
needs of their customers' habits to enhance their shopping experience.
| [
{
"version": "v1",
"created": "Sat, 1 Oct 2016 20:48:24 GMT"
}
] | 2017-03-14T00:00:00 | [
[
"Yoshimura",
"Yuji",
""
],
[
"Sobolevsky",
"Stanislav",
""
],
[
"Hobin",
"Juan N Bautista",
""
],
[
"Ratti",
"Carlo",
""
],
[
"Blat",
"Josep",
""
]
] | TITLE: Urban association rules: uncovering linked trips for shopping behavior
ABSTRACT: In this article, we introduce the method of urban association rules and its
uses for extracting frequently appearing combinations of stores that are
visited together to characterize shoppers' behaviors. The Apriori algorithm is
used to extract the association rules (i.e., if -> result) from customer
transaction datasets in a market-basket analysis. An application to our
large-scale and anonymized bank card transaction dataset enables us to output
linked trips for shopping all over the city: the method enables us to predict
the other shops most likely to be visited by a customer given a particular shop
that was already visited as an input. In addition, our methodology can consider
all transaction activities conducted by customers for a whole city in addition
to the location of stores dispersed in the city. This approach enables us to
uncover not only simple linked trips such as transition movements between
stores but also the edge weight for each linked trip in the specific district.
Thus, the proposed methodology can complement conventional research methods.
Enhancing understanding of people's shopping behaviors could be useful for city
authorities and urban practitioners for effective urban management. The results
also help individual retailers to rearrange their services by accommodating the
needs of their customers' habits to enhance their shopping experience.
| no_new_dataset | 0.941115 |
1611.04246 | Quanshi Zhang | Quanshi Zhang, Ruiming Cao, Ying Nian Wu, and Song-Chun Zhu | Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning | in the Thirty-First AAAI Conference on Artificial Intelligence
(AAAI-17) | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes a learning strategy that extracts object-part concepts
from a pre-trained convolutional neural network (CNN), in an attempt to 1)
explore explicit semantics hidden in CNN units and 2) gradually grow a
semantically interpretable graphical model on the pre-trained CNN for
hierarchical object understanding. Given part annotations on very few (e.g.,
3-12) objects, our method mines certain latent patterns from the pre-trained
CNN and associates them with different semantic parts. We use a four-layer
And-Or graph to organize the mined latent patterns, so as to clarify their
internal semantic hierarchy. Our method is guided by a small number of part
annotations, and it achieves superior performance (about 13%-107% improvement)
in part center prediction on the PASCAL VOC and ImageNet datasets.
| [
{
"version": "v1",
"created": "Mon, 14 Nov 2016 04:13:37 GMT"
},
{
"version": "v2",
"created": "Mon, 13 Mar 2017 07:23:20 GMT"
}
] | 2017-03-14T00:00:00 | [
[
"Zhang",
"Quanshi",
""
],
[
"Cao",
"Ruiming",
""
],
[
"Wu",
"Ying Nian",
""
],
[
"Zhu",
"Song-Chun",
""
]
] | TITLE: Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning
ABSTRACT: This paper proposes a learning strategy that extracts object-part concepts
from a pre-trained convolutional neural network (CNN), in an attempt to 1)
explore explicit semantics hidden in CNN units and 2) gradually grow a
semantically interpretable graphical model on the pre-trained CNN for
hierarchical object understanding. Given part annotations on very few (e.g.,
3-12) objects, our method mines certain latent patterns from the pre-trained
CNN and associates them with different semantic parts. We use a four-layer
And-Or graph to organize the mined latent patterns, so as to clarify their
internal semantic hierarchy. Our method is guided by a small number of part
annotations, and it achieves superior performance (about 13%-107% improvement)
in part center prediction on the PASCAL VOC and ImageNet datasets.
| no_new_dataset | 0.954478 |
1612.01082 | Junjie Zhang | Junjie Zhang, Qi Wu, Chunhua Shen, Jian Zhang, Jianfeng Lu | Multi-Label Image Classification with Regional Latent Semantic
Dependencies | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep convolution neural networks (CNN) have demonstrated advanced performance
on single-label image classification, and various progress also have been made
to apply CNN methods on multi-label image classification, which requires to
annotate objects, attributes, scene categories etc. in a single shot. Recent
state-of-the-art approaches to multi-label image classification exploit the
label dependencies in an image, at global level, largely improving the labeling
capacity. However, predicting small objects and visual concepts is still
challenging due to the limited discrimination of the global visual features. In
this paper, we propose a Regional Latent Semantic Dependencies model (RLSD) to
address this problem. The utilized model includes a fully convolutional
localization architecture to localize the regions that may contain multiple
highly-dependent labels. The localized regions are further sent to the
recurrent neural networks (RNN) to characterize the latent semantic
dependencies at the regional level. Experimental results on several benchmark
datasets show that our proposed model achieves the best performance compared to
the state-of-the-art models, especially for predicting small objects occurred
in the images. In addition, we set up an upper bound model (RLSD+ft-RPN) using
bounding box coordinates during training, the experimental results also show
that our RLSD can approach the upper bound without using the bounding-box
annotations, which is more realistic in the real world.
| [
{
"version": "v1",
"created": "Sun, 4 Dec 2016 07:25:25 GMT"
},
{
"version": "v2",
"created": "Wed, 4 Jan 2017 04:44:29 GMT"
},
{
"version": "v3",
"created": "Sun, 12 Mar 2017 23:41:23 GMT"
}
] | 2017-03-14T00:00:00 | [
[
"Zhang",
"Junjie",
""
],
[
"Wu",
"Qi",
""
],
[
"Shen",
"Chunhua",
""
],
[
"Zhang",
"Jian",
""
],
[
"Lu",
"Jianfeng",
""
]
] | TITLE: Multi-Label Image Classification with Regional Latent Semantic
Dependencies
ABSTRACT: Deep convolution neural networks (CNN) have demonstrated advanced performance
on single-label image classification, and various progress also have been made
to apply CNN methods on multi-label image classification, which requires to
annotate objects, attributes, scene categories etc. in a single shot. Recent
state-of-the-art approaches to multi-label image classification exploit the
label dependencies in an image, at global level, largely improving the labeling
capacity. However, predicting small objects and visual concepts is still
challenging due to the limited discrimination of the global visual features. In
this paper, we propose a Regional Latent Semantic Dependencies model (RLSD) to
address this problem. The utilized model includes a fully convolutional
localization architecture to localize the regions that may contain multiple
highly-dependent labels. The localized regions are further sent to the
recurrent neural networks (RNN) to characterize the latent semantic
dependencies at the regional level. Experimental results on several benchmark
datasets show that our proposed model achieves the best performance compared to
the state-of-the-art models, especially for predicting small objects occurred
in the images. In addition, we set up an upper bound model (RLSD+ft-RPN) using
bounding box coordinates during training, the experimental results also show
that our RLSD can approach the upper bound without using the bounding-box
annotations, which is more realistic in the real world.
| no_new_dataset | 0.95096 |
1701.05228 | Konstantina Christakopoulou | Konstantina Christakopoulou, Jaya Kawale, Arindam Banerjee | Recommendation under Capacity Constraints | Extended methods section and experimental section to include bayesian
personalized ranking objective as well | null | null | null | stat.ML cs.IR cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we investigate the common scenario where every candidate item
for recommendation is characterized by a maximum capacity, i.e., number of
seats in a Point-of-Interest (POI) or size of an item's inventory. Despite the
prevalence of the task of recommending items under capacity constraints in a
variety of settings, to the best of our knowledge, none of the known
recommender methods is designed to respect capacity constraints. To close this
gap, we extend three state-of-the art latent factor recommendation approaches:
probabilistic matrix factorization (PMF), geographical matrix factorization
(GeoMF), and bayesian personalized ranking (BPR), to optimize for both
recommendation accuracy and expected item usage that respects the capacity
constraints. We introduce the useful concepts of user propensity to listen and
item capacity. Our experimental results in real-world datasets, both for the
domain of item recommendation and POI recommendation, highlight the benefit of
our method for the setting of recommendation under capacity constraints.
| [
{
"version": "v1",
"created": "Wed, 18 Jan 2017 20:45:57 GMT"
},
{
"version": "v2",
"created": "Sun, 12 Mar 2017 23:33:18 GMT"
}
] | 2017-03-14T00:00:00 | [
[
"Christakopoulou",
"Konstantina",
""
],
[
"Kawale",
"Jaya",
""
],
[
"Banerjee",
"Arindam",
""
]
] | TITLE: Recommendation under Capacity Constraints
ABSTRACT: In this paper, we investigate the common scenario where every candidate item
for recommendation is characterized by a maximum capacity, i.e., number of
seats in a Point-of-Interest (POI) or size of an item's inventory. Despite the
prevalence of the task of recommending items under capacity constraints in a
variety of settings, to the best of our knowledge, none of the known
recommender methods is designed to respect capacity constraints. To close this
gap, we extend three state-of-the art latent factor recommendation approaches:
probabilistic matrix factorization (PMF), geographical matrix factorization
(GeoMF), and bayesian personalized ranking (BPR), to optimize for both
recommendation accuracy and expected item usage that respects the capacity
constraints. We introduce the useful concepts of user propensity to listen and
item capacity. Our experimental results in real-world datasets, both for the
domain of item recommendation and POI recommendation, highlight the benefit of
our method for the setting of recommendation under capacity constraints.
| no_new_dataset | 0.950041 |
1702.00546 | Yuji Yoshimura | Yuji Yoshimura, Alexander Amini, Stanislav Sobolevsky, Josep Blat,
Carlo Ratti | Analysis of pedestrian behaviors through non-invasive Bluetooth
monitoring | 16 pages, 7 figures | Applied Geography 81, 43-51, 2017 | null | null | physics.soc-ph cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper analyzes pedestrians' behavioral patterns in the pedestrianized
shopping environment in the historical center of Barcelona, Spain. We employ a
Bluetooth detection technique to capture a large-scale dataset of pedestrians'
behavior over a one-month period, including during a key sales period. We
focused on comparing particular behaviors before, during, and after the
discount sales by analyzing this large-scale dataset, which is different but
complementary to the conventionally used small-scale samples. Our results
uncover pedestrians actively exploring a wider area of the district during a
discount period compared to weekdays, giving rise to strong underlying mobility
patterns.
| [
{
"version": "v1",
"created": "Thu, 2 Feb 2017 06:12:23 GMT"
}
] | 2017-03-14T00:00:00 | [
[
"Yoshimura",
"Yuji",
""
],
[
"Amini",
"Alexander",
""
],
[
"Sobolevsky",
"Stanislav",
""
],
[
"Blat",
"Josep",
""
],
[
"Ratti",
"Carlo",
""
]
] | TITLE: Analysis of pedestrian behaviors through non-invasive Bluetooth
monitoring
ABSTRACT: This paper analyzes pedestrians' behavioral patterns in the pedestrianized
shopping environment in the historical center of Barcelona, Spain. We employ a
Bluetooth detection technique to capture a large-scale dataset of pedestrians'
behavior over a one-month period, including during a key sales period. We
focused on comparing particular behaviors before, during, and after the
discount sales by analyzing this large-scale dataset, which is different but
complementary to the conventionally used small-scale samples. Our results
uncover pedestrians actively exploring a wider area of the district during a
discount period compared to weekdays, giving rise to strong underlying mobility
patterns.
| no_new_dataset | 0.936227 |
1702.04457 | Jingbo Shang | Jingbo Shang, Jialu Liu, Meng Jiang, Xiang Ren, Clare R Voss, Jiawei
Han | Automated Phrase Mining from Massive Text Corpora | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As one of the fundamental tasks in text analysis, phrase mining aims at
extracting quality phrases from a text corpus. Phrase mining is important in
various tasks such as information extraction/retrieval, taxonomy construction,
and topic modeling. Most existing methods rely on complex, trained linguistic
analyzers, and thus likely have unsatisfactory performance on text corpora of
new domains and genres without extra but expensive adaption. Recently, a few
data-driven methods have been developed successfully for extraction of phrases
from massive domain-specific text. However, none of the state-of-the-art models
is fully automated because they require human experts for designing rules or
labeling phrases.
Since one can easily obtain many quality phrases from public knowledge bases
to a scale that is much larger than that produced by human experts, in this
paper, we propose a novel framework for automated phrase mining, AutoPhrase,
which leverages this large amount of high-quality phrases in an effective way
and achieves better performance compared to limited human labeled phrases. In
addition, we develop a POS-guided phrasal segmentation model, which
incorporates the shallow syntactic information in part-of-speech (POS) tags to
further enhance the performance, when a POS tagger is available. Note that,
AutoPhrase can support any language as long as a general knowledge base (e.g.,
Wikipedia) in that language is available, while benefiting from, but not
requiring, a POS tagger. Compared to the state-of-the-art methods, the new
method has shown significant improvements in effectiveness on five real-world
datasets across different domains and languages.
| [
{
"version": "v1",
"created": "Wed, 15 Feb 2017 03:35:03 GMT"
},
{
"version": "v2",
"created": "Sat, 11 Mar 2017 19:33:41 GMT"
}
] | 2017-03-14T00:00:00 | [
[
"Shang",
"Jingbo",
""
],
[
"Liu",
"Jialu",
""
],
[
"Jiang",
"Meng",
""
],
[
"Ren",
"Xiang",
""
],
[
"Voss",
"Clare R",
""
],
[
"Han",
"Jiawei",
""
]
] | TITLE: Automated Phrase Mining from Massive Text Corpora
ABSTRACT: As one of the fundamental tasks in text analysis, phrase mining aims at
extracting quality phrases from a text corpus. Phrase mining is important in
various tasks such as information extraction/retrieval, taxonomy construction,
and topic modeling. Most existing methods rely on complex, trained linguistic
analyzers, and thus likely have unsatisfactory performance on text corpora of
new domains and genres without extra but expensive adaption. Recently, a few
data-driven methods have been developed successfully for extraction of phrases
from massive domain-specific text. However, none of the state-of-the-art models
is fully automated because they require human experts for designing rules or
labeling phrases.
Since one can easily obtain many quality phrases from public knowledge bases
to a scale that is much larger than that produced by human experts, in this
paper, we propose a novel framework for automated phrase mining, AutoPhrase,
which leverages this large amount of high-quality phrases in an effective way
and achieves better performance compared to limited human labeled phrases. In
addition, we develop a POS-guided phrasal segmentation model, which
incorporates the shallow syntactic information in part-of-speech (POS) tags to
further enhance the performance, when a POS tagger is available. Note that,
AutoPhrase can support any language as long as a general knowledge base (e.g.,
Wikipedia) in that language is available, while benefiting from, but not
requiring, a POS tagger. Compared to the state-of-the-art methods, the new
method has shown significant improvements in effectiveness on five real-world
datasets across different domains and languages.
| no_new_dataset | 0.949482 |
1703.00391 | Ilias Tachmazidis | Ilias Tachmazidis, Sotiris Batsakis, John Davies, Alistair Duke, Mauro
Vallati, Grigoris Antoniou, Sandra Stincic Clarke | A Hypercat-enabled Semantic Internet of Things Data Hub: Technical
Report | Technical report of an accepted ESWC-2017 paper | null | null | null | cs.AI cs.DB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An increasing amount of information is generated from the rapidly increasing
number of sensor networks and smart devices. A wide variety of sources generate
and publish information in different formats, thus highlighting
interoperability as one of the key prerequisites for the success of Internet of
Things (IoT). The BT Hypercat Data Hub provides a focal point for the sharing
and consumption of available datasets from a wide range of sources. In this
work, we propose a semantic enrichment of the BT Hypercat Data Hub, using
well-accepted Semantic Web standards and tools. We propose an ontology that
captures the semantics of the imported data and present the BT SPARQL Endpoint
by means of a mapping between SPARQL and SQL queries. Furthermore, federated
SPARQL queries allow queries over multiple hub-based and external data sources.
Finally, we provide two use cases in order to illustrate the advantages
afforded by our semantic approach.
| [
{
"version": "v1",
"created": "Wed, 1 Mar 2017 17:10:27 GMT"
},
{
"version": "v2",
"created": "Sun, 12 Mar 2017 13:18:29 GMT"
}
] | 2017-03-14T00:00:00 | [
[
"Tachmazidis",
"Ilias",
""
],
[
"Batsakis",
"Sotiris",
""
],
[
"Davies",
"John",
""
],
[
"Duke",
"Alistair",
""
],
[
"Vallati",
"Mauro",
""
],
[
"Antoniou",
"Grigoris",
""
],
[
"Clarke",
"Sandra Stincic",
""
]
] | TITLE: A Hypercat-enabled Semantic Internet of Things Data Hub: Technical
Report
ABSTRACT: An increasing amount of information is generated from the rapidly increasing
number of sensor networks and smart devices. A wide variety of sources generate
and publish information in different formats, thus highlighting
interoperability as one of the key prerequisites for the success of Internet of
Things (IoT). The BT Hypercat Data Hub provides a focal point for the sharing
and consumption of available datasets from a wide range of sources. In this
work, we propose a semantic enrichment of the BT Hypercat Data Hub, using
well-accepted Semantic Web standards and tools. We propose an ontology that
captures the semantics of the imported data and present the BT SPARQL Endpoint
by means of a mapping between SPARQL and SQL queries. Furthermore, federated
SPARQL queries allow queries over multiple hub-based and external data sources.
Finally, we provide two use cases in order to illustrate the advantages
afforded by our semantic approach.
| no_new_dataset | 0.951818 |
1703.03895 | Roberto Souza | Pedro Calais Guerra, Roberto C.S.N.P. Souza, Renato M. Assun\c{c}\~ao,
Wagner Meira Jr | Antagonism also Flows through Retweets: The Impact of Out-of-Context
Quotes in Opinion Polarization Analysis | This is an extended version of the short paper published at ICWSM
2017 | null | null | null | cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we study the implications of the commonplace assumption that
most social media studies make with respect to the nature of message shares
(such as retweets) as a predominantly positive interaction. By analyzing two
large longitudinal Brazilian Twitter datasets containing 5 years of
conversations on two polarizing topics - Politics and Sports - we empirically
demonstrate that groups holding antagonistic views can actually retweet each
other more often than they retweet other groups. We show that assuming retweets
as endorsement interactions can lead to misleading conclusions with respect to
the level of antagonism among social communities, and that this apparent
paradox is explained in part by the use of retweets to quote the original
content creator out of the message's original temporal context, for humor and
criticism purposes. As a consequence, messages diffused on online media can
have their polarity reversed over time, what poses challenges for social and
computer scientists aiming to classify and track opinion groups on online
media. On the other hand, we found that the time users take to retweet a
message after it has been originally posted can be a useful signal to infer
antagonism in social platforms, and that surges of out-of-context retweets
correlate with sentiment drifts triggered by real-world events. We also discuss
how such evidences can be embedded in sentiment analysis models.
| [
{
"version": "v1",
"created": "Sat, 11 Mar 2017 02:16:41 GMT"
}
] | 2017-03-14T00:00:00 | [
[
"Guerra",
"Pedro Calais",
""
],
[
"Souza",
"Roberto C. S. N. P.",
""
],
[
"Assunção",
"Renato M.",
""
],
[
"Meira",
"Wagner",
"Jr"
]
] | TITLE: Antagonism also Flows through Retweets: The Impact of Out-of-Context
Quotes in Opinion Polarization Analysis
ABSTRACT: In this paper, we study the implications of the commonplace assumption that
most social media studies make with respect to the nature of message shares
(such as retweets) as a predominantly positive interaction. By analyzing two
large longitudinal Brazilian Twitter datasets containing 5 years of
conversations on two polarizing topics - Politics and Sports - we empirically
demonstrate that groups holding antagonistic views can actually retweet each
other more often than they retweet other groups. We show that assuming retweets
as endorsement interactions can lead to misleading conclusions with respect to
the level of antagonism among social communities, and that this apparent
paradox is explained in part by the use of retweets to quote the original
content creator out of the message's original temporal context, for humor and
criticism purposes. As a consequence, messages diffused on online media can
have their polarity reversed over time, what poses challenges for social and
computer scientists aiming to classify and track opinion groups on online
media. On the other hand, we found that the time users take to retweet a
message after it has been originally posted can be a useful signal to infer
antagonism in social platforms, and that surges of out-of-context retweets
correlate with sentiment drifts triggered by real-world events. We also discuss
how such evidences can be embedded in sentiment analysis models.
| no_new_dataset | 0.929312 |
1703.03897 | Le An | Le An, Ons Mlouki, Foutse Khomh, Giuliano Antoniol | Stack Overflow: A Code Laundering Platform? | In proceedings of the 24th IEEE International Conference on Software
Analysis, Evolution, and Reengineering (SANER) | null | null | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Developers use Question and Answer (Q&A) websites to exchange knowledge and
expertise. Stack Overflow is a popular Q&A website where developers discuss
coding problems and share code examples. Although all Stack Overflow posts are
free to access, code examples on Stack Overflow are governed by the Creative
Commons Attribute-ShareAlike 3.0 Unported license that developers should obey
when reusing code from Stack Overflow or posting code to Stack Overflow. In
this paper, we conduct a case study with 399 Android apps, to investigate
whether developers respect license terms when reusing code from Stack Overflow
posts (and the other way around). We found 232 code snippets in 62 Android apps
from our dataset that were potentially reused from Stack Overflow, and 1,226
Stack Overflow posts containing code examples that are clones of code released
in 68 Android apps, suggesting that developers may have copied the code of
these apps to answer Stack Overflow questions. We investigated the licenses of
these pieces of code and observed 1,279 cases of potential license violations
(related to code posting to Stack overflow or code reuse from Stack overflow).
This paper aims to raise the awareness of the software engineering community
about potential unethical code reuse activities taking place on Q&A websites
like Stack Overflow.
| [
{
"version": "v1",
"created": "Sat, 11 Mar 2017 02:41:31 GMT"
}
] | 2017-03-14T00:00:00 | [
[
"An",
"Le",
""
],
[
"Mlouki",
"Ons",
""
],
[
"Khomh",
"Foutse",
""
],
[
"Antoniol",
"Giuliano",
""
]
] | TITLE: Stack Overflow: A Code Laundering Platform?
ABSTRACT: Developers use Question and Answer (Q&A) websites to exchange knowledge and
expertise. Stack Overflow is a popular Q&A website where developers discuss
coding problems and share code examples. Although all Stack Overflow posts are
free to access, code examples on Stack Overflow are governed by the Creative
Commons Attribute-ShareAlike 3.0 Unported license that developers should obey
when reusing code from Stack Overflow or posting code to Stack Overflow. In
this paper, we conduct a case study with 399 Android apps, to investigate
whether developers respect license terms when reusing code from Stack Overflow
posts (and the other way around). We found 232 code snippets in 62 Android apps
from our dataset that were potentially reused from Stack Overflow, and 1,226
Stack Overflow posts containing code examples that are clones of code released
in 68 Android apps, suggesting that developers may have copied the code of
these apps to answer Stack Overflow questions. We investigated the licenses of
these pieces of code and observed 1,279 cases of potential license violations
(related to code posting to Stack overflow or code reuse from Stack overflow).
This paper aims to raise the awareness of the software engineering community
about potential unethical code reuse activities taking place on Q&A websites
like Stack Overflow.
| new_dataset | 0.631197 |
1703.03939 | Govardana Sachithanandam Ramachandran | Govardana Sachithanandam Ramachandran, Ajay Sohmshetty | Ask Me Even More: Dynamic Memory Tensor Networks (Extended Model) | null | null | null | null | cs.CL cs.LG cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We examine Memory Networks for the task of question answering (QA), under
common real world scenario where training examples are scarce and under weakly
supervised scenario, that is only extrinsic labels are available for training.
We propose extensions for the Dynamic Memory Network (DMN), specifically within
the attention mechanism, we call the resulting Neural Architecture as Dynamic
Memory Tensor Network (DMTN). Ultimately, we see that our proposed extensions
results in over 80% improvement in the number of task passed against the
baselined standard DMN and 20% more task passed compared to state-of-the-art
End-to-End Memory Network for Facebook's single task weakly trained 1K bAbi
dataset.
| [
{
"version": "v1",
"created": "Sat, 11 Mar 2017 10:05:19 GMT"
}
] | 2017-03-14T00:00:00 | [
[
"Ramachandran",
"Govardana Sachithanandam",
""
],
[
"Sohmshetty",
"Ajay",
""
]
] | TITLE: Ask Me Even More: Dynamic Memory Tensor Networks (Extended Model)
ABSTRACT: We examine Memory Networks for the task of question answering (QA), under
common real world scenario where training examples are scarce and under weakly
supervised scenario, that is only extrinsic labels are available for training.
We propose extensions for the Dynamic Memory Network (DMN), specifically within
the attention mechanism, we call the resulting Neural Architecture as Dynamic
Memory Tensor Network (DMTN). Ultimately, we see that our proposed extensions
results in over 80% improvement in the number of task passed against the
baselined standard DMN and 20% more task passed compared to state-of-the-art
End-to-End Memory Network for Facebook's single task weakly trained 1K bAbi
dataset.
| no_new_dataset | 0.954095 |
1703.03957 | Shenglan Liu | Shenglan Liu, Jun Wu, Lin Feng, Feilong Wang | Neural method for Explicit Mapping of Quasi-curvature Locally Linear
Embedding in image retrieval | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposed a new explicit nonlinear dimensionality reduction using
neural networks for image retrieval tasks. We first proposed a Quasi-curvature
Locally Linear Embedding (QLLE) for training set. QLLE guarantees the linear
criterion in neighborhood of each sample. Then, a neural method (NM) is
proposed for out-of-sample problem. Combining QLLE and NM, we provide a
explicit nonlinear dimensionality reduction approach for efficient image
retrieval. The experimental results in three benchmark datasets illustrate that
our method can get better performance than other state-of-the-art out-of-sample
methods.
| [
{
"version": "v1",
"created": "Sat, 11 Mar 2017 11:29:01 GMT"
}
] | 2017-03-14T00:00:00 | [
[
"Liu",
"Shenglan",
""
],
[
"Wu",
"Jun",
""
],
[
"Feng",
"Lin",
""
],
[
"Wang",
"Feilong",
""
]
] | TITLE: Neural method for Explicit Mapping of Quasi-curvature Locally Linear
Embedding in image retrieval
ABSTRACT: This paper proposed a new explicit nonlinear dimensionality reduction using
neural networks for image retrieval tasks. We first proposed a Quasi-curvature
Locally Linear Embedding (QLLE) for training set. QLLE guarantees the linear
criterion in neighborhood of each sample. Then, a neural method (NM) is
proposed for out-of-sample problem. Combining QLLE and NM, we provide a
explicit nonlinear dimensionality reduction approach for efficient image
retrieval. The experimental results in three benchmark datasets illustrate that
our method can get better performance than other state-of-the-art out-of-sample
methods.
| no_new_dataset | 0.94887 |
1703.04135 | Ji Li | Ji Li, Zihao Yuan, Zhe Li, Caiwen Ding, Ao Ren, Qinru Qiu, Jeffrey
Draper, Yanzhi Wang | Hardware-Driven Nonlinear Activation for Stochastic Computing Based Deep
Convolutional Neural Networks | This paper is accepted by 2017 International Joint Conference on
Neural Networks (IJCNN) | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, Deep Convolutional Neural Networks (DCNNs) have made unprecedented
progress, achieving the accuracy close to, or even better than human-level
perception in various tasks. There is a timely need to map the latest software
DCNNs to application-specific hardware, in order to achieve orders of magnitude
improvement in performance, energy efficiency and compactness. Stochastic
Computing (SC), as a low-cost alternative to the conventional binary computing
paradigm, has the potential to enable massively parallel and highly scalable
hardware implementation of DCNNs. One major challenge in SC based DCNNs is
designing accurate nonlinear activation functions, which have a significant
impact on the network-level accuracy but cannot be implemented accurately by
existing SC computing blocks. In this paper, we design and optimize SC based
neurons, and we propose highly accurate activation designs for the three most
frequently used activation functions in software DCNNs, i.e, hyperbolic
tangent, logistic, and rectified linear units. Experimental results on LeNet-5
using MNIST dataset demonstrate that compared with a binary ASIC hardware DCNN,
the DCNN with the proposed SC neurons can achieve up to 61X, 151X, and 2X
improvement in terms of area, power, and energy, respectively, at the cost of
small precision degradation.In addition, the SC approach achieves up to 21X and
41X of the area, 41X and 72X of the power, and 198200X and 96443X of the
energy, compared with CPU and GPU approaches, respectively, while the error is
increased by less than 3.07%. ReLU activation is suggested for future SC based
DCNNs considering its superior performance under a small bit stream length.
| [
{
"version": "v1",
"created": "Sun, 12 Mar 2017 15:27:23 GMT"
}
] | 2017-03-14T00:00:00 | [
[
"Li",
"Ji",
""
],
[
"Yuan",
"Zihao",
""
],
[
"Li",
"Zhe",
""
],
[
"Ding",
"Caiwen",
""
],
[
"Ren",
"Ao",
""
],
[
"Qiu",
"Qinru",
""
],
[
"Draper",
"Jeffrey",
""
],
[
"Wang",
"Yanzhi",
""
]
] | TITLE: Hardware-Driven Nonlinear Activation for Stochastic Computing Based Deep
Convolutional Neural Networks
ABSTRACT: Recently, Deep Convolutional Neural Networks (DCNNs) have made unprecedented
progress, achieving the accuracy close to, or even better than human-level
perception in various tasks. There is a timely need to map the latest software
DCNNs to application-specific hardware, in order to achieve orders of magnitude
improvement in performance, energy efficiency and compactness. Stochastic
Computing (SC), as a low-cost alternative to the conventional binary computing
paradigm, has the potential to enable massively parallel and highly scalable
hardware implementation of DCNNs. One major challenge in SC based DCNNs is
designing accurate nonlinear activation functions, which have a significant
impact on the network-level accuracy but cannot be implemented accurately by
existing SC computing blocks. In this paper, we design and optimize SC based
neurons, and we propose highly accurate activation designs for the three most
frequently used activation functions in software DCNNs, i.e, hyperbolic
tangent, logistic, and rectified linear units. Experimental results on LeNet-5
using MNIST dataset demonstrate that compared with a binary ASIC hardware DCNN,
the DCNN with the proposed SC neurons can achieve up to 61X, 151X, and 2X
improvement in terms of area, power, and energy, respectively, at the cost of
small precision degradation.In addition, the SC approach achieves up to 21X and
41X of the area, 41X and 72X of the power, and 198200X and 96443X of the
energy, compared with CPU and GPU approaches, respectively, while the error is
increased by less than 3.07%. ReLU activation is suggested for future SC based
DCNNs considering its superior performance under a small bit stream length.
| no_new_dataset | 0.95297 |
1703.04219 | Ioakeim Perros | Ioakeim Perros and Evangelos E. Papalexakis and Fei Wang and Richard
Vuduc and Elizabeth Searles and Michael Thompson and Jimeng Sun | SPARTan: Scalable PARAFAC2 for Large & Sparse Data | null | null | null | null | cs.LG cs.NA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In exploratory tensor mining, a common problem is how to analyze a set of
variables across a set of subjects whose observations do not align naturally.
For example, when modeling medical features across a set of patients, the
number and duration of treatments may vary widely in time, meaning there is no
meaningful way to align their clinical records across time points for analysis
purposes. To handle such data, the state-of-the-art tensor model is the
so-called PARAFAC2, which yields interpretable and robust output and can
naturally handle sparse data. However, its main limitation up to now has been
the lack of efficient algorithms that can handle large-scale datasets.
In this work, we fill this gap by developing a scalable method to compute the
PARAFAC2 decomposition of large and sparse datasets, called SPARTan. Our method
exploits special structure within PARAFAC2, leading to a novel algorithmic
reformulation that is both fast (in absolute time) and more memory-efficient
than prior work. We evaluate SPARTan on both synthetic and real datasets,
showing 22X performance gains over the best previous implementation and also
handling larger problem instances for which the baseline fails. Furthermore, we
are able to apply SPARTan to the mining of temporally-evolving phenotypes on
data taken from real and medically complex pediatric patients. The clinical
meaningfulness of the phenotypes identified in this process, as well as their
temporal evolution over time for several patients, have been endorsed by
clinical experts.
| [
{
"version": "v1",
"created": "Mon, 13 Mar 2017 01:38:56 GMT"
}
] | 2017-03-14T00:00:00 | [
[
"Perros",
"Ioakeim",
""
],
[
"Papalexakis",
"Evangelos E.",
""
],
[
"Wang",
"Fei",
""
],
[
"Vuduc",
"Richard",
""
],
[
"Searles",
"Elizabeth",
""
],
[
"Thompson",
"Michael",
""
],
[
"Sun",
"Jimeng",
""
]
] | TITLE: SPARTan: Scalable PARAFAC2 for Large & Sparse Data
ABSTRACT: In exploratory tensor mining, a common problem is how to analyze a set of
variables across a set of subjects whose observations do not align naturally.
For example, when modeling medical features across a set of patients, the
number and duration of treatments may vary widely in time, meaning there is no
meaningful way to align their clinical records across time points for analysis
purposes. To handle such data, the state-of-the-art tensor model is the
so-called PARAFAC2, which yields interpretable and robust output and can
naturally handle sparse data. However, its main limitation up to now has been
the lack of efficient algorithms that can handle large-scale datasets.
In this work, we fill this gap by developing a scalable method to compute the
PARAFAC2 decomposition of large and sparse datasets, called SPARTan. Our method
exploits special structure within PARAFAC2, leading to a novel algorithmic
reformulation that is both fast (in absolute time) and more memory-efficient
than prior work. We evaluate SPARTan on both synthetic and real datasets,
showing 22X performance gains over the best previous implementation and also
handling larger problem instances for which the baseline fails. Furthermore, we
are able to apply SPARTan to the mining of temporally-evolving phenotypes on
data taken from real and medically complex pediatric patients. The clinical
meaningfulness of the phenotypes identified in this process, as well as their
temporal evolution over time for several patients, have been endorsed by
clinical experts.
| no_new_dataset | 0.943504 |
1703.04309 | Alex Kendall | Alex Kendall, Hayk Martirosyan, Saumitro Dasgupta, Peter Henry, Ryan
Kennedy, Abraham Bachrach, Adam Bry | End-to-End Learning of Geometry and Context for Deep Stereo Regression | null | null | null | null | cs.CV cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a novel deep learning architecture for regressing disparity from a
rectified pair of stereo images. We leverage knowledge of the problem's
geometry to form a cost volume using deep feature representations. We learn to
incorporate contextual information using 3-D convolutions over this volume.
Disparity values are regressed from the cost volume using a proposed
differentiable soft argmin operation, which allows us to train our method
end-to-end to sub-pixel accuracy without any additional post-processing or
regularization. We evaluate our method on the Scene Flow and KITTI datasets and
on KITTI we set a new state-of-the-art benchmark, while being significantly
faster than competing approaches.
| [
{
"version": "v1",
"created": "Mon, 13 Mar 2017 10:00:52 GMT"
}
] | 2017-03-14T00:00:00 | [
[
"Kendall",
"Alex",
""
],
[
"Martirosyan",
"Hayk",
""
],
[
"Dasgupta",
"Saumitro",
""
],
[
"Henry",
"Peter",
""
],
[
"Kennedy",
"Ryan",
""
],
[
"Bachrach",
"Abraham",
""
],
[
"Bry",
"Adam",
""
]
] | TITLE: End-to-End Learning of Geometry and Context for Deep Stereo Regression
ABSTRACT: We propose a novel deep learning architecture for regressing disparity from a
rectified pair of stereo images. We leverage knowledge of the problem's
geometry to form a cost volume using deep feature representations. We learn to
incorporate contextual information using 3-D convolutions over this volume.
Disparity values are regressed from the cost volume using a proposed
differentiable soft argmin operation, which allows us to train our method
end-to-end to sub-pixel accuracy without any additional post-processing or
regularization. We evaluate our method on the Scene Flow and KITTI datasets and
on KITTI we set a new state-of-the-art benchmark, while being significantly
faster than competing approaches.
| no_new_dataset | 0.945096 |
1703.04318 | Hossein Hosseini | Hossein Hosseini, Yize Chen, Sreeram Kannan, Baosen Zhang and Radha
Poovendran | Blocking Transferability of Adversarial Examples in Black-Box Learning
Systems | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Advances in Machine Learning (ML) have led to its adoption as an integral
component in many applications, including banking, medical diagnosis, and
driverless cars. To further broaden the use of ML models, cloud-based services
offered by Microsoft, Amazon, Google, and others have developed ML-as-a-service
tools as black-box systems. However, ML classifiers are vulnerable to
adversarial examples: inputs that are maliciously modified can cause the
classifier to provide adversary-desired outputs. Moreover, it is known that
adversarial examples generated on one classifier are likely to cause another
classifier to make the same mistake, even if the classifiers have different
architectures or are trained on disjoint datasets. This property, which is
known as transferability, opens up the possibility of attacking black-box
systems by generating adversarial examples on a substitute classifier and
transferring the examples to the target classifier. Therefore, the key to
protect black-box learning systems against the adversarial examples is to block
their transferability. To this end, we propose a training method that, as the
input is more perturbed, the classifier smoothly outputs lower confidence on
the original label and instead predicts that the input is "invalid". In
essence, we augment the output class set with a NULL label and train the
classifier to reject the adversarial examples by classifying them as NULL. In
experiments, we apply a wide range of attacks based on adversarial examples on
the black-box systems. We show that a classifier trained with the proposed
method effectively resists against the adversarial examples, while maintaining
the accuracy on clean data.
| [
{
"version": "v1",
"created": "Mon, 13 Mar 2017 10:28:24 GMT"
}
] | 2017-03-14T00:00:00 | [
[
"Hosseini",
"Hossein",
""
],
[
"Chen",
"Yize",
""
],
[
"Kannan",
"Sreeram",
""
],
[
"Zhang",
"Baosen",
""
],
[
"Poovendran",
"Radha",
""
]
] | TITLE: Blocking Transferability of Adversarial Examples in Black-Box Learning
Systems
ABSTRACT: Advances in Machine Learning (ML) have led to its adoption as an integral
component in many applications, including banking, medical diagnosis, and
driverless cars. To further broaden the use of ML models, cloud-based services
offered by Microsoft, Amazon, Google, and others have developed ML-as-a-service
tools as black-box systems. However, ML classifiers are vulnerable to
adversarial examples: inputs that are maliciously modified can cause the
classifier to provide adversary-desired outputs. Moreover, it is known that
adversarial examples generated on one classifier are likely to cause another
classifier to make the same mistake, even if the classifiers have different
architectures or are trained on disjoint datasets. This property, which is
known as transferability, opens up the possibility of attacking black-box
systems by generating adversarial examples on a substitute classifier and
transferring the examples to the target classifier. Therefore, the key to
protect black-box learning systems against the adversarial examples is to block
their transferability. To this end, we propose a training method that, as the
input is more perturbed, the classifier smoothly outputs lower confidence on
the original label and instead predicts that the input is "invalid". In
essence, we augment the output class set with a NULL label and train the
classifier to reject the adversarial examples by classifying them as NULL. In
experiments, we apply a wide range of attacks based on adversarial examples on
the black-box systems. We show that a classifier trained with the proposed
method effectively resists against the adversarial examples, while maintaining
the accuracy on clean data.
| no_new_dataset | 0.942981 |
1703.04347 | Anjany Kumar Sekuboyina | Anjany Sekuboyina, Alexander Valentinitsch, Jan S. Kirschke, and
Bjoern H. Menze | A Localisation-Segmentation Approach for Multi-label Annotation of
Lumbar Vertebrae using Deep Nets | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-class segmentation of vertebrae is a non-trivial task mainly due to the
high correlation in the appearance of adjacent vertebrae. Hence, such a task
calls for the consideration of both global and local context. Based on this
motivation, we propose a two-staged approach that, given a computed tomography
dataset of the spine, segments the five lumbar vertebrae and simultaneously
labels them. The first stage employs a multi-layered perceptron performing
non-linear regression for locating the lumbar region using the global context.
The second stage, comprised of a fully-convolutional deep network, exploits the
local context in the localised lumbar region to segment and label the lumbar
vertebrae in one go. Aided with practical data augmentation for training, our
approach is highly generalisable, capable of successfully segmenting both
healthy and abnormal vertebrae (fractured and scoliotic spines). We
consistently achieve an average Dice coefficient of over 90 percent on a
publicly available dataset of the xVertSeg segmentation challenge of MICCAI
2016. This is particularly noteworthy because the xVertSeg dataset is beset
with severe deformities in the form of vertebral fractures and scoliosis.
| [
{
"version": "v1",
"created": "Mon, 13 Mar 2017 11:55:16 GMT"
}
] | 2017-03-14T00:00:00 | [
[
"Sekuboyina",
"Anjany",
""
],
[
"Valentinitsch",
"Alexander",
""
],
[
"Kirschke",
"Jan S.",
""
],
[
"Menze",
"Bjoern H.",
""
]
] | TITLE: A Localisation-Segmentation Approach for Multi-label Annotation of
Lumbar Vertebrae using Deep Nets
ABSTRACT: Multi-class segmentation of vertebrae is a non-trivial task mainly due to the
high correlation in the appearance of adjacent vertebrae. Hence, such a task
calls for the consideration of both global and local context. Based on this
motivation, we propose a two-staged approach that, given a computed tomography
dataset of the spine, segments the five lumbar vertebrae and simultaneously
labels them. The first stage employs a multi-layered perceptron performing
non-linear regression for locating the lumbar region using the global context.
The second stage, comprised of a fully-convolutional deep network, exploits the
local context in the localised lumbar region to segment and label the lumbar
vertebrae in one go. Aided with practical data augmentation for training, our
approach is highly generalisable, capable of successfully segmenting both
healthy and abnormal vertebrae (fractured and scoliotic spines). We
consistently achieve an average Dice coefficient of over 90 percent on a
publicly available dataset of the xVertSeg segmentation challenge of MICCAI
2016. This is particularly noteworthy because the xVertSeg dataset is beset
with severe deformities in the form of vertebral fractures and scoliosis.
| no_new_dataset | 0.932392 |
1703.04498 | Preeti Bhargava | Preeti Bhargava, Nemanja Spasojevic, Guoning Hu | High-Throughput and Language-Agnostic Entity Disambiguation and Linking
on User Generated Data | 10 pages, 7 figures, 5 tables, WWW2017, Linked Data on the Web
workshop 2017, LDOW'17 | null | null | null | cs.IR cs.AI cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Entity Disambiguation and Linking (EDL) task matches entity mentions in
text to a unique Knowledge Base (KB) identifier such as a Wikipedia or Freebase
id. It plays a critical role in the construction of a high quality information
network, and can be further leveraged for a variety of information retrieval
and NLP tasks such as text categorization and document tagging. EDL is a
complex and challenging problem due to ambiguity of the mentions and real world
text being multi-lingual. Moreover, EDL systems need to have high throughput
and should be lightweight in order to scale to large datasets and run on
off-the-shelf machines. More importantly, these systems need to be able to
extract and disambiguate dense annotations from the data in order to enable an
Information Retrieval or Extraction task running on the data to be more
efficient and accurate. In order to address all these challenges, we present
the Lithium EDL system and algorithm - a high-throughput, lightweight,
language-agnostic EDL system that extracts and correctly disambiguates 75% more
entities than state-of-the-art EDL systems and is significantly faster than
them.
| [
{
"version": "v1",
"created": "Mon, 13 Mar 2017 17:34:18 GMT"
}
] | 2017-03-14T00:00:00 | [
[
"Bhargava",
"Preeti",
""
],
[
"Spasojevic",
"Nemanja",
""
],
[
"Hu",
"Guoning",
""
]
] | TITLE: High-Throughput and Language-Agnostic Entity Disambiguation and Linking
on User Generated Data
ABSTRACT: The Entity Disambiguation and Linking (EDL) task matches entity mentions in
text to a unique Knowledge Base (KB) identifier such as a Wikipedia or Freebase
id. It plays a critical role in the construction of a high quality information
network, and can be further leveraged for a variety of information retrieval
and NLP tasks such as text categorization and document tagging. EDL is a
complex and challenging problem due to ambiguity of the mentions and real world
text being multi-lingual. Moreover, EDL systems need to have high throughput
and should be lightweight in order to scale to large datasets and run on
off-the-shelf machines. More importantly, these systems need to be able to
extract and disambiguate dense annotations from the data in order to enable an
Information Retrieval or Extraction task running on the data to be more
efficient and accurate. In order to address all these challenges, we present
the Lithium EDL system and algorithm - a high-throughput, lightweight,
language-agnostic EDL system that extracts and correctly disambiguates 75% more
entities than state-of-the-art EDL systems and is significantly faster than
them.
| no_new_dataset | 0.948202 |
1611.01734 | Timothy Dozat | Timothy Dozat and Christopher D. Manning | Deep Biaffine Attention for Neural Dependency Parsing | Accepted to ICLR 2017; updated with new results and comparison to
more recent models, including current state-of-the-art | null | null | null | cs.CL cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper builds off recent work from Kiperwasser & Goldberg (2016) using
neural attention in a simple graph-based dependency parser. We use a larger but
more thoroughly regularized parser than other recent BiLSTM-based approaches,
with biaffine classifiers to predict arcs and labels. Our parser gets state of
the art or near state of the art performance on standard treebanks for six
different languages, achieving 95.7% UAS and 94.1% LAS on the most popular
English PTB dataset. This makes it the highest-performing graph-based parser on
this benchmark---outperforming Kiperwasser Goldberg (2016) by 1.8% and
2.2%---and comparable to the highest performing transition-based parser
(Kuncoro et al., 2016), which achieves 95.8% UAS and 94.6% LAS. We also show
which hyperparameter choices had a significant effect on parsing accuracy,
allowing us to achieve large gains over other graph-based approaches.
| [
{
"version": "v1",
"created": "Sun, 6 Nov 2016 07:26:38 GMT"
},
{
"version": "v2",
"created": "Tue, 22 Nov 2016 02:01:39 GMT"
},
{
"version": "v3",
"created": "Fri, 10 Mar 2017 04:37:03 GMT"
}
] | 2017-03-13T00:00:00 | [
[
"Dozat",
"Timothy",
""
],
[
"Manning",
"Christopher D.",
""
]
] | TITLE: Deep Biaffine Attention for Neural Dependency Parsing
ABSTRACT: This paper builds off recent work from Kiperwasser & Goldberg (2016) using
neural attention in a simple graph-based dependency parser. We use a larger but
more thoroughly regularized parser than other recent BiLSTM-based approaches,
with biaffine classifiers to predict arcs and labels. Our parser gets state of
the art or near state of the art performance on standard treebanks for six
different languages, achieving 95.7% UAS and 94.1% LAS on the most popular
English PTB dataset. This makes it the highest-performing graph-based parser on
this benchmark---outperforming Kiperwasser Goldberg (2016) by 1.8% and
2.2%---and comparable to the highest performing transition-based parser
(Kuncoro et al., 2016), which achieves 95.8% UAS and 94.6% LAS. We also show
which hyperparameter choices had a significant effect on parsing accuracy,
allowing us to achieve large gains over other graph-based approaches.
| no_new_dataset | 0.953579 |
1611.01886 | Wentao Huang | Wentao Huang and Kechen Zhang | An Information-Theoretic Framework for Fast and Robust Unsupervised
Learning via Neural Population Infomax | 25 pages, 7 figures, 5th International Conference on Learning
Representations (ICLR 2017) | null | null | null | cs.LG cs.AI cs.IT math.IT q-bio.NC stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A framework is presented for unsupervised learning of representations based
on infomax principle for large-scale neural populations. We use an asymptotic
approximation to the Shannon's mutual information for a large neural population
to demonstrate that a good initial approximation to the global
information-theoretic optimum can be obtained by a hierarchical infomax method.
Starting from the initial solution, an efficient algorithm based on gradient
descent of the final objective function is proposed to learn representations
from the input datasets, and the method works for complete, overcomplete, and
undercomplete bases. As confirmed by numerical experiments, our method is
robust and highly efficient for extracting salient features from input
datasets. Compared with the main existing methods, our algorithm has a distinct
advantage in both the training speed and the robustness of unsupervised
representation learning. Furthermore, the proposed method is easily extended to
the supervised or unsupervised model for training deep structure networks.
| [
{
"version": "v1",
"created": "Mon, 7 Nov 2016 04:17:28 GMT"
},
{
"version": "v2",
"created": "Thu, 19 Jan 2017 17:53:31 GMT"
},
{
"version": "v3",
"created": "Mon, 6 Feb 2017 17:11:34 GMT"
},
{
"version": "v4",
"created": "Fri, 10 Mar 2017 16:41:16 GMT"
}
] | 2017-03-13T00:00:00 | [
[
"Huang",
"Wentao",
""
],
[
"Zhang",
"Kechen",
""
]
] | TITLE: An Information-Theoretic Framework for Fast and Robust Unsupervised
Learning via Neural Population Infomax
ABSTRACT: A framework is presented for unsupervised learning of representations based
on infomax principle for large-scale neural populations. We use an asymptotic
approximation to the Shannon's mutual information for a large neural population
to demonstrate that a good initial approximation to the global
information-theoretic optimum can be obtained by a hierarchical infomax method.
Starting from the initial solution, an efficient algorithm based on gradient
descent of the final objective function is proposed to learn representations
from the input datasets, and the method works for complete, overcomplete, and
undercomplete bases. As confirmed by numerical experiments, our method is
robust and highly efficient for extracting salient features from input
datasets. Compared with the main existing methods, our algorithm has a distinct
advantage in both the training speed and the robustness of unsupervised
representation learning. Furthermore, the proposed method is easily extended to
the supervised or unsupervised model for training deep structure networks.
| no_new_dataset | 0.946448 |
1701.03126 | Chiori Hori Dr. | Chiori Hori, Takaaki Hori, Teng-Yok Lee, Kazuhiro Sumi, John R.
Hershey, Tim K. Marks | Attention-Based Multimodal Fusion for Video Description | Resubmitted to the rebuttal for CVPR 2017 for review, 8 pages, 4
figures | null | null | null | cs.CV cs.CL cs.MM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Currently successful methods for video description are based on
encoder-decoder sentence generation using recur-rent neural networks (RNNs).
Recent work has shown the advantage of integrating temporal and/or spatial
attention mechanisms into these models, in which the decoder net-work predicts
each word in the description by selectively giving more weight to encoded
features from specific time frames (temporal attention) or to features from
specific spatial regions (spatial attention). In this paper, we propose to
expand the attention model to selectively attend not just to specific times or
spatial regions, but to specific modalities of input such as image features,
motion features, and audio features. Our new modality-dependent attention
mechanism, which we call multimodal attention, provides a natural way to fuse
multimodal information for video description. We evaluate our method on the
Youtube2Text dataset, achieving results that are competitive with current state
of the art. More importantly, we demonstrate that our model incorporating
multimodal attention as well as temporal attention significantly outperforms
the model that uses temporal attention alone.
| [
{
"version": "v1",
"created": "Wed, 11 Jan 2017 19:16:42 GMT"
},
{
"version": "v2",
"created": "Thu, 9 Mar 2017 22:57:10 GMT"
}
] | 2017-03-13T00:00:00 | [
[
"Hori",
"Chiori",
""
],
[
"Hori",
"Takaaki",
""
],
[
"Lee",
"Teng-Yok",
""
],
[
"Sumi",
"Kazuhiro",
""
],
[
"Hershey",
"John R.",
""
],
[
"Marks",
"Tim K.",
""
]
] | TITLE: Attention-Based Multimodal Fusion for Video Description
ABSTRACT: Currently successful methods for video description are based on
encoder-decoder sentence generation using recur-rent neural networks (RNNs).
Recent work has shown the advantage of integrating temporal and/or spatial
attention mechanisms into these models, in which the decoder net-work predicts
each word in the description by selectively giving more weight to encoded
features from specific time frames (temporal attention) or to features from
specific spatial regions (spatial attention). In this paper, we propose to
expand the attention model to selectively attend not just to specific times or
spatial regions, but to specific modalities of input such as image features,
motion features, and audio features. Our new modality-dependent attention
mechanism, which we call multimodal attention, provides a natural way to fuse
multimodal information for video description. We evaluate our method on the
Youtube2Text dataset, achieving results that are competitive with current state
of the art. More importantly, we demonstrate that our model incorporating
multimodal attention as well as temporal attention significantly outperforms
the model that uses temporal attention alone.
| no_new_dataset | 0.949949 |
1703.00785 | Anthony Faustine Sambaiga | Anthony Faustine, Nerey Henry Mvungi, Shubi Kaijage, Kisangiri Michael | A Survey on Non-Intrusive Load Monitoring Methodies and Techniques for
Energy Disaggregation Problem | null | null | null | null | cs.OH | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The rapid urbanization of developing countries coupled with explosion in
construction of high rising buildings and the high power usage in them calls
for conservation and efficient energy program. Such a program require
monitoring of end-use appliances energy consumption in real-time. The worldwide
recent adoption of smart-meter in smart-grid, has led to the rise of
Non-Intrusive Load Monitoring (NILM); which enables estimation of
appliance-specific power consumption from building's aggregate power
consumption reading. NILM provides households with cost-effective real-time
monitoring of end-use appliances to help them understand their consumption
pattern and become part and parcel of energy conservation strategy. This paper
presents an up to date overview of NILM system and its associated methods and
techniques for energy disaggregation problem. This is followed by the review of
the state-of-the art NILM algorithms. Furthermore, we review several
performance metrics used by NILM researcher to evaluate NILM algorithms and
discuss existing benchmarking framework for direct comparison of the state of
the art NILM algorithms. Finally, the paper discuss potential NILM use-cases,
presents an overview of the public available dataset and highlight challenges
and future research directions.
| [
{
"version": "v1",
"created": "Thu, 2 Mar 2017 13:52:30 GMT"
},
{
"version": "v2",
"created": "Fri, 3 Mar 2017 04:59:51 GMT"
},
{
"version": "v3",
"created": "Fri, 10 Mar 2017 17:13:52 GMT"
}
] | 2017-03-13T00:00:00 | [
[
"Faustine",
"Anthony",
""
],
[
"Mvungi",
"Nerey Henry",
""
],
[
"Kaijage",
"Shubi",
""
],
[
"Michael",
"Kisangiri",
""
]
] | TITLE: A Survey on Non-Intrusive Load Monitoring Methodies and Techniques for
Energy Disaggregation Problem
ABSTRACT: The rapid urbanization of developing countries coupled with explosion in
construction of high rising buildings and the high power usage in them calls
for conservation and efficient energy program. Such a program require
monitoring of end-use appliances energy consumption in real-time. The worldwide
recent adoption of smart-meter in smart-grid, has led to the rise of
Non-Intrusive Load Monitoring (NILM); which enables estimation of
appliance-specific power consumption from building's aggregate power
consumption reading. NILM provides households with cost-effective real-time
monitoring of end-use appliances to help them understand their consumption
pattern and become part and parcel of energy conservation strategy. This paper
presents an up to date overview of NILM system and its associated methods and
techniques for energy disaggregation problem. This is followed by the review of
the state-of-the art NILM algorithms. Furthermore, we review several
performance metrics used by NILM researcher to evaluate NILM algorithms and
discuss existing benchmarking framework for direct comparison of the state of
the art NILM algorithms. Finally, the paper discuss potential NILM use-cases,
presents an overview of the public available dataset and highlight challenges
and future research directions.
| no_new_dataset | 0.937038 |
1703.02438 | Zheng Yuan | Zheng Yuan, William Hendrix, Seung Woo Son, Christoph Federrath, Ankit
Agrawal, Wei-keng Liao, Alok Choudhary | Parallel Implementation of Lossy Data Compression for Temporal Data Sets | 10 pages, HiPC 2016 | null | 10.1109/HiPC.2016.017 | null | cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many scientific data sets contain temporal dimensions. These are the data
storing information at the same spatial location but different time stamps.
Some of the biggest temporal datasets are produced by parallel computing
applications such as simulations of climate change and fluid dynamics. Temporal
datasets can be very large and cost a huge amount of time to transfer among
storage locations. Using data compression techniques, files can be transferred
faster and save storage space. NUMARCK is a lossy data compression algorithm
for temporal data sets that can learn emerging distributions of element-wise
change ratios along the temporal dimension and encodes them into an index table
to be concisely represented. This paper presents a parallel implementation of
NUMARCK. Evaluated with six data sets obtained from climate and astrophysics
simulations, parallel NUMARCK achieved scalable speedups of up to 8788 when
running 12800 MPI processes on a parallel computer. We also compare the
compression ratios against two lossy data compression algorithms, ISABELA and
ZFP. The results show that NUMARCK achieved higher compression ratio than
ISABELA and ZFP.
| [
{
"version": "v1",
"created": "Tue, 7 Mar 2017 15:37:30 GMT"
}
] | 2017-03-13T00:00:00 | [
[
"Yuan",
"Zheng",
""
],
[
"Hendrix",
"William",
""
],
[
"Son",
"Seung Woo",
""
],
[
"Federrath",
"Christoph",
""
],
[
"Agrawal",
"Ankit",
""
],
[
"Liao",
"Wei-keng",
""
],
[
"Choudhary",
"Alok",
""
]
] | TITLE: Parallel Implementation of Lossy Data Compression for Temporal Data Sets
ABSTRACT: Many scientific data sets contain temporal dimensions. These are the data
storing information at the same spatial location but different time stamps.
Some of the biggest temporal datasets are produced by parallel computing
applications such as simulations of climate change and fluid dynamics. Temporal
datasets can be very large and cost a huge amount of time to transfer among
storage locations. Using data compression techniques, files can be transferred
faster and save storage space. NUMARCK is a lossy data compression algorithm
for temporal data sets that can learn emerging distributions of element-wise
change ratios along the temporal dimension and encodes them into an index table
to be concisely represented. This paper presents a parallel implementation of
NUMARCK. Evaluated with six data sets obtained from climate and astrophysics
simulations, parallel NUMARCK achieved scalable speedups of up to 8788 when
running 12800 MPI processes on a parallel computer. We also compare the
compression ratios against two lossy data compression algorithms, ISABELA and
ZFP. The results show that NUMARCK achieved higher compression ratio than
ISABELA and ZFP.
| no_new_dataset | 0.94366 |
1703.03200 | Murahtan Kurfal{\i} | Burcu Can, Ahmet \"Ust\"un, Murathan Kurfal{\i} | Turkish PoS Tagging by Reducing Sparsity with Morpheme Tags in Small
Datasets | 13 pages, accepted and presented in 17th International Conference on
Intelligent Text Processing and Computational Linguistics (CICLING) | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sparsity is one of the major problems in natural language processing. The
problem becomes even more severe in agglutinating languages that are highly
prone to be inflected. We deal with sparsity in Turkish by adopting
morphological features for part-of-speech tagging. We learn inflectional and
derivational morpheme tags in Turkish by using conditional random fields (CRF)
and we employ the morpheme tags in part-of-speech (PoS) tagging by using hidden
Markov models (HMMs) to mitigate sparsity. Results show that using morpheme
tags in PoS tagging helps alleviate the sparsity in emission probabilities. Our
model outperforms other hidden Markov model based PoS tagging models for small
training datasets in Turkish. We obtain an accuracy of 94.1% in morpheme
tagging and 89.2% in PoS tagging on a 5K training dataset.
| [
{
"version": "v1",
"created": "Thu, 9 Mar 2017 09:46:56 GMT"
},
{
"version": "v2",
"created": "Fri, 10 Mar 2017 08:11:22 GMT"
}
] | 2017-03-13T00:00:00 | [
[
"Can",
"Burcu",
""
],
[
"Üstün",
"Ahmet",
""
],
[
"Kurfalı",
"Murathan",
""
]
] | TITLE: Turkish PoS Tagging by Reducing Sparsity with Morpheme Tags in Small
Datasets
ABSTRACT: Sparsity is one of the major problems in natural language processing. The
problem becomes even more severe in agglutinating languages that are highly
prone to be inflected. We deal with sparsity in Turkish by adopting
morphological features for part-of-speech tagging. We learn inflectional and
derivational morpheme tags in Turkish by using conditional random fields (CRF)
and we employ the morpheme tags in part-of-speech (PoS) tagging by using hidden
Markov models (HMMs) to mitigate sparsity. Results show that using morpheme
tags in PoS tagging helps alleviate the sparsity in emission probabilities. Our
model outperforms other hidden Markov model based PoS tagging models for small
training datasets in Turkish. We obtain an accuracy of 94.1% in morpheme
tagging and 89.2% in PoS tagging on a 5K training dataset.
| no_new_dataset | 0.958069 |
1703.03567 | Ruoyu Liu | Ruoyu Liu, Yao Zhao, Liang Zheng, Shikui Wei and Yi Yang | A New Evaluation Protocol and Benchmarking Results for Extendable
Cross-media Retrieval | 10 pages, 9 figures | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes a new evaluation protocol for cross-media retrieval which
better fits the real-word applications. Both image-text and text-image
retrieval modes are considered. Traditionally, class labels in the training and
testing sets are identical. That is, it is usually assumed that the query falls
into some pre-defined classes. However, in practice, the content of a query
image/text may vary extensively, and the retrieval system does not necessarily
know in advance the class label of a query. Considering the inconsistency
between the real-world applications and laboratory assumptions, we think that
the existing protocol that works under identical train/test classes can be
modified and improved.
This work is dedicated to addressing this problem by considering the protocol
under an extendable scenario, \ie, the training and testing classes do not
overlap. We provide extensive benchmarking results obtained by the existing
protocol and the proposed new protocol on several commonly used datasets. We
demonstrate a noticeable performance drop when the testing classes are unseen
during training. Additionally, a trivial solution, \ie, directly using the
predicted class label for cross-media retrieval, is tested. We show that the
trivial solution is very competitive in traditional non-extendable retrieval,
but becomes less so under the new settings. The train/test split, evaluation
code, and benchmarking results are publicly available on our website.
| [
{
"version": "v1",
"created": "Fri, 10 Mar 2017 07:56:01 GMT"
}
] | 2017-03-13T00:00:00 | [
[
"Liu",
"Ruoyu",
""
],
[
"Zhao",
"Yao",
""
],
[
"Zheng",
"Liang",
""
],
[
"Wei",
"Shikui",
""
],
[
"Yang",
"Yi",
""
]
] | TITLE: A New Evaluation Protocol and Benchmarking Results for Extendable
Cross-media Retrieval
ABSTRACT: This paper proposes a new evaluation protocol for cross-media retrieval which
better fits the real-word applications. Both image-text and text-image
retrieval modes are considered. Traditionally, class labels in the training and
testing sets are identical. That is, it is usually assumed that the query falls
into some pre-defined classes. However, in practice, the content of a query
image/text may vary extensively, and the retrieval system does not necessarily
know in advance the class label of a query. Considering the inconsistency
between the real-world applications and laboratory assumptions, we think that
the existing protocol that works under identical train/test classes can be
modified and improved.
This work is dedicated to addressing this problem by considering the protocol
under an extendable scenario, \ie, the training and testing classes do not
overlap. We provide extensive benchmarking results obtained by the existing
protocol and the proposed new protocol on several commonly used datasets. We
demonstrate a noticeable performance drop when the testing classes are unseen
during training. Additionally, a trivial solution, \ie, directly using the
predicted class label for cross-media retrieval, is tested. We show that the
trivial solution is very competitive in traditional non-extendable retrieval,
but becomes less so under the new settings. The train/test split, evaluation
code, and benchmarking results are publicly available on our website.
| no_new_dataset | 0.948346 |
1703.03609 | Mostafa Salehi | Saeedreza Shehnepoor, Mostafa Salehi, Reza Farahbakhsh and Noel Crespi | NetSpam: a Network-based Spam Detection Framework for Reviews in Online
Social Media | null | null | 10.1109/TIFS.2017.2675361 | null | cs.SI cs.CL cs.IR physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Nowadays, a big part of people rely on available content in social media in
their decisions (e.g. reviews and feedback on a topic or product). The
possibility that anybody can leave a review provide a golden opportunity for
spammers to write spam reviews about products and services for different
interests. Identifying these spammers and the spam content is a hot topic of
research and although a considerable number of studies have been done recently
toward this end, but so far the methodologies put forth still barely detect
spam reviews, and none of them show the importance of each extracted feature
type. In this study, we propose a novel framework, named NetSpam, which
utilizes spam features for modeling review datasets as heterogeneous
information networks to map spam detection procedure into a classification
problem in such networks. Using the importance of spam features help us to
obtain better results in terms of different metrics experimented on real-world
review datasets from Yelp and Amazon websites. The results show that NetSpam
outperforms the existing methods and among four categories of features;
including review-behavioral, user-behavioral, reviewlinguistic,
user-linguistic, the first type of features performs better than the other
categories.
| [
{
"version": "v1",
"created": "Fri, 10 Mar 2017 10:17:27 GMT"
}
] | 2017-03-13T00:00:00 | [
[
"Shehnepoor",
"Saeedreza",
""
],
[
"Salehi",
"Mostafa",
""
],
[
"Farahbakhsh",
"Reza",
""
],
[
"Crespi",
"Noel",
""
]
] | TITLE: NetSpam: a Network-based Spam Detection Framework for Reviews in Online
Social Media
ABSTRACT: Nowadays, a big part of people rely on available content in social media in
their decisions (e.g. reviews and feedback on a topic or product). The
possibility that anybody can leave a review provide a golden opportunity for
spammers to write spam reviews about products and services for different
interests. Identifying these spammers and the spam content is a hot topic of
research and although a considerable number of studies have been done recently
toward this end, but so far the methodologies put forth still barely detect
spam reviews, and none of them show the importance of each extracted feature
type. In this study, we propose a novel framework, named NetSpam, which
utilizes spam features for modeling review datasets as heterogeneous
information networks to map spam detection procedure into a classification
problem in such networks. Using the importance of spam features help us to
obtain better results in terms of different metrics experimented on real-world
review datasets from Yelp and Amazon websites. The results show that NetSpam
outperforms the existing methods and among four categories of features;
including review-behavioral, user-behavioral, reviewlinguistic,
user-linguistic, the first type of features performs better than the other
categories.
| no_new_dataset | 0.944893 |
1703.03624 | Guido Borghi | Marco Venturelli, Guido Borghi, Roberto Vezzani, Rita Cucchiara | From Depth Data to Head Pose Estimation: a Siamese approach | VISAPP 2017. arXiv admin note: text overlap with arXiv:1703.01883 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The correct estimation of the head pose is a problem of the great importance
for many applications. For instance, it is an enabling technology in automotive
for driver attention monitoring. In this paper, we tackle the pose estimation
problem through a deep learning network working in regression manner.
Traditional methods usually rely on visual facial features, such as facial
landmarks or nose tip position. In contrast, we exploit a Convolutional Neural
Network (CNN) to perform head pose estimation directly from depth data. We
exploit a Siamese architecture and we propose a novel loss function to improve
the learning of the regression network layer. The system has been tested on two
public datasets, Biwi Kinect Head Pose and ICT-3DHP database. The reported
results demonstrate the improvement in accuracy with respect to current
state-of-the-art approaches and the real time capabilities of the overall
framework.
| [
{
"version": "v1",
"created": "Fri, 10 Mar 2017 11:08:50 GMT"
}
] | 2017-03-13T00:00:00 | [
[
"Venturelli",
"Marco",
""
],
[
"Borghi",
"Guido",
""
],
[
"Vezzani",
"Roberto",
""
],
[
"Cucchiara",
"Rita",
""
]
] | TITLE: From Depth Data to Head Pose Estimation: a Siamese approach
ABSTRACT: The correct estimation of the head pose is a problem of the great importance
for many applications. For instance, it is an enabling technology in automotive
for driver attention monitoring. In this paper, we tackle the pose estimation
problem through a deep learning network working in regression manner.
Traditional methods usually rely on visual facial features, such as facial
landmarks or nose tip position. In contrast, we exploit a Convolutional Neural
Network (CNN) to perform head pose estimation directly from depth data. We
exploit a Siamese architecture and we propose a novel loss function to improve
the learning of the regression network layer. The system has been tested on two
public datasets, Biwi Kinect Head Pose and ICT-3DHP database. The reported
results demonstrate the improvement in accuracy with respect to current
state-of-the-art approaches and the real time capabilities of the overall
framework.
| no_new_dataset | 0.950457 |
1703.03640 | Christina Lioma Assoc. Prof | Christina Lioma and Niels Dalum Hansen | A Study of Metrics of Distance and Correlation Between Ranked Lists for
Compositionality Detection | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Compositionality in language refers to how much the meaning of some phrase
can be decomposed into the meaning of its constituents and the way these
constituents are combined. Based on the premise that substitution by synonyms
is meaning-preserving, compositionality can be approximated as the semantic
similarity between a phrase and a version of that phrase where words have been
replaced by their synonyms. Different ways of representing such phrases exist
(e.g., vectors [1] or language models [2]), and the choice of representation
affects the measurement of semantic similarity.
We propose a new compositionality detection method that represents phrases as
ranked lists of term weights. Our method approximates the semantic similarity
between two ranked list representations using a range of well-known distance
and correlation metrics. In contrast to most state-of-the-art approaches in
compositionality detection, our method is completely unsupervised. Experiments
with a publicly available dataset of 1048 human-annotated phrases shows that,
compared to strong supervised baselines, our approach provides superior
measurement of compositionality using any of the distance and correlation
metrics considered.
| [
{
"version": "v1",
"created": "Fri, 10 Mar 2017 11:58:48 GMT"
}
] | 2017-03-13T00:00:00 | [
[
"Lioma",
"Christina",
""
],
[
"Hansen",
"Niels Dalum",
""
]
] | TITLE: A Study of Metrics of Distance and Correlation Between Ranked Lists for
Compositionality Detection
ABSTRACT: Compositionality in language refers to how much the meaning of some phrase
can be decomposed into the meaning of its constituents and the way these
constituents are combined. Based on the premise that substitution by synonyms
is meaning-preserving, compositionality can be approximated as the semantic
similarity between a phrase and a version of that phrase where words have been
replaced by their synonyms. Different ways of representing such phrases exist
(e.g., vectors [1] or language models [2]), and the choice of representation
affects the measurement of semantic similarity.
We propose a new compositionality detection method that represents phrases as
ranked lists of term weights. Our method approximates the semantic similarity
between two ranked list representations using a range of well-known distance
and correlation metrics. In contrast to most state-of-the-art approaches in
compositionality detection, our method is completely unsupervised. Experiments
with a publicly available dataset of 1048 human-annotated phrases shows that,
compared to strong supervised baselines, our approach provides superior
measurement of compositionality using any of the distance and correlation
metrics considered.
| no_new_dataset | 0.878158 |
1510.00552 | Daniele Ramazzotti | Francesco Bonchi, Sara Hajian, Bud Mishra, Daniele Ramazzotti | Exposing the Probabilistic Causal Structure of Discrimination | null | null | 10.1007/s41060-016-0040-z | null | cs.DB cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Discrimination discovery from data is an important task aiming at identifying
patterns of illegal and unethical discriminatory activities against
protected-by-law groups, e.g., ethnic minorities. While any legally-valid proof
of discrimination requires evidence of causality, the state-of-the-art methods
are essentially correlation-based, albeit, as it is well known, correlation
does not imply causation.
In this paper we take a principled causal approach to the data mining problem
of discrimination detection in databases. Following Suppes' probabilistic
causation theory, we define a method to extract, from a dataset of historical
decision records, the causal structures existing among the attributes in the
data. The result is a type of constrained Bayesian network, which we dub
Suppes-Bayes Causal Network (SBCN). Next, we develop a toolkit of methods based
on random walks on top of the SBCN, addressing different anti-discrimination
legal concepts, such as direct and indirect discrimination, group and
individual discrimination, genuine requirement, and favoritism. Our experiments
on real-world datasets confirm the inferential power of our approach in all
these different tasks.
| [
{
"version": "v1",
"created": "Fri, 2 Oct 2015 10:31:29 GMT"
},
{
"version": "v2",
"created": "Mon, 5 Oct 2015 08:38:16 GMT"
},
{
"version": "v3",
"created": "Wed, 8 Mar 2017 21:10:10 GMT"
}
] | 2017-03-10T00:00:00 | [
[
"Bonchi",
"Francesco",
""
],
[
"Hajian",
"Sara",
""
],
[
"Mishra",
"Bud",
""
],
[
"Ramazzotti",
"Daniele",
""
]
] | TITLE: Exposing the Probabilistic Causal Structure of Discrimination
ABSTRACT: Discrimination discovery from data is an important task aiming at identifying
patterns of illegal and unethical discriminatory activities against
protected-by-law groups, e.g., ethnic minorities. While any legally-valid proof
of discrimination requires evidence of causality, the state-of-the-art methods
are essentially correlation-based, albeit, as it is well known, correlation
does not imply causation.
In this paper we take a principled causal approach to the data mining problem
of discrimination detection in databases. Following Suppes' probabilistic
causation theory, we define a method to extract, from a dataset of historical
decision records, the causal structures existing among the attributes in the
data. The result is a type of constrained Bayesian network, which we dub
Suppes-Bayes Causal Network (SBCN). Next, we develop a toolkit of methods based
on random walks on top of the SBCN, addressing different anti-discrimination
legal concepts, such as direct and indirect discrimination, group and
individual discrimination, genuine requirement, and favoritism. Our experiments
on real-world datasets confirm the inferential power of our approach in all
these different tasks.
| no_new_dataset | 0.943452 |
1606.06461 | Dat Quoc Nguyen | Dat Quoc Nguyen, Kairit Sirts, Lizhen Qu and Mark Johnson | Neighborhood Mixture Model for Knowledge Base Completion | V1: In Proceedings of the 20th SIGNLL Conference on Computational
Natural Language Learning, CoNLL 2016. V2: Corrected citation to (Krompa{\ss}
et al., 2015). V3: A revised version of our CoNLL 2016 paper to update latest
related work | null | 10.18653/v1/K16-1005 | null | cs.CL cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge bases are useful resources for many natural language processing
tasks, however, they are far from complete. In this paper, we define a novel
entity representation as a mixture of its neighborhood in the knowledge base
and apply this technique on TransE-a well-known embedding model for knowledge
base completion. Experimental results show that the neighborhood information
significantly helps to improve the results of the TransE model, leading to
better performance than obtained by other state-of-the-art embedding models on
three benchmark datasets for triple classification, entity prediction and
relation prediction tasks.
| [
{
"version": "v1",
"created": "Tue, 21 Jun 2016 07:54:35 GMT"
},
{
"version": "v2",
"created": "Thu, 21 Jul 2016 16:08:32 GMT"
},
{
"version": "v3",
"created": "Thu, 9 Mar 2017 12:51:31 GMT"
}
] | 2017-03-10T00:00:00 | [
[
"Nguyen",
"Dat Quoc",
""
],
[
"Sirts",
"Kairit",
""
],
[
"Qu",
"Lizhen",
""
],
[
"Johnson",
"Mark",
""
]
] | TITLE: Neighborhood Mixture Model for Knowledge Base Completion
ABSTRACT: Knowledge bases are useful resources for many natural language processing
tasks, however, they are far from complete. In this paper, we define a novel
entity representation as a mixture of its neighborhood in the knowledge base
and apply this technique on TransE-a well-known embedding model for knowledge
base completion. Experimental results show that the neighborhood information
significantly helps to improve the results of the TransE model, leading to
better performance than obtained by other state-of-the-art embedding models on
three benchmark datasets for triple classification, entity prediction and
relation prediction tasks.
| no_new_dataset | 0.949763 |
1610.08452 | Muhammad Bilal Zafar | Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, Krishna
P. Gummadi | Fairness Beyond Disparate Treatment & Disparate Impact: Learning
Classification without Disparate Mistreatment | To appear in Proceedings of the 26th International World Wide Web
Conference (WWW), 2017. Code available at:
https://github.com/mbilalzafar/fair-classification | null | 10.1145/3038912.3052660 | null | stat.ML cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Automated data-driven decision making systems are increasingly being used to
assist, or even replace humans in many settings. These systems function by
learning from historical decisions, often taken by humans. In order to maximize
the utility of these systems (or, classifiers), their training involves
minimizing the errors (or, misclassifications) over the given historical data.
However, it is quite possible that the optimally trained classifier makes
decisions for people belonging to different social groups with different
misclassification rates (e.g., misclassification rates for females are higher
than for males), thereby placing these groups at an unfair disadvantage. To
account for and avoid such unfairness, in this paper, we introduce a new notion
of unfairness, disparate mistreatment, which is defined in terms of
misclassification rates. We then propose intuitive measures of disparate
mistreatment for decision boundary-based classifiers, which can be easily
incorporated into their formulation as convex-concave constraints. Experiments
on synthetic as well as real world datasets show that our methodology is
effective at avoiding disparate mistreatment, often at a small cost in terms of
accuracy.
| [
{
"version": "v1",
"created": "Wed, 26 Oct 2016 18:34:48 GMT"
},
{
"version": "v2",
"created": "Wed, 8 Mar 2017 19:04:28 GMT"
}
] | 2017-03-10T00:00:00 | [
[
"Zafar",
"Muhammad Bilal",
""
],
[
"Valera",
"Isabel",
""
],
[
"Rodriguez",
"Manuel Gomez",
""
],
[
"Gummadi",
"Krishna P.",
""
]
] | TITLE: Fairness Beyond Disparate Treatment & Disparate Impact: Learning
Classification without Disparate Mistreatment
ABSTRACT: Automated data-driven decision making systems are increasingly being used to
assist, or even replace humans in many settings. These systems function by
learning from historical decisions, often taken by humans. In order to maximize
the utility of these systems (or, classifiers), their training involves
minimizing the errors (or, misclassifications) over the given historical data.
However, it is quite possible that the optimally trained classifier makes
decisions for people belonging to different social groups with different
misclassification rates (e.g., misclassification rates for females are higher
than for males), thereby placing these groups at an unfair disadvantage. To
account for and avoid such unfairness, in this paper, we introduce a new notion
of unfairness, disparate mistreatment, which is defined in terms of
misclassification rates. We then propose intuitive measures of disparate
mistreatment for decision boundary-based classifiers, which can be easily
incorporated into their formulation as convex-concave constraints. Experiments
on synthetic as well as real world datasets show that our methodology is
effective at avoiding disparate mistreatment, often at a small cost in terms of
accuracy.
| no_new_dataset | 0.946646 |
1701.02046 | Ping Li | Ping Li | Tunable GMM Kernels | null | null | null | null | stat.ML cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The recently proposed "generalized min-max" (GMM) kernel can be efficiently
linearized, with direct applications in large-scale statistical learning and
fast near neighbor search. The linearized GMM kernel was extensively compared
in with linearized radial basis function (RBF) kernel. On a large number of
classification tasks, the tuning-free GMM kernel performs (surprisingly) well
compared to the best-tuned RBF kernel. Nevertheless, one would naturally expect
that the GMM kernel ought to be further improved if we introduce tuning
parameters.
In this paper, we study three simple constructions of tunable GMM kernels:
(i) the exponentiated-GMM (or eGMM) kernel, (ii) the powered-GMM (or pGMM)
kernel, and (iii) the exponentiated-powered-GMM (epGMM) kernel. The pGMM kernel
can still be efficiently linearized by modifying the original hashing procedure
for the GMM kernel. On about 60 publicly available classification datasets, we
verify that the proposed tunable GMM kernels typically improve over the
original GMM kernel. On some datasets, the improvements can be astonishingly
significant.
For example, on 11 popular datasets which were used for testing deep learning
algorithms and tree methods, our experiments show that the proposed tunable GMM
kernels are strong competitors to trees and deep nets. The previous studies
developed tree methods including "abc-robust-logitboost" and demonstrated the
excellent performance on those 11 datasets (and other datasets), by
establishing the second-order tree-split formula and new derivatives for
multi-class logistic loss. Compared to tree methods like
"abc-robust-logitboost" (which are slow and need substantial model sizes), the
tunable GMM kernels produce largely comparable results.
| [
{
"version": "v1",
"created": "Mon, 9 Jan 2017 01:20:55 GMT"
},
{
"version": "v2",
"created": "Thu, 9 Mar 2017 17:25:16 GMT"
}
] | 2017-03-10T00:00:00 | [
[
"Li",
"Ping",
""
]
] | TITLE: Tunable GMM Kernels
ABSTRACT: The recently proposed "generalized min-max" (GMM) kernel can be efficiently
linearized, with direct applications in large-scale statistical learning and
fast near neighbor search. The linearized GMM kernel was extensively compared
in with linearized radial basis function (RBF) kernel. On a large number of
classification tasks, the tuning-free GMM kernel performs (surprisingly) well
compared to the best-tuned RBF kernel. Nevertheless, one would naturally expect
that the GMM kernel ought to be further improved if we introduce tuning
parameters.
In this paper, we study three simple constructions of tunable GMM kernels:
(i) the exponentiated-GMM (or eGMM) kernel, (ii) the powered-GMM (or pGMM)
kernel, and (iii) the exponentiated-powered-GMM (epGMM) kernel. The pGMM kernel
can still be efficiently linearized by modifying the original hashing procedure
for the GMM kernel. On about 60 publicly available classification datasets, we
verify that the proposed tunable GMM kernels typically improve over the
original GMM kernel. On some datasets, the improvements can be astonishingly
significant.
For example, on 11 popular datasets which were used for testing deep learning
algorithms and tree methods, our experiments show that the proposed tunable GMM
kernels are strong competitors to trees and deep nets. The previous studies
developed tree methods including "abc-robust-logitboost" and demonstrated the
excellent performance on those 11 datasets (and other datasets), by
establishing the second-order tree-split formula and new derivatives for
multi-class logistic loss. Compared to tree methods like
"abc-robust-logitboost" (which are slow and need substantial model sizes), the
tunable GMM kernels produce largely comparable results.
| no_new_dataset | 0.947962 |
1703.02992 | Stephen Giguere | Stephen Giguere, Francisco Garcia, Sridhar Mahadevan | A Manifold Approach to Learning Mutually Orthogonal Subspaces | 9 pages, 3 Figures | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Although many machine learning algorithms involve learning subspaces with
particular characteristics, optimizing a parameter matrix that is constrained
to represent a subspace can be challenging. One solution is to use Riemannian
optimization methods that enforce such constraints implicitly, leveraging the
fact that the feasible parameter values form a manifold. While Riemannian
methods exist for some specific problems, such as learning a single subspace,
there are more general subspace constraints that offer additional flexibility
when setting up an optimization problem, but have not been formulated as a
manifold.
We propose the partitioned subspace (PS) manifold for optimizing matrices
that are constrained to represent one or more subspaces. Each point on the
manifold defines a partitioning of the input space into mutually orthogonal
subspaces, where the number of partitions and their sizes are defined by the
user. As a result, distinct groups of features can be learned by defining
different objective functions for each partition. We illustrate the properties
of the manifold through experiments on multiple dataset analysis and domain
adaptation.
| [
{
"version": "v1",
"created": "Wed, 8 Mar 2017 19:08:28 GMT"
}
] | 2017-03-10T00:00:00 | [
[
"Giguere",
"Stephen",
""
],
[
"Garcia",
"Francisco",
""
],
[
"Mahadevan",
"Sridhar",
""
]
] | TITLE: A Manifold Approach to Learning Mutually Orthogonal Subspaces
ABSTRACT: Although many machine learning algorithms involve learning subspaces with
particular characteristics, optimizing a parameter matrix that is constrained
to represent a subspace can be challenging. One solution is to use Riemannian
optimization methods that enforce such constraints implicitly, leveraging the
fact that the feasible parameter values form a manifold. While Riemannian
methods exist for some specific problems, such as learning a single subspace,
there are more general subspace constraints that offer additional flexibility
when setting up an optimization problem, but have not been formulated as a
manifold.
We propose the partitioned subspace (PS) manifold for optimizing matrices
that are constrained to represent one or more subspaces. Each point on the
manifold defines a partitioning of the input space into mutually orthogonal
subspaces, where the number of partitions and their sizes are defined by the
user. As a result, distinct groups of features can be learned by defining
different objective functions for each partition. We illustrate the properties
of the manifold through experiments on multiple dataset analysis and domain
adaptation.
| no_new_dataset | 0.946941 |
1703.03054 | Xiaodan Liang | Xiaodan Liang and Lisa Lee and Eric P. Xing | Deep Variation-structured Reinforcement Learning for Visual Relationship
and Attribute Detection | This manuscript is accepted by CVPR 2017 as a spotlight paper | null | null | null | cs.CV cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite progress in visual perception tasks such as image classification and
detection, computers still struggle to understand the interdependency of
objects in the scene as a whole, e.g., relations between objects or their
attributes. Existing methods often ignore global context cues capturing the
interactions among different object instances, and can only recognize a handful
of types by exhaustively training individual detectors for all possible
relationships. To capture such global interdependency, we propose a deep
Variation-structured Reinforcement Learning (VRL) framework to sequentially
discover object relationships and attributes in the whole image. First, a
directed semantic action graph is built using language priors to provide a rich
and compact representation of semantic correlations between object categories,
predicates, and attributes. Next, we use a variation-structured traversal over
the action graph to construct a small, adaptive action set for each step based
on the current state and historical actions. In particular, an ambiguity-aware
object mining scheme is used to resolve semantic ambiguity among object
categories that the object detector fails to distinguish. We then make
sequential predictions using a deep RL framework, incorporating global context
cues and semantic embeddings of previously extracted phrases in the state
vector. Our experiments on the Visual Relationship Detection (VRD) dataset and
the large-scale Visual Genome dataset validate the superiority of VRL, which
can achieve significantly better detection results on datasets involving
thousands of relationship and attribute types. We also demonstrate that VRL is
able to predict unseen types embedded in our action graph by learning
correlations on shared graph nodes.
| [
{
"version": "v1",
"created": "Wed, 8 Mar 2017 22:09:10 GMT"
}
] | 2017-03-10T00:00:00 | [
[
"Liang",
"Xiaodan",
""
],
[
"Lee",
"Lisa",
""
],
[
"Xing",
"Eric P.",
""
]
] | TITLE: Deep Variation-structured Reinforcement Learning for Visual Relationship
and Attribute Detection
ABSTRACT: Despite progress in visual perception tasks such as image classification and
detection, computers still struggle to understand the interdependency of
objects in the scene as a whole, e.g., relations between objects or their
attributes. Existing methods often ignore global context cues capturing the
interactions among different object instances, and can only recognize a handful
of types by exhaustively training individual detectors for all possible
relationships. To capture such global interdependency, we propose a deep
Variation-structured Reinforcement Learning (VRL) framework to sequentially
discover object relationships and attributes in the whole image. First, a
directed semantic action graph is built using language priors to provide a rich
and compact representation of semantic correlations between object categories,
predicates, and attributes. Next, we use a variation-structured traversal over
the action graph to construct a small, adaptive action set for each step based
on the current state and historical actions. In particular, an ambiguity-aware
object mining scheme is used to resolve semantic ambiguity among object
categories that the object detector fails to distinguish. We then make
sequential predictions using a deep RL framework, incorporating global context
cues and semantic embeddings of previously extracted phrases in the state
vector. Our experiments on the Visual Relationship Detection (VRD) dataset and
the large-scale Visual Genome dataset validate the superiority of VRL, which
can achieve significantly better detection results on datasets involving
thousands of relationship and attribute types. We also demonstrate that VRL is
able to predict unseen types embedded in our action graph by learning
correlations on shared graph nodes.
| no_new_dataset | 0.931525 |
1703.03097 | Mayank Kejriwal | Mayank Kejriwal, Pedro Szekely | Information Extraction in Illicit Domains | 10 pages, ACM WWW 2017 | null | 10.1145/3038912.3052642 | null | cs.CL cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Extracting useful entities and attribute values from illicit domains such as
human trafficking is a challenging problem with the potential for widespread
social impact. Such domains employ atypical language models, have `long tails'
and suffer from the problem of concept drift. In this paper, we propose a
lightweight, feature-agnostic Information Extraction (IE) paradigm specifically
designed for such domains. Our approach uses raw, unlabeled text from an
initial corpus, and a few (12-120) seed annotations per domain-specific
attribute, to learn robust IE models for unobserved pages and websites.
Empirically, we demonstrate that our approach can outperform feature-centric
Conditional Random Field baselines by over 18\% F-Measure on five annotated
sets of real-world human trafficking datasets in both low-supervision and
high-supervision settings. We also show that our approach is demonstrably
robust to concept drift, and can be efficiently bootstrapped even in a serial
computing environment.
| [
{
"version": "v1",
"created": "Thu, 9 Mar 2017 01:28:00 GMT"
}
] | 2017-03-10T00:00:00 | [
[
"Kejriwal",
"Mayank",
""
],
[
"Szekely",
"Pedro",
""
]
] | TITLE: Information Extraction in Illicit Domains
ABSTRACT: Extracting useful entities and attribute values from illicit domains such as
human trafficking is a challenging problem with the potential for widespread
social impact. Such domains employ atypical language models, have `long tails'
and suffer from the problem of concept drift. In this paper, we propose a
lightweight, feature-agnostic Information Extraction (IE) paradigm specifically
designed for such domains. Our approach uses raw, unlabeled text from an
initial corpus, and a few (12-120) seed annotations per domain-specific
attribute, to learn robust IE models for unobserved pages and websites.
Empirically, we demonstrate that our approach can outperform feature-centric
Conditional Random Field baselines by over 18\% F-Measure on five annotated
sets of real-world human trafficking datasets in both low-supervision and
high-supervision settings. We also show that our approach is demonstrably
robust to concept drift, and can be efficiently bootstrapped even in a serial
computing environment.
| no_new_dataset | 0.95253 |
1703.03129 | {\L}ukasz Kaiser | {\L}ukasz Kaiser and Ofir Nachum and Aurko Roy and Samy Bengio | Learning to Remember Rare Events | Conference paper accepted for ICLR'17 | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite recent advances, memory-augmented deep neural networks are still
limited when it comes to life-long and one-shot learning, especially in
remembering rare events. We present a large-scale life-long memory module for
use in deep learning. The module exploits fast nearest-neighbor algorithms for
efficiency and thus scales to large memory sizes. Except for the
nearest-neighbor query, the module is fully differentiable and trained
end-to-end with no extra supervision. It operates in a life-long manner, i.e.,
without the need to reset it during training.
Our memory module can be easily added to any part of a supervised neural
network. To show its versatility we add it to a number of networks, from simple
convolutional ones tested on image classification to deep sequence-to-sequence
and recurrent-convolutional models. In all cases, the enhanced network gains
the ability to remember and do life-long one-shot learning. Our module
remembers training examples shown many thousands of steps in the past and it
can successfully generalize from them. We set new state-of-the-art for one-shot
learning on the Omniglot dataset and demonstrate, for the first time, life-long
one-shot learning in recurrent neural networks on a large-scale machine
translation task.
| [
{
"version": "v1",
"created": "Thu, 9 Mar 2017 04:36:15 GMT"
}
] | 2017-03-10T00:00:00 | [
[
"Kaiser",
"Łukasz",
""
],
[
"Nachum",
"Ofir",
""
],
[
"Roy",
"Aurko",
""
],
[
"Bengio",
"Samy",
""
]
] | TITLE: Learning to Remember Rare Events
ABSTRACT: Despite recent advances, memory-augmented deep neural networks are still
limited when it comes to life-long and one-shot learning, especially in
remembering rare events. We present a large-scale life-long memory module for
use in deep learning. The module exploits fast nearest-neighbor algorithms for
efficiency and thus scales to large memory sizes. Except for the
nearest-neighbor query, the module is fully differentiable and trained
end-to-end with no extra supervision. It operates in a life-long manner, i.e.,
without the need to reset it during training.
Our memory module can be easily added to any part of a supervised neural
network. To show its versatility we add it to a number of networks, from simple
convolutional ones tested on image classification to deep sequence-to-sequence
and recurrent-convolutional models. In all cases, the enhanced network gains
the ability to remember and do life-long one-shot learning. Our module
remembers training examples shown many thousands of steps in the past and it
can successfully generalize from them. We set new state-of-the-art for one-shot
learning on the Omniglot dataset and demonstrate, for the first time, life-long
one-shot learning in recurrent neural networks on a large-scale machine
translation task.
| no_new_dataset | 0.946794 |
1703.03186 | Lucia Maddalena | Mario Rosario Guarracino and Lucia Maddalena | Segmenting Dermoscopic Images | 4 pages | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose an automatic algorithm, named SDI, for the segmentation of skin
lesions in dermoscopic images, articulated into three main steps: selection of
the image ROI, selection of the segmentation band, and segmentation. We present
extensive experimental results achieved by the SDI algorithm on the lesion
segmentation dataset made available for the ISIC 2017 challenge on Skin Lesion
Analysis Towards Melanoma Detection, highlighting its advantages and
disadvantages.
| [
{
"version": "v1",
"created": "Thu, 9 Mar 2017 09:14:40 GMT"
}
] | 2017-03-10T00:00:00 | [
[
"Guarracino",
"Mario Rosario",
""
],
[
"Maddalena",
"Lucia",
""
]
] | TITLE: Segmenting Dermoscopic Images
ABSTRACT: We propose an automatic algorithm, named SDI, for the segmentation of skin
lesions in dermoscopic images, articulated into three main steps: selection of
the image ROI, selection of the segmentation band, and segmentation. We present
extensive experimental results achieved by the SDI algorithm on the lesion
segmentation dataset made available for the ISIC 2017 challenge on Skin Lesion
Analysis Towards Melanoma Detection, highlighting its advantages and
disadvantages.
| no_new_dataset | 0.943608 |
1703.03225 | Zhe Chen | Sai Xie, Zhe Chen | Anomaly Detection and Redundancy Elimination of Big Sensor Data in
Internet of Things | null | null | null | null | cs.DC cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the era of big data and Internet of things, massive sensor data are
gathered with Internet of things. Quantity of data captured by sensor networks
are considered to contain highly useful and valuable information. However, for
a variety of reasons, received sensor data often appear abnormal. Therefore,
effective anomaly detection methods are required to guarantee the quality of
data collected by those sensor nodes. Since sensor data are usually correlated
in time and space, not all the gathered data are valuable for further data
processing and analysis. Preprocessing is necessary for eliminating the
redundancy in gathered massive sensor data. In this paper, the proposed work
defines a sensor data preprocessing framework. It is mainly composed of two
parts, i.e., sensor data anomaly detection and sensor data redundancy
elimination. In the first part, methods based on principal statistic analysis
and Bayesian network is proposed for sensor data anomaly detection. Then,
approaches based on static Bayesian network (SBN) and dynamic Bayesian networks
(DBNs) are proposed for sensor data redundancy elimination. Static sensor data
redundancy detection algorithm (SSDRDA) for eliminating redundant data in
static datasets and real-time sensor data redundancy detection algorithm
(RSDRDA) for eliminating redundant sensor data in real-time are proposed. The
efficiency and effectiveness of the proposed methods are validated using
real-world gathered sensor datasets.
| [
{
"version": "v1",
"created": "Thu, 9 Mar 2017 10:49:52 GMT"
}
] | 2017-03-10T00:00:00 | [
[
"Xie",
"Sai",
""
],
[
"Chen",
"Zhe",
""
]
] | TITLE: Anomaly Detection and Redundancy Elimination of Big Sensor Data in
Internet of Things
ABSTRACT: In the era of big data and Internet of things, massive sensor data are
gathered with Internet of things. Quantity of data captured by sensor networks
are considered to contain highly useful and valuable information. However, for
a variety of reasons, received sensor data often appear abnormal. Therefore,
effective anomaly detection methods are required to guarantee the quality of
data collected by those sensor nodes. Since sensor data are usually correlated
in time and space, not all the gathered data are valuable for further data
processing and analysis. Preprocessing is necessary for eliminating the
redundancy in gathered massive sensor data. In this paper, the proposed work
defines a sensor data preprocessing framework. It is mainly composed of two
parts, i.e., sensor data anomaly detection and sensor data redundancy
elimination. In the first part, methods based on principal statistic analysis
and Bayesian network is proposed for sensor data anomaly detection. Then,
approaches based on static Bayesian network (SBN) and dynamic Bayesian networks
(DBNs) are proposed for sensor data redundancy elimination. Static sensor data
redundancy detection algorithm (SSDRDA) for eliminating redundant data in
static datasets and real-time sensor data redundancy detection algorithm
(RSDRDA) for eliminating redundant sensor data in real-time are proposed. The
efficiency and effectiveness of the proposed methods are validated using
real-world gathered sensor datasets.
| no_new_dataset | 0.953579 |
1703.03305 | Umut G\"u\c{c}l\"u | Umut G\"u\c{c}l\"u, Ya\u{g}mur G\"u\c{c}l\"ut\"urk, Meysam Madadi,
Sergio Escalera, Xavier Bar\'o, Jordi Gonz\'alez, Rob van Lier, Marcel A. J.
van Gerven | End-to-end semantic face segmentation with conditional random fields as
convolutional, recurrent and adversarial networks | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent years have seen a sharp increase in the number of related yet distinct
advances in semantic segmentation. Here, we tackle this problem by leveraging
the respective strengths of these advances. That is, we formulate a conditional
random field over a four-connected graph as end-to-end trainable convolutional
and recurrent networks, and estimate them via an adversarial process.
Importantly, our model learns not only unary potentials but also pairwise
potentials, while aggregating multi-scale contexts and controlling higher-order
inconsistencies. We evaluate our model on two standard benchmark datasets for
semantic face segmentation, achieving state-of-the-art results on both of them.
| [
{
"version": "v1",
"created": "Thu, 9 Mar 2017 15:48:22 GMT"
}
] | 2017-03-10T00:00:00 | [
[
"Güçlü",
"Umut",
""
],
[
"Güçlütürk",
"Yağmur",
""
],
[
"Madadi",
"Meysam",
""
],
[
"Escalera",
"Sergio",
""
],
[
"Baró",
"Xavier",
""
],
[
"González",
"Jordi",
""
],
[
"van Lier",
"Rob",
""
],
[
"van Gerven",
"Marcel A. J.",
""
]
] | TITLE: End-to-end semantic face segmentation with conditional random fields as
convolutional, recurrent and adversarial networks
ABSTRACT: Recent years have seen a sharp increase in the number of related yet distinct
advances in semantic segmentation. Here, we tackle this problem by leveraging
the respective strengths of these advances. That is, we formulate a conditional
random field over a four-connected graph as end-to-end trainable convolutional
and recurrent networks, and estimate them via an adversarial process.
Importantly, our model learns not only unary potentials but also pairwise
potentials, while aggregating multi-scale contexts and controlling higher-order
inconsistencies. We evaluate our model on two standard benchmark datasets for
semantic face segmentation, achieving state-of-the-art results on both of them.
| no_new_dataset | 0.954393 |
1703.03401 | Chandra Mouli S | S Chandra Mouli, Abhishek Naik, Bruno Ribeiro, Jennifer Neville | Identifying User Survival Types via Clustering of Censored Social
Network Data | null | null | null | null | cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The goal of cluster analysis in survival data is to identify clusters that
are decidedly associated with the survival outcome. Previous research has
explored this problem primarily in the medical domain with relatively small
datasets, but the need for such a clustering methodology could arise in other
domains with large datasets, such as social networks. Concretely, we wish to
identify different survival classes in a social network by clustering the users
based on their lifespan in the network. In this paper, we propose a decision
tree based algorithm that uses a global normalization of $p$-values to identify
clusters with significantly different survival distributions. We evaluate the
clusters from our model with the help of a simple survival prediction task and
show that our model outperforms other competing methods.
| [
{
"version": "v1",
"created": "Thu, 9 Mar 2017 18:58:26 GMT"
}
] | 2017-03-10T00:00:00 | [
[
"Mouli",
"S Chandra",
""
],
[
"Naik",
"Abhishek",
""
],
[
"Ribeiro",
"Bruno",
""
],
[
"Neville",
"Jennifer",
""
]
] | TITLE: Identifying User Survival Types via Clustering of Censored Social
Network Data
ABSTRACT: The goal of cluster analysis in survival data is to identify clusters that
are decidedly associated with the survival outcome. Previous research has
explored this problem primarily in the medical domain with relatively small
datasets, but the need for such a clustering methodology could arise in other
domains with large datasets, such as social networks. Concretely, we wish to
identify different survival classes in a social network by clustering the users
based on their lifespan in the network. In this paper, we propose a decision
tree based algorithm that uses a global normalization of $p$-values to identify
clusters with significantly different survival distributions. We evaluate the
clusters from our model with the help of a simple survival prediction task and
show that our model outperforms other competing methods.
| no_new_dataset | 0.949153 |
1603.09732 | Radu Horaud P | Vincent Drouard, Radu Horaud, Antoine Deleforge, Sil\`eye Ba and
Georgios Evangelidis | Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear
Regressions | 12 pages, 5 figures, 3 tables | IEEE Transactions on Image Processing, volume 26, Issue 3,
1428-1440, 2017 | 10.1109/TIP.2017.2654165 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Head-pose estimation has many applications, such as social event analysis,
human-robot and human-computer interaction, driving assistance, and so forth.
Head-pose estimation is challenging because it must cope with changing
illumination conditions, variabilities in face orientation and in appearance,
partial occlusions of facial landmarks, as well as bounding-box-to-face
alignment errors. We propose tu use a mixture of linear regressions with
partially-latent output. This regression method learns to map high-dimensional
feature vectors (extracted from bounding boxes of faces) onto the joint space
of head-pose angles and bounding-box shifts, such that they are robustly
predicted in the presence of unobservable phenomena. We describe in detail the
mapping method that combines the merits of unsupervised manifold learning
techniques and of mixtures of regressions. We validate our method with three
publicly available datasets and we thoroughly benchmark four variants of the
proposed algorithm with several state-of-the-art head-pose estimation methods.
| [
{
"version": "v1",
"created": "Thu, 31 Mar 2016 19:32:52 GMT"
},
{
"version": "v2",
"created": "Fri, 15 Apr 2016 09:10:36 GMT"
},
{
"version": "v3",
"created": "Mon, 6 Mar 2017 11:18:47 GMT"
}
] | 2017-03-09T00:00:00 | [
[
"Drouard",
"Vincent",
""
],
[
"Horaud",
"Radu",
""
],
[
"Deleforge",
"Antoine",
""
],
[
"Ba",
"Silèye",
""
],
[
"Evangelidis",
"Georgios",
""
]
] | TITLE: Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear
Regressions
ABSTRACT: Head-pose estimation has many applications, such as social event analysis,
human-robot and human-computer interaction, driving assistance, and so forth.
Head-pose estimation is challenging because it must cope with changing
illumination conditions, variabilities in face orientation and in appearance,
partial occlusions of facial landmarks, as well as bounding-box-to-face
alignment errors. We propose tu use a mixture of linear regressions with
partially-latent output. This regression method learns to map high-dimensional
feature vectors (extracted from bounding boxes of faces) onto the joint space
of head-pose angles and bounding-box shifts, such that they are robustly
predicted in the presence of unobservable phenomena. We describe in detail the
mapping method that combines the merits of unsupervised manifold learning
techniques and of mixtures of regressions. We validate our method with three
publicly available datasets and we thoroughly benchmark four variants of the
proposed algorithm with several state-of-the-art head-pose estimation methods.
| no_new_dataset | 0.943712 |
1606.06364 | Lovenoor Aulck | Lovenoor Aulck and Nishant Velagapudi and Joshua Blumenstock and Jevin
West | Predicting Student Dropout in Higher Education | Presented at 2016 ICML Workshop on #Data4Good: Machine Learning in
Social Good Applications, New York, NY | null | null | null | stat.ML cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Each year, roughly 30% of first-year students at US baccalaureate
institutions do not return for their second year and over $9 billion is spent
educating these students. Yet, little quantitative research has analyzed the
causes and possible remedies for student attrition. Here, we describe initial
efforts to model student dropout using the largest known dataset on higher
education attrition, which tracks over 32,500 students' demographics and
transcript records at one of the nation's largest public universities. Our
results highlight several early indicators of student attrition and show that
dropout can be accurately predicted even when predictions are based on a single
term of academic transcript data. These results highlight the potential for
machine learning to have an impact on student retention and success while
pointing to several promising directions for future work.
| [
{
"version": "v1",
"created": "Mon, 20 Jun 2016 23:41:19 GMT"
},
{
"version": "v2",
"created": "Thu, 30 Jun 2016 00:50:55 GMT"
},
{
"version": "v3",
"created": "Thu, 28 Jul 2016 21:41:47 GMT"
},
{
"version": "v4",
"created": "Tue, 7 Mar 2017 22:50:28 GMT"
}
] | 2017-03-09T00:00:00 | [
[
"Aulck",
"Lovenoor",
""
],
[
"Velagapudi",
"Nishant",
""
],
[
"Blumenstock",
"Joshua",
""
],
[
"West",
"Jevin",
""
]
] | TITLE: Predicting Student Dropout in Higher Education
ABSTRACT: Each year, roughly 30% of first-year students at US baccalaureate
institutions do not return for their second year and over $9 billion is spent
educating these students. Yet, little quantitative research has analyzed the
causes and possible remedies for student attrition. Here, we describe initial
efforts to model student dropout using the largest known dataset on higher
education attrition, which tracks over 32,500 students' demographics and
transcript records at one of the nation's largest public universities. Our
results highlight several early indicators of student attrition and show that
dropout can be accurately predicted even when predictions are based on a single
term of academic transcript data. These results highlight the potential for
machine learning to have an impact on student retention and success while
pointing to several promising directions for future work.
| no_new_dataset | 0.940188 |
1606.08140 | Dat Quoc Nguyen | Dat Quoc Nguyen, Kairit Sirts, Lizhen Qu and Mark Johnson | STransE: a novel embedding model of entities and relationships in
knowledge bases | V1: In Proceedings of the 2016 Conference of the North American
Chapter of the Association for Computational Linguistics: Human Language
Technologies, NAACL HLT 2016. V2: Corrected citation to (Krompa{\ss} et al.,
2015). V3: A revised version of our NAACL-HLT 2016 paper with additional
experimental results and latest related work | null | 10.18653/v1/N16-1054 | null | cs.CL cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge bases of real-world facts about entities and their relationships
are useful resources for a variety of natural language processing tasks.
However, because knowledge bases are typically incomplete, it is useful to be
able to perform link prediction or knowledge base completion, i.e., predict
whether a relationship not in the knowledge base is likely to be true. This
paper combines insights from several previous link prediction models into a new
embedding model STransE that represents each entity as a low-dimensional
vector, and each relation by two matrices and a translation vector. STransE is
a simple combination of the SE and TransE models, but it obtains better link
prediction performance on two benchmark datasets than previous embedding
models. Thus, STransE can serve as a new baseline for the more complex models
in the link prediction task.
| [
{
"version": "v1",
"created": "Mon, 27 Jun 2016 06:50:10 GMT"
},
{
"version": "v2",
"created": "Thu, 21 Jul 2016 16:24:49 GMT"
},
{
"version": "v3",
"created": "Wed, 8 Mar 2017 16:57:40 GMT"
}
] | 2017-03-09T00:00:00 | [
[
"Nguyen",
"Dat Quoc",
""
],
[
"Sirts",
"Kairit",
""
],
[
"Qu",
"Lizhen",
""
],
[
"Johnson",
"Mark",
""
]
] | TITLE: STransE: a novel embedding model of entities and relationships in
knowledge bases
ABSTRACT: Knowledge bases of real-world facts about entities and their relationships
are useful resources for a variety of natural language processing tasks.
However, because knowledge bases are typically incomplete, it is useful to be
able to perform link prediction or knowledge base completion, i.e., predict
whether a relationship not in the knowledge base is likely to be true. This
paper combines insights from several previous link prediction models into a new
embedding model STransE that represents each entity as a low-dimensional
vector, and each relation by two matrices and a translation vector. STransE is
a simple combination of the SE and TransE models, but it obtains better link
prediction performance on two benchmark datasets than previous embedding
models. Thus, STransE can serve as a new baseline for the more complex models
in the link prediction task.
| no_new_dataset | 0.94743 |
1609.03396 | Priyadarshini Panda | Priyadarshini Panda, Aayush Ankit, Parami Wijesinghe, and Kaushik Roy | FALCON: Feature Driven Selective Classification for Energy-Efficient
Image Recognition | 13 pages, 13 figures, Accepted for publication in IEEE TCAD, 2017 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Machine-learning algorithms have shown outstanding image recognition or
classification performance for computer vision applications. However, the
compute and energy requirement for implementing such classifier models for
large-scale problems is quite high. In this paper, we propose Feature Driven
Selective Classification (FALCON) inspired by the biological visual attention
mechanism in the brain to optimize the energy-efficiency of machine-learning
classifiers. We use the consensus in the characteristic features
(color/texture) across images in a dataset to decompose the original
classification problem and construct a tree of classifiers (nodes) with a
generic-to-specific transition in the classification hierarchy. The initial
nodes of the tree separate the instances based on feature information and
selectively enable the latter nodes to perform object specific classification.
The proposed methodology allows selective activation of only those branches and
nodes of the classification tree that are relevant to the input while keeping
the remaining nodes idle. Additionally, we propose a programmable and scalable
Neuromorphic Engine (NeuE) that utilizes arrays of specialized neural
computational elements to execute the FALCON based classifier models for
diverse datasets. The structure of FALCON facilitates the reuse of nodes while
scaling up from small classification problems to larger ones thus allowing us
to construct classifier implementations that are significantly more efficient.
We evaluate our approach for a 12-object classification task on the Caltech101
dataset and 10-object task on CIFAR-10 dataset by constructing FALCON models on
the NeuE platform in 45nm technology. Our results demonstrate significant
improvement in energy-efficiency and training time for minimal loss in output
quality.
| [
{
"version": "v1",
"created": "Mon, 12 Sep 2016 13:40:13 GMT"
},
{
"version": "v2",
"created": "Wed, 8 Mar 2017 15:16:19 GMT"
}
] | 2017-03-09T00:00:00 | [
[
"Panda",
"Priyadarshini",
""
],
[
"Ankit",
"Aayush",
""
],
[
"Wijesinghe",
"Parami",
""
],
[
"Roy",
"Kaushik",
""
]
] | TITLE: FALCON: Feature Driven Selective Classification for Energy-Efficient
Image Recognition
ABSTRACT: Machine-learning algorithms have shown outstanding image recognition or
classification performance for computer vision applications. However, the
compute and energy requirement for implementing such classifier models for
large-scale problems is quite high. In this paper, we propose Feature Driven
Selective Classification (FALCON) inspired by the biological visual attention
mechanism in the brain to optimize the energy-efficiency of machine-learning
classifiers. We use the consensus in the characteristic features
(color/texture) across images in a dataset to decompose the original
classification problem and construct a tree of classifiers (nodes) with a
generic-to-specific transition in the classification hierarchy. The initial
nodes of the tree separate the instances based on feature information and
selectively enable the latter nodes to perform object specific classification.
The proposed methodology allows selective activation of only those branches and
nodes of the classification tree that are relevant to the input while keeping
the remaining nodes idle. Additionally, we propose a programmable and scalable
Neuromorphic Engine (NeuE) that utilizes arrays of specialized neural
computational elements to execute the FALCON based classifier models for
diverse datasets. The structure of FALCON facilitates the reuse of nodes while
scaling up from small classification problems to larger ones thus allowing us
to construct classifier implementations that are significantly more efficient.
We evaluate our approach for a 12-object classification task on the Caltech101
dataset and 10-object task on CIFAR-10 dataset by constructing FALCON models on
the NeuE platform in 45nm technology. Our results demonstrate significant
improvement in energy-efficiency and training time for minimal loss in output
quality.
| no_new_dataset | 0.95452 |
1703.02570 | Amina Mollaysa | Amina Mollaysa, Pablo Strasser, Alexandros Kalousis | Regularising Non-linear Models Using Feature Side-information | 11 page with appendix | null | null | null | cs.LG stat.ML | http://creativecommons.org/licenses/by/4.0/ | Very often features come with their own vectorial descriptions which provide
detailed information about their properties. We refer to these vectorial
descriptions as feature side-information. In the standard learning scenario,
input is represented as a vector of features and the feature side-information
is most often ignored or used only for feature selection prior to model
fitting. We believe that feature side-information which carries information
about features intrinsic property will help improve model prediction if used in
a proper way during learning process. In this paper, we propose a framework
that allows for the incorporation of the feature side-information during the
learning of very general model families to improve the prediction performance.
We control the structures of the learned models so that they reflect features
similarities as these are defined on the basis of the side-information. We
perform experiments on a number of benchmark datasets which show significant
predictive performance gains, over a number of baselines, as a result of the
exploitation of the side-information.
| [
{
"version": "v1",
"created": "Tue, 7 Mar 2017 19:47:22 GMT"
}
] | 2017-03-09T00:00:00 | [
[
"Mollaysa",
"Amina",
""
],
[
"Strasser",
"Pablo",
""
],
[
"Kalousis",
"Alexandros",
""
]
] | TITLE: Regularising Non-linear Models Using Feature Side-information
ABSTRACT: Very often features come with their own vectorial descriptions which provide
detailed information about their properties. We refer to these vectorial
descriptions as feature side-information. In the standard learning scenario,
input is represented as a vector of features and the feature side-information
is most often ignored or used only for feature selection prior to model
fitting. We believe that feature side-information which carries information
about features intrinsic property will help improve model prediction if used in
a proper way during learning process. In this paper, we propose a framework
that allows for the incorporation of the feature side-information during the
learning of very general model families to improve the prediction performance.
We control the structures of the learned models so that they reflect features
similarities as these are defined on the basis of the side-information. We
perform experiments on a number of benchmark datasets which show significant
predictive performance gains, over a number of baselines, as a result of the
exploitation of the side-information.
| no_new_dataset | 0.949576 |
1703.02577 | Md Momin Al Aziz | Md Nazmus Sadat, Md Momin Al Aziz, Noman Mohammed, Feng Chen, Shuang
Wang, Xiaoqian Jiang | SAFETY: Secure gwAs in Federated Environment Through a hYbrid solution
with Intel SGX and Homomorphic Encryption | Hybrid Cryptosystem using SGX and Homomorphic Encryption | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent studies demonstrate that effective healthcare can benefit from using
the human genomic information. For instance, analysis of tumor genomes has
revealed 140 genes whose mutations contribute to cancer. As a result, many
institutions are using statistical analysis of genomic data, which are mostly
based on genome-wide association studies (GWAS). GWAS analyze genome sequence
variations in order to identify genetic risk factors for diseases. These
studies often require pooling data from different sources together in order to
unravel statistical patterns or relationships between genetic variants and
diseases. In this case, the primary challenge is to fulfill one major
objective: accessing multiple genomic data repositories for collaborative
research in a privacy-preserving manner. Due to the sensitivity and privacy
concerns regarding the genomic data, multi-jurisdictional laws and policies of
cross-border genomic data sharing are enforced among different regions of the
world. In this article, we present SAFETY, a hybrid framework, which can
securely perform GWAS on federated genomic datasets using homomorphic
encryption and recently introduced secure hardware component of Intel Software
Guard Extensions (Intel SGX) to ensure high efficiency and privacy at the same
time. Different experimental settings show the efficacy and applicability of
such hybrid framework in secure conduction of GWAS. To the best of our
knowledge, this hybrid use of homomorphic encryption along with Intel SGX is
not proposed or experimented to this date. Our proposed framework, SAFETY is up
to 4.82 times faster than the best existing secure computation technique.
| [
{
"version": "v1",
"created": "Tue, 7 Mar 2017 20:21:53 GMT"
}
] | 2017-03-09T00:00:00 | [
[
"Sadat",
"Md Nazmus",
""
],
[
"Aziz",
"Md Momin Al",
""
],
[
"Mohammed",
"Noman",
""
],
[
"Chen",
"Feng",
""
],
[
"Wang",
"Shuang",
""
],
[
"Jiang",
"Xiaoqian",
""
]
] | TITLE: SAFETY: Secure gwAs in Federated Environment Through a hYbrid solution
with Intel SGX and Homomorphic Encryption
ABSTRACT: Recent studies demonstrate that effective healthcare can benefit from using
the human genomic information. For instance, analysis of tumor genomes has
revealed 140 genes whose mutations contribute to cancer. As a result, many
institutions are using statistical analysis of genomic data, which are mostly
based on genome-wide association studies (GWAS). GWAS analyze genome sequence
variations in order to identify genetic risk factors for diseases. These
studies often require pooling data from different sources together in order to
unravel statistical patterns or relationships between genetic variants and
diseases. In this case, the primary challenge is to fulfill one major
objective: accessing multiple genomic data repositories for collaborative
research in a privacy-preserving manner. Due to the sensitivity and privacy
concerns regarding the genomic data, multi-jurisdictional laws and policies of
cross-border genomic data sharing are enforced among different regions of the
world. In this article, we present SAFETY, a hybrid framework, which can
securely perform GWAS on federated genomic datasets using homomorphic
encryption and recently introduced secure hardware component of Intel Software
Guard Extensions (Intel SGX) to ensure high efficiency and privacy at the same
time. Different experimental settings show the efficacy and applicability of
such hybrid framework in secure conduction of GWAS. To the best of our
knowledge, this hybrid use of homomorphic encryption along with Intel SGX is
not proposed or experimented to this date. Our proposed framework, SAFETY is up
to 4.82 times faster than the best existing secure computation technique.
| no_new_dataset | 0.93784 |
1703.02638 | Fabio Porto | Fabio Porto, Amir Khatibi, Jo\~ao R. Nobre, Eduardo Ogasawara, Patrick
Valduriez, Dennis Shasha | Constellation Queries over Big Data | null | null | null | null | cs.DB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A geometrical pattern is a set of points with all pairwise distances (or,
more generally, relative distances) specified. Finding matches to such patterns
has applications to spatial data in seismic, astronomical, and transportation
contexts. For example, a particularly interesting geometric pattern in
astronomy is the Einstein cross, which is an astronomical phenomenon in which a
single quasar is observed as four distinct sky objects (due to gravitational
lensing) when captured by earth telescopes. Finding such crosses, as well as
other geometric patterns, is a challenging problem as the potential number of
sets of elements that compose shapes is exponentially large in the size of the
dataset and the pattern. In this paper, we denote geometric patterns as
constellation queries and propose algorithms to find them in large data
applications. Our methods combine quadtrees, matrix multiplication, and
unindexed join processing to discover sets of points that match a geometric
pattern within some additive factor on the pairwise distances. Our distributed
experiments show that the choice of composition algorithm (matrix
multiplication or nested loops) depends on the freedom introduced in the query
geometry through the distance additive factor. Three clearly identified blocks
of threshold values guide the choice of the best composition algorithm.
Finally, solving the problem for relative distances requires a novel
continuous-to-discrete transformation. To the best of our knowledge this paper
is the first to investigate constellation queries at scale.
| [
{
"version": "v1",
"created": "Tue, 7 Mar 2017 23:45:46 GMT"
}
] | 2017-03-09T00:00:00 | [
[
"Porto",
"Fabio",
""
],
[
"Khatibi",
"Amir",
""
],
[
"Nobre",
"João R.",
""
],
[
"Ogasawara",
"Eduardo",
""
],
[
"Valduriez",
"Patrick",
""
],
[
"Shasha",
"Dennis",
""
]
] | TITLE: Constellation Queries over Big Data
ABSTRACT: A geometrical pattern is a set of points with all pairwise distances (or,
more generally, relative distances) specified. Finding matches to such patterns
has applications to spatial data in seismic, astronomical, and transportation
contexts. For example, a particularly interesting geometric pattern in
astronomy is the Einstein cross, which is an astronomical phenomenon in which a
single quasar is observed as four distinct sky objects (due to gravitational
lensing) when captured by earth telescopes. Finding such crosses, as well as
other geometric patterns, is a challenging problem as the potential number of
sets of elements that compose shapes is exponentially large in the size of the
dataset and the pattern. In this paper, we denote geometric patterns as
constellation queries and propose algorithms to find them in large data
applications. Our methods combine quadtrees, matrix multiplication, and
unindexed join processing to discover sets of points that match a geometric
pattern within some additive factor on the pairwise distances. Our distributed
experiments show that the choice of composition algorithm (matrix
multiplication or nested loops) depends on the freedom introduced in the query
geometry through the distance additive factor. Three clearly identified blocks
of threshold values guide the choice of the best composition algorithm.
Finally, solving the problem for relative distances requires a novel
continuous-to-discrete transformation. To the best of our knowledge this paper
is the first to investigate constellation queries at scale.
| no_new_dataset | 0.950503 |
1703.02690 | Erik Lindgren | Erik M. Lindgren, Shanshan Wu, Alexandros G. Dimakis | Leveraging Sparsity for Efficient Submodular Data Summarization | In NIPS 2016 | null | null | null | stat.ML cs.DS cs.IT cs.LG math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The facility location problem is widely used for summarizing large datasets
and has additional applications in sensor placement, image retrieval, and
clustering. One difficulty of this problem is that submodular optimization
algorithms require the calculation of pairwise benefits for all items in the
dataset. This is infeasible for large problems, so recent work proposed to only
calculate nearest neighbor benefits. One limitation is that several strong
assumptions were invoked to obtain provable approximation guarantees. In this
paper we establish that these extra assumptions are not necessary---solving the
sparsified problem will be almost optimal under the standard assumptions of the
problem. We then analyze a different method of sparsification that is a better
model for methods such as Locality Sensitive Hashing to accelerate the nearest
neighbor computations and extend the use of the problem to a broader family of
similarities. We validate our approach by demonstrating that it rapidly
generates interpretable summaries.
| [
{
"version": "v1",
"created": "Wed, 8 Mar 2017 03:56:27 GMT"
}
] | 2017-03-09T00:00:00 | [
[
"Lindgren",
"Erik M.",
""
],
[
"Wu",
"Shanshan",
""
],
[
"Dimakis",
"Alexandros G.",
""
]
] | TITLE: Leveraging Sparsity for Efficient Submodular Data Summarization
ABSTRACT: The facility location problem is widely used for summarizing large datasets
and has additional applications in sensor placement, image retrieval, and
clustering. One difficulty of this problem is that submodular optimization
algorithms require the calculation of pairwise benefits for all items in the
dataset. This is infeasible for large problems, so recent work proposed to only
calculate nearest neighbor benefits. One limitation is that several strong
assumptions were invoked to obtain provable approximation guarantees. In this
paper we establish that these extra assumptions are not necessary---solving the
sparsified problem will be almost optimal under the standard assumptions of the
problem. We then analyze a different method of sparsification that is a better
model for methods such as Locality Sensitive Hashing to accelerate the nearest
neighbor computations and extend the use of the problem to a broader family of
similarities. We validate our approach by demonstrating that it rapidly
generates interpretable summaries.
| no_new_dataset | 0.94545 |
1703.02716 | Yuanjun Xiong | Yuanjun Xiong, Yue Zhao, Limin Wang, Dahua Lin, Xiaoou Tang | A Pursuit of Temporal Accuracy in General Activity Detection | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Detecting activities in untrimmed videos is an important but challenging
task. The performance of existing methods remains unsatisfactory, e.g., they
often meet difficulties in locating the beginning and end of a long complex
action. In this paper, we propose a generic framework that can accurately
detect a wide variety of activities from untrimmed videos. Our first
contribution is a novel proposal scheme that can efficiently generate
candidates with accurate temporal boundaries. The other contribution is a
cascaded classification pipeline that explicitly distinguishes between
relevance and completeness of a candidate instance. On two challenging temporal
activity detection datasets, THUMOS14 and ActivityNet, the proposed framework
significantly outperforms the existing state-of-the-art methods, demonstrating
superior accuracy and strong adaptivity in handling activities with various
temporal structures.
| [
{
"version": "v1",
"created": "Wed, 8 Mar 2017 05:52:52 GMT"
}
] | 2017-03-09T00:00:00 | [
[
"Xiong",
"Yuanjun",
""
],
[
"Zhao",
"Yue",
""
],
[
"Wang",
"Limin",
""
],
[
"Lin",
"Dahua",
""
],
[
"Tang",
"Xiaoou",
""
]
] | TITLE: A Pursuit of Temporal Accuracy in General Activity Detection
ABSTRACT: Detecting activities in untrimmed videos is an important but challenging
task. The performance of existing methods remains unsatisfactory, e.g., they
often meet difficulties in locating the beginning and end of a long complex
action. In this paper, we propose a generic framework that can accurately
detect a wide variety of activities from untrimmed videos. Our first
contribution is a novel proposal scheme that can efficiently generate
candidates with accurate temporal boundaries. The other contribution is a
cascaded classification pipeline that explicitly distinguishes between
relevance and completeness of a candidate instance. On two challenging temporal
activity detection datasets, THUMOS14 and ActivityNet, the proposed framework
significantly outperforms the existing state-of-the-art methods, demonstrating
superior accuracy and strong adaptivity in handling activities with various
temporal structures.
| no_new_dataset | 0.949342 |
1703.02719 | Chao Peng | Chao Peng and Xiangyu Zhang and Gang Yu and Guiming Luo and Jian Sun | Large Kernel Matters -- Improve Semantic Segmentation by Global
Convolutional Network | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of recent trends [30, 31, 14] in network architec- ture design is
stacking small filters (e.g., 1x1 or 3x3) in the entire network because the
stacked small filters is more ef- ficient than a large kernel, given the same
computational complexity. However, in the field of semantic segmenta- tion,
where we need to perform dense per-pixel prediction, we find that the large
kernel (and effective receptive field) plays an important role when we have to
perform the clas- sification and localization tasks simultaneously. Following
our design principle, we propose a Global Convolutional Network to address both
the classification and localization issues for the semantic segmentation. We
also suggest a residual-based boundary refinement to further refine the ob-
ject boundaries. Our approach achieves state-of-art perfor- mance on two public
benchmarks and significantly outper- forms previous results, 82.2% (vs 80.2%)
on PASCAL VOC 2012 dataset and 76.9% (vs 71.8%) on Cityscapes dataset.
| [
{
"version": "v1",
"created": "Wed, 8 Mar 2017 06:14:55 GMT"
}
] | 2017-03-09T00:00:00 | [
[
"Peng",
"Chao",
""
],
[
"Zhang",
"Xiangyu",
""
],
[
"Yu",
"Gang",
""
],
[
"Luo",
"Guiming",
""
],
[
"Sun",
"Jian",
""
]
] | TITLE: Large Kernel Matters -- Improve Semantic Segmentation by Global
Convolutional Network
ABSTRACT: One of recent trends [30, 31, 14] in network architec- ture design is
stacking small filters (e.g., 1x1 or 3x3) in the entire network because the
stacked small filters is more ef- ficient than a large kernel, given the same
computational complexity. However, in the field of semantic segmenta- tion,
where we need to perform dense per-pixel prediction, we find that the large
kernel (and effective receptive field) plays an important role when we have to
perform the clas- sification and localization tasks simultaneously. Following
our design principle, we propose a Global Convolutional Network to address both
the classification and localization issues for the semantic segmentation. We
also suggest a residual-based boundary refinement to further refine the ob-
ject boundaries. Our approach achieves state-of-art perfor- mance on two public
benchmarks and significantly outper- forms previous results, 82.2% (vs 80.2%)
on PASCAL VOC 2012 dataset and 76.9% (vs 71.8%) on Cityscapes dataset.
| no_new_dataset | 0.952706 |
1703.02723 | Rajiv Khanna | Rajiv Khanna, Ethan Elenberg, Alexandros G. Dimakis, Sahand Negahban,
Joydeep Ghosh | Scalable Greedy Feature Selection via Weak Submodularity | To appear in AISTATS 2017 | null | null | null | stat.ML cs.IT cs.LG math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Greedy algorithms are widely used for problems in machine learning such as
feature selection and set function optimization. Unfortunately, for large
datasets, the running time of even greedy algorithms can be quite high. This is
because for each greedy step we need to refit a model or calculate a function
using the previously selected choices and the new candidate.
Two algorithms that are faster approximations to the greedy forward selection
were introduced recently ([Mirzasoleiman et al. 2013, 2015]). They achieve
better performance by exploiting distributed computation and stochastic
evaluation respectively. Both algorithms have provable performance guarantees
for submodular functions.
In this paper we show that divergent from previously held opinion,
submodularity is not required to obtain approximation guarantees for these two
algorithms. Specifically, we show that a generalized concept of weak
submodularity suffices to give multiplicative approximation guarantees. Our
result extends the applicability of these algorithms to a larger class of
functions. Furthermore, we show that a bounded submodularity ratio can be used
to provide data dependent bounds that can sometimes be tighter also for
submodular functions. We empirically validate our work by showing superior
performance of fast greedy approximations versus several established baselines
on artificial and real datasets.
| [
{
"version": "v1",
"created": "Wed, 8 Mar 2017 06:21:46 GMT"
}
] | 2017-03-09T00:00:00 | [
[
"Khanna",
"Rajiv",
""
],
[
"Elenberg",
"Ethan",
""
],
[
"Dimakis",
"Alexandros G.",
""
],
[
"Negahban",
"Sahand",
""
],
[
"Ghosh",
"Joydeep",
""
]
] | TITLE: Scalable Greedy Feature Selection via Weak Submodularity
ABSTRACT: Greedy algorithms are widely used for problems in machine learning such as
feature selection and set function optimization. Unfortunately, for large
datasets, the running time of even greedy algorithms can be quite high. This is
because for each greedy step we need to refit a model or calculate a function
using the previously selected choices and the new candidate.
Two algorithms that are faster approximations to the greedy forward selection
were introduced recently ([Mirzasoleiman et al. 2013, 2015]). They achieve
better performance by exploiting distributed computation and stochastic
evaluation respectively. Both algorithms have provable performance guarantees
for submodular functions.
In this paper we show that divergent from previously held opinion,
submodularity is not required to obtain approximation guarantees for these two
algorithms. Specifically, we show that a generalized concept of weak
submodularity suffices to give multiplicative approximation guarantees. Our
result extends the applicability of these algorithms to a larger class of
functions. Furthermore, we show that a bounded submodularity ratio can be used
to provide data dependent bounds that can sometimes be tighter also for
submodular functions. We empirically validate our work by showing superior
performance of fast greedy approximations versus several established baselines
on artificial and real datasets.
| no_new_dataset | 0.943348 |
1703.02852 | Yulong Pei | Wouter Ligtenberg and Yulong Pei | Introduction to a Temporal Graph Benchmark | null | null | null | null | cs.SI physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A temporal graph is a data structure, consisting of nodes and edges in which
the edges are associated with time labels. To analyze the temporal graph, the
first step is to find a proper graph dataset/benchmark. While many temporal
graph datasets exist online, none could be found that used the interval labels
in which each edge is associated with a starting and ending time. Therefore we
create a temporal graph data based on Wikipedia reference graph for temporal
analysis. This report aims to provide more details of this graph benchmark to
those who are interested in using it.
| [
{
"version": "v1",
"created": "Mon, 20 Feb 2017 20:19:32 GMT"
}
] | 2017-03-09T00:00:00 | [
[
"Ligtenberg",
"Wouter",
""
],
[
"Pei",
"Yulong",
""
]
] | TITLE: Introduction to a Temporal Graph Benchmark
ABSTRACT: A temporal graph is a data structure, consisting of nodes and edges in which
the edges are associated with time labels. To analyze the temporal graph, the
first step is to find a proper graph dataset/benchmark. While many temporal
graph datasets exist online, none could be found that used the interval labels
in which each edge is associated with a starting and ending time. Therefore we
create a temporal graph data based on Wikipedia reference graph for temporal
analysis. This report aims to provide more details of this graph benchmark to
those who are interested in using it.
| no_new_dataset | 0.748444 |
1703.02883 | Hadi Zare | Kayvan Bijari, Hadi Zare, Hadi Veisi, Hossein Bobarshad | Memory Enriched Big Bang Big Crunch Optimization Algorithm for Data
Clustering | 17 pages, 3 figures, 8 tables | Neural Comput & Applic (2016) | 10.1007/s00521-016-2528-9 | null | cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cluster analysis plays an important role in decision making process for many
knowledge-based systems. There exist a wide variety of different approaches for
clustering applications including the heuristic techniques, probabilistic
models, and traditional hierarchical algorithms. In this paper, a novel
heuristic approach based on big bang-big crunch algorithm is proposed for
clustering problems. The proposed method not only takes advantage of heuristic
nature to alleviate typical clustering algorithms such as k-means, but it also
benefits from the memory based scheme as compared to its similar heuristic
techniques. Furthermore, the performance of the proposed algorithm is
investigated based on several benchmark test functions as well as on the
well-known datasets. The experimental results show the significant superiority
of the proposed method over the similar algorithms.
| [
{
"version": "v1",
"created": "Wed, 8 Mar 2017 15:50:35 GMT"
}
] | 2017-03-09T00:00:00 | [
[
"Bijari",
"Kayvan",
""
],
[
"Zare",
"Hadi",
""
],
[
"Veisi",
"Hadi",
""
],
[
"Bobarshad",
"Hossein",
""
]
] | TITLE: Memory Enriched Big Bang Big Crunch Optimization Algorithm for Data
Clustering
ABSTRACT: Cluster analysis plays an important role in decision making process for many
knowledge-based systems. There exist a wide variety of different approaches for
clustering applications including the heuristic techniques, probabilistic
models, and traditional hierarchical algorithms. In this paper, a novel
heuristic approach based on big bang-big crunch algorithm is proposed for
clustering problems. The proposed method not only takes advantage of heuristic
nature to alleviate typical clustering algorithms such as k-means, but it also
benefits from the memory based scheme as compared to its similar heuristic
techniques. Furthermore, the performance of the proposed algorithm is
investigated based on several benchmark test functions as well as on the
well-known datasets. The experimental results show the significant superiority
of the proposed method over the similar algorithms.
| no_new_dataset | 0.953232 |
1703.02910 | Yarin Gal | Yarin Gal and Riashat Islam and Zoubin Ghahramani | Deep Bayesian Active Learning with Image Data | null | null | null | null | cs.LG cs.CV stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Even though active learning forms an important pillar of machine learning,
deep learning tools are not prevalent within it. Deep learning poses several
difficulties when used in an active learning setting. First, active learning
(AL) methods generally rely on being able to learn and update models from small
amounts of data. Recent advances in deep learning, on the other hand, are
notorious for their dependence on large amounts of data. Second, many AL
acquisition functions rely on model uncertainty, yet deep learning methods
rarely represent such model uncertainty. In this paper we combine recent
advances in Bayesian deep learning into the active learning framework in a
practical way. We develop an active learning framework for high dimensional
data, a task which has been extremely challenging so far, with very sparse
existing literature. Taking advantage of specialised models such as Bayesian
convolutional neural networks, we demonstrate our active learning techniques
with image data, obtaining a significant improvement on existing active
learning approaches. We demonstrate this on both the MNIST dataset, as well as
for skin cancer diagnosis from lesion images (ISIC2016 task).
| [
{
"version": "v1",
"created": "Wed, 8 Mar 2017 16:53:57 GMT"
}
] | 2017-03-09T00:00:00 | [
[
"Gal",
"Yarin",
""
],
[
"Islam",
"Riashat",
""
],
[
"Ghahramani",
"Zoubin",
""
]
] | TITLE: Deep Bayesian Active Learning with Image Data
ABSTRACT: Even though active learning forms an important pillar of machine learning,
deep learning tools are not prevalent within it. Deep learning poses several
difficulties when used in an active learning setting. First, active learning
(AL) methods generally rely on being able to learn and update models from small
amounts of data. Recent advances in deep learning, on the other hand, are
notorious for their dependence on large amounts of data. Second, many AL
acquisition functions rely on model uncertainty, yet deep learning methods
rarely represent such model uncertainty. In this paper we combine recent
advances in Bayesian deep learning into the active learning framework in a
practical way. We develop an active learning framework for high dimensional
data, a task which has been extremely challenging so far, with very sparse
existing literature. Taking advantage of specialised models such as Bayesian
convolutional neural networks, we demonstrate our active learning techniques
with image data, obtaining a significant improvement on existing active
learning approaches. We demonstrate this on both the MNIST dataset, as well as
for skin cancer diagnosis from lesion images (ISIC2016 task).
| no_new_dataset | 0.950227 |
1703.02931 | Guido Borghi | Guido Borghi, Roberto Vezzani, Rita Cucchiara | Fast Gesture Recognition with Multiple Stream Discrete HMMs on 3D
Skeletons | Accepted in ICPR 2016 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | HMMs are widely used in action and gesture recognition due to their
implementation simplicity, low computational requirement, scalability and high
parallelism. They have worth performance even with a limited training set. All
these characteristics are hard to find together in other even more accurate
methods. In this paper, we propose a novel double-stage classification
approach, based on Multiple Stream Discrete Hidden Markov Models (MSD-HMM) and
3D skeleton joint data, able to reach high performances maintaining all
advantages listed above. The approach allows both to quickly classify
pre-segmented gestures (offline classification), and to perform temporal
segmentation on streams of gestures (online classification) faster than real
time. We test our system on three public datasets, MSRAction3D, UTKinect-Action
and MSRDailyAction, and on a new dataset, Kinteract Dataset, explicitly created
for Human Computer Interaction (HCI). We obtain state of the art performances
on all of them.
| [
{
"version": "v1",
"created": "Wed, 8 Mar 2017 17:37:13 GMT"
}
] | 2017-03-09T00:00:00 | [
[
"Borghi",
"Guido",
""
],
[
"Vezzani",
"Roberto",
""
],
[
"Cucchiara",
"Rita",
""
]
] | TITLE: Fast Gesture Recognition with Multiple Stream Discrete HMMs on 3D
Skeletons
ABSTRACT: HMMs are widely used in action and gesture recognition due to their
implementation simplicity, low computational requirement, scalability and high
parallelism. They have worth performance even with a limited training set. All
these characteristics are hard to find together in other even more accurate
methods. In this paper, we propose a novel double-stage classification
approach, based on Multiple Stream Discrete Hidden Markov Models (MSD-HMM) and
3D skeleton joint data, able to reach high performances maintaining all
advantages listed above. The approach allows both to quickly classify
pre-segmented gestures (offline classification), and to perform temporal
segmentation on streams of gestures (online classification) faster than real
time. We test our system on three public datasets, MSRAction3D, UTKinect-Action
and MSRDailyAction, and on a new dataset, Kinteract Dataset, explicitly created
for Human Computer Interaction (HCI). We obtain state of the art performances
on all of them.
| new_dataset | 0.958577 |
1506.09174 | Jongpil Kim | Jongpil Kim and Vladimir Pavlovic | Discovering Characteristic Landmarks on Ancient Coins using
Convolutional Networks | null | null | 10.1117/1.JEI.26.1.011018 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose a novel method to find characteristic landmarks on
ancient Roman imperial coins using deep convolutional neural network models
(CNNs). We formulate an optimization problem to discover class-specific regions
while guaranteeing specific controlled loss of accuracy. Analysis on
visualization of the discovered region confirms that not only can the proposed
method successfully find a set of characteristic regions per class, but also
the discovered region is consistent with human expert annotations. We also
propose a new framework to recognize the Roman coins which exploits
hierarchical structure of the ancient Roman coins using the state-of-the-art
classification power of the CNNs adopted to a new task of coin classification.
Experimental results show that the proposed framework is able to effectively
recognize the ancient Roman coins. For this research, we have collected a new
Roman coin dataset where all coins are annotated and consist of observe (head)
and reverse (tail) images.
| [
{
"version": "v1",
"created": "Tue, 30 Jun 2015 17:41:12 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Jul 2015 01:10:13 GMT"
}
] | 2017-03-08T00:00:00 | [
[
"Kim",
"Jongpil",
""
],
[
"Pavlovic",
"Vladimir",
""
]
] | TITLE: Discovering Characteristic Landmarks on Ancient Coins using
Convolutional Networks
ABSTRACT: In this paper, we propose a novel method to find characteristic landmarks on
ancient Roman imperial coins using deep convolutional neural network models
(CNNs). We formulate an optimization problem to discover class-specific regions
while guaranteeing specific controlled loss of accuracy. Analysis on
visualization of the discovered region confirms that not only can the proposed
method successfully find a set of characteristic regions per class, but also
the discovered region is consistent with human expert annotations. We also
propose a new framework to recognize the Roman coins which exploits
hierarchical structure of the ancient Roman coins using the state-of-the-art
classification power of the CNNs adopted to a new task of coin classification.
Experimental results show that the proposed framework is able to effectively
recognize the ancient Roman coins. For this research, we have collected a new
Roman coin dataset where all coins are annotated and consist of observe (head)
and reverse (tail) images.
| new_dataset | 0.953535 |
1605.07079 | Aaron Klein | Aaron Klein, Stefan Falkner, Simon Bartels, Philipp Hennig, Frank
Hutter | Fast Bayesian Optimization of Machine Learning Hyperparameters on Large
Datasets | null | null | null | null | cs.LG cs.AI stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bayesian optimization has become a successful tool for hyperparameter
optimization of machine learning algorithms, such as support vector machines or
deep neural networks. Despite its success, for large datasets, training and
validating a single configuration often takes hours, days, or even weeks, which
limits the achievable performance. To accelerate hyperparameter optimization,
we propose a generative model for the validation error as a function of
training set size, which is learned during the optimization process and allows
exploration of preliminary configurations on small subsets, by extrapolating to
the full dataset. We construct a Bayesian optimization procedure, dubbed
Fabolas, which models loss and training time as a function of dataset size and
automatically trades off high information gain about the global optimum against
computational cost. Experiments optimizing support vector machines and deep
neural networks show that Fabolas often finds high-quality solutions 10 to 100
times faster than other state-of-the-art Bayesian optimization methods or the
recently proposed bandit strategy Hyperband.
| [
{
"version": "v1",
"created": "Mon, 23 May 2016 16:29:51 GMT"
},
{
"version": "v2",
"created": "Tue, 7 Mar 2017 14:48:54 GMT"
}
] | 2017-03-08T00:00:00 | [
[
"Klein",
"Aaron",
""
],
[
"Falkner",
"Stefan",
""
],
[
"Bartels",
"Simon",
""
],
[
"Hennig",
"Philipp",
""
],
[
"Hutter",
"Frank",
""
]
] | TITLE: Fast Bayesian Optimization of Machine Learning Hyperparameters on Large
Datasets
ABSTRACT: Bayesian optimization has become a successful tool for hyperparameter
optimization of machine learning algorithms, such as support vector machines or
deep neural networks. Despite its success, for large datasets, training and
validating a single configuration often takes hours, days, or even weeks, which
limits the achievable performance. To accelerate hyperparameter optimization,
we propose a generative model for the validation error as a function of
training set size, which is learned during the optimization process and allows
exploration of preliminary configurations on small subsets, by extrapolating to
the full dataset. We construct a Bayesian optimization procedure, dubbed
Fabolas, which models loss and training time as a function of dataset size and
automatically trades off high information gain about the global optimum against
computational cost. Experiments optimizing support vector machines and deep
neural networks show that Fabolas often finds high-quality solutions 10 to 100
times faster than other state-of-the-art Bayesian optimization methods or the
recently proposed bandit strategy Hyperband.
| no_new_dataset | 0.951097 |
1606.08168 | Toshinori Mori | Toshinori Mori (for the MEG Collaboration) | Final Results of the MEG Experiment | 8 pages, 7 figures, 1 table; invited contribution to Les Rencontres
de Physique de la Vall\'ee d'Aoste, La Thuile, March 6-12, 2016 | null | 10.1393/ncc/i2016-16325-7 | null | hep-ex physics.ins-det | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Transitions of charged leptons from one generation to another are basically
prohibited in the Standard Model because of the mysteriously tiny neutrino
masses, although such flavor-violating transitions have been long observed for
quarks and neutrinos. Supersymmetric Grand Unified Theories (SUSY GUT), which
unify quarks and leptons as well as their forces, predict that charged leptons
should also make such transitions at small but experimentally observable rates.
The MEG experiment was the first to have explored one of such transitions, mu+
-> e+ gamma decays, down to the branching ratios predicted by SUSY GUT. Here we
report the final results of the MEG experiment based on the full dataset
collected from 2009 to 2013 at the Paul Scherrer Institut, corresponding to a
total of 7.5 x 10^14 stopped muons on target. No excess for mu+ -> e+ gamma
decays was found. Thus the most stringent upper bound was placed on the
branching ratio, B(mu+ -> e+ gamma) < 4.2 x 10^-13 at 90% C.L., about 30 times
tighter than previous experiments, and severely constrains SUSY GUT and other
well-motivated theories. We are now preparing the upgraded experiment MEG II
with an aim to achieve a sensitivity of 4 x 10^-14 after three years of data
taking. It is expected to start late in 2017.
| [
{
"version": "v1",
"created": "Mon, 27 Jun 2016 09:12:21 GMT"
}
] | 2017-03-08T00:00:00 | [
[
"Mori",
"Toshinori",
"",
"for the MEG Collaboration"
]
] | TITLE: Final Results of the MEG Experiment
ABSTRACT: Transitions of charged leptons from one generation to another are basically
prohibited in the Standard Model because of the mysteriously tiny neutrino
masses, although such flavor-violating transitions have been long observed for
quarks and neutrinos. Supersymmetric Grand Unified Theories (SUSY GUT), which
unify quarks and leptons as well as their forces, predict that charged leptons
should also make such transitions at small but experimentally observable rates.
The MEG experiment was the first to have explored one of such transitions, mu+
-> e+ gamma decays, down to the branching ratios predicted by SUSY GUT. Here we
report the final results of the MEG experiment based on the full dataset
collected from 2009 to 2013 at the Paul Scherrer Institut, corresponding to a
total of 7.5 x 10^14 stopped muons on target. No excess for mu+ -> e+ gamma
decays was found. Thus the most stringent upper bound was placed on the
branching ratio, B(mu+ -> e+ gamma) < 4.2 x 10^-13 at 90% C.L., about 30 times
tighter than previous experiments, and severely constrains SUSY GUT and other
well-motivated theories. We are now preparing the upgraded experiment MEG II
with an aim to achieve a sensitivity of 4 x 10^-14 after three years of data
taking. It is expected to start late in 2017.
| no_new_dataset | 0.944177 |
1608.00775 | Michele Volpi Michele Volpi | Michele Volpi, Devis Tuia | Dense semantic labeling of sub-decimeter resolution images with
convolutional neural networks | Accepted in IEEE Transactions on Geoscience and Remote Sensing, 2016 | null | 10.1109/TGRS.2016.2616585 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Semantic labeling (or pixel-level land-cover classification) in ultra-high
resolution imagery (< 10cm) requires statistical models able to learn high
level concepts from spatial data, with large appearance variations.
Convolutional Neural Networks (CNNs) achieve this goal by learning
discriminatively a hierarchy of representations of increasing abstraction.
In this paper we present a CNN-based system relying on an
downsample-then-upsample architecture. Specifically, it first learns a rough
spatial map of high-level representations by means of convolutions and then
learns to upsample them back to the original resolution by deconvolutions. By
doing so, the CNN learns to densely label every pixel at the original
resolution of the image. This results in many advantages, including i)
state-of-the-art numerical accuracy, ii) improved geometric accuracy of
predictions and iii) high efficiency at inference time.
We test the proposed system on the Vaihingen and Potsdam sub-decimeter
resolution datasets, involving semantic labeling of aerial images of 9cm and
5cm resolution, respectively. These datasets are composed by many large and
fully annotated tiles allowing an unbiased evaluation of models making use of
spatial information. We do so by comparing two standard CNN architectures to
the proposed one: standard patch classification, prediction of local label
patches by employing only convolutions and full patch labeling by employing
deconvolutions. All the systems compare favorably or outperform a
state-of-the-art baseline relying on superpixels and powerful appearance
descriptors. The proposed full patch labeling CNN outperforms these models by a
large margin, also showing a very appealing inference time.
| [
{
"version": "v1",
"created": "Tue, 2 Aug 2016 11:33:44 GMT"
},
{
"version": "v2",
"created": "Mon, 10 Oct 2016 15:07:33 GMT"
}
] | 2017-03-08T00:00:00 | [
[
"Volpi",
"Michele",
""
],
[
"Tuia",
"Devis",
""
]
] | TITLE: Dense semantic labeling of sub-decimeter resolution images with
convolutional neural networks
ABSTRACT: Semantic labeling (or pixel-level land-cover classification) in ultra-high
resolution imagery (< 10cm) requires statistical models able to learn high
level concepts from spatial data, with large appearance variations.
Convolutional Neural Networks (CNNs) achieve this goal by learning
discriminatively a hierarchy of representations of increasing abstraction.
In this paper we present a CNN-based system relying on an
downsample-then-upsample architecture. Specifically, it first learns a rough
spatial map of high-level representations by means of convolutions and then
learns to upsample them back to the original resolution by deconvolutions. By
doing so, the CNN learns to densely label every pixel at the original
resolution of the image. This results in many advantages, including i)
state-of-the-art numerical accuracy, ii) improved geometric accuracy of
predictions and iii) high efficiency at inference time.
We test the proposed system on the Vaihingen and Potsdam sub-decimeter
resolution datasets, involving semantic labeling of aerial images of 9cm and
5cm resolution, respectively. These datasets are composed by many large and
fully annotated tiles allowing an unbiased evaluation of models making use of
spatial information. We do so by comparing two standard CNN architectures to
the proposed one: standard patch classification, prediction of local label
patches by employing only convolutions and full patch labeling by employing
deconvolutions. All the systems compare favorably or outperform a
state-of-the-art baseline relying on superpixels and powerful appearance
descriptors. The proposed full patch labeling CNN outperforms these models by a
large margin, also showing a very appealing inference time.
| no_new_dataset | 0.952086 |
1609.07257 | Tomas Pevny | Tomas Pevny and Petr Somol | Using Neural Network Formalism to Solve Multiple-Instance Problems | Accepted to International Symposium on Neural Networks | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many objects in the real world are difficult to describe by a single
numerical vector of a fixed length, whereas describing them by a set of vectors
is more natural. Therefore, Multiple instance learning (MIL) techniques have
been constantly gaining on importance throughout last years. MIL formalism
represents each object (sample) by a set (bag) of feature vectors (instances)
of fixed length where knowledge about objects (e.g., class label) is available
on bag level but not necessarily on instance level. Many standard tools
including supervised classifiers have been already adapted to MIL setting since
the problem got formalized in late nineties. In this work we propose a neural
network (NN) based formalism that intuitively bridges the gap between MIL
problem definition and the vast existing knowledge-base of standard models and
classifiers. We show that the proposed NN formalism is effectively optimizable
by a modified back-propagation algorithm and can reveal unknown patterns inside
bags. Comparison to eight types of classifiers from the prior art on a set of
14 publicly available benchmark datasets confirms the advantages and accuracy
of the proposed solution.
| [
{
"version": "v1",
"created": "Fri, 23 Sep 2016 07:40:12 GMT"
},
{
"version": "v2",
"created": "Sun, 16 Oct 2016 11:15:43 GMT"
},
{
"version": "v3",
"created": "Tue, 7 Mar 2017 06:38:36 GMT"
}
] | 2017-03-08T00:00:00 | [
[
"Pevny",
"Tomas",
""
],
[
"Somol",
"Petr",
""
]
] | TITLE: Using Neural Network Formalism to Solve Multiple-Instance Problems
ABSTRACT: Many objects in the real world are difficult to describe by a single
numerical vector of a fixed length, whereas describing them by a set of vectors
is more natural. Therefore, Multiple instance learning (MIL) techniques have
been constantly gaining on importance throughout last years. MIL formalism
represents each object (sample) by a set (bag) of feature vectors (instances)
of fixed length where knowledge about objects (e.g., class label) is available
on bag level but not necessarily on instance level. Many standard tools
including supervised classifiers have been already adapted to MIL setting since
the problem got formalized in late nineties. In this work we propose a neural
network (NN) based formalism that intuitively bridges the gap between MIL
problem definition and the vast existing knowledge-base of standard models and
classifiers. We show that the proposed NN formalism is effectively optimizable
by a modified back-propagation algorithm and can reveal unknown patterns inside
bags. Comparison to eight types of classifiers from the prior art on a set of
14 publicly available benchmark datasets confirms the advantages and accuracy
of the proposed solution.
| no_new_dataset | 0.949248 |
1703.01599 | Barnab\'e Monnot | Barnab\'e Monnot, Francisco Benita, Georgios Piliouras | How bad is selfish routing in practice? | 19 pages, 7 figures | null | null | null | cs.GT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Routing games are one of the most successful domains of application of game
theory. It is well understood that simple dynamics converge to equilibria,
whose performance is nearly optimal regardless of the size of the network or
the number of agents. These strong theoretical assertions prompt a natural
question: How well do these pen-and-paper calculations agree with the reality
of everyday traffic routing? We focus on a semantically rich dataset from
Singapore's National Science Experiment that captures detailed information
about the daily behavior of thousands of Singaporean students. Using this
dataset, we can identify the routes as well as the modes of transportation used
by the students, e.g. car (driving or being driven to school) versus bus or
metro, estimate source and sink destinations (home-school) and trip duration,
as well as their mode-dependent available routes. We quantify both the system
and individual optimality. Our estimate of the Empirical Price of Anarchy lies
between 1.11 and 1.22. Individually, the typical behavior is consistent from
day to day and nearly optimal, with low regret for not deviating to alternative
paths.
| [
{
"version": "v1",
"created": "Sun, 5 Mar 2017 14:28:53 GMT"
},
{
"version": "v2",
"created": "Tue, 7 Mar 2017 13:05:15 GMT"
}
] | 2017-03-08T00:00:00 | [
[
"Monnot",
"Barnabé",
""
],
[
"Benita",
"Francisco",
""
],
[
"Piliouras",
"Georgios",
""
]
] | TITLE: How bad is selfish routing in practice?
ABSTRACT: Routing games are one of the most successful domains of application of game
theory. It is well understood that simple dynamics converge to equilibria,
whose performance is nearly optimal regardless of the size of the network or
the number of agents. These strong theoretical assertions prompt a natural
question: How well do these pen-and-paper calculations agree with the reality
of everyday traffic routing? We focus on a semantically rich dataset from
Singapore's National Science Experiment that captures detailed information
about the daily behavior of thousands of Singaporean students. Using this
dataset, we can identify the routes as well as the modes of transportation used
by the students, e.g. car (driving or being driven to school) versus bus or
metro, estimate source and sink destinations (home-school) and trip duration,
as well as their mode-dependent available routes. We quantify both the system
and individual optimality. Our estimate of the Empirical Price of Anarchy lies
between 1.11 and 1.22. Individually, the typical behavior is consistent from
day to day and nearly optimal, with low regret for not deviating to alternative
paths.
| no_new_dataset | 0.920754 |
1703.02036 | Jakob Wasserthal | Jakob Wasserthal, Peter F. Neher, Fabian Isensee, Klaus H. Maier-Hein | Direct White Matter Bundle Segmentation using Stacked U-Nets | null | null | null | null | cs.CV q-bio.NC q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The state-of-the-art method for automatically segmenting white matter bundles
in diffusion-weighted MRI is tractography in conjunction with streamline
cluster selection. This process involves long chains of processing steps which
are not only computationally expensive but also complex to setup and tedious
with respect to quality control. Direct bundle segmentation methods treat the
task as a traditional image segmentation problem. While they so far did not
deliver competitive results, they can potentially mitigate many of the
mentioned issues. We present a novel supervised approach for direct tract
segmentation that shows major performance gains. It builds upon a stacked U-Net
architecture which is trained on manual bundle segmentations from Human
Connectome Project subjects. We evaluate our approach \textit{in vivo} as well
as \textit{in silico} using the ISMRM 2015 Tractography Challenge phantom
dataset. We achieve human segmentation performance and a major performance gain
over previous pipelines. We show how the learned spatial priors efficiently
guide the segmentation even at lower image qualities with little quality loss.
| [
{
"version": "v1",
"created": "Mon, 6 Mar 2017 14:21:49 GMT"
}
] | 2017-03-08T00:00:00 | [
[
"Wasserthal",
"Jakob",
""
],
[
"Neher",
"Peter F.",
""
],
[
"Isensee",
"Fabian",
""
],
[
"Maier-Hein",
"Klaus H.",
""
]
] | TITLE: Direct White Matter Bundle Segmentation using Stacked U-Nets
ABSTRACT: The state-of-the-art method for automatically segmenting white matter bundles
in diffusion-weighted MRI is tractography in conjunction with streamline
cluster selection. This process involves long chains of processing steps which
are not only computationally expensive but also complex to setup and tedious
with respect to quality control. Direct bundle segmentation methods treat the
task as a traditional image segmentation problem. While they so far did not
deliver competitive results, they can potentially mitigate many of the
mentioned issues. We present a novel supervised approach for direct tract
segmentation that shows major performance gains. It builds upon a stacked U-Net
architecture which is trained on manual bundle segmentations from Human
Connectome Project subjects. We evaluate our approach \textit{in vivo} as well
as \textit{in silico} using the ISMRM 2015 Tractography Challenge phantom
dataset. We achieve human segmentation performance and a major performance gain
over previous pipelines. We show how the learned spatial priors efficiently
guide the segmentation even at lower image qualities with little quality loss.
| no_new_dataset | 0.949435 |
1703.02212 | Md Saiful Islam | Mehdi Naseriparsa, Md. Saiful Islam, Chengfei Liu and Irene Moser | No-But-Semantic-Match: Computing Semantically Matched XML Keyword Search
Results | 24 pages, 21 figures, 6 tables, submitted to The VLDB Journal for
possible publication | null | null | null | cs.DB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Users are rarely familiar with the content of a data source they are
querying, and therefore cannot avoid using keywords that do not exist in the
data source. Traditional systems may respond with an empty result, causing
dissatisfaction, while the data source in effect holds semantically related
content. In this paper we study this no-but-semantic-match problem on XML
keyword search and propose a solution which enables us to present the top-k
semantically related results to the user. Our solution involves two steps: (a)
extracting semantically related candidate queries from the original query and
(b) processing candidate queries and retrieving the top-k semantically related
results. Candidate queries are generated by replacement of non-mapped keywords
with candidate keywords obtained from an ontological knowledge base. Candidate
results are scored using their cohesiveness and their similarity to the
original query. Since the number of queries to process can be large, with each
result having to be analyzed, we propose pruning techniques to retrieve the
top-$k$ results efficiently. We develop two query processing algorithms based
on our pruning techniques. Further, we exploit a property of the candidate
queries to propose a technique for processing multiple queries in batch, which
improves the performance substantially. Extensive experiments on two real
datasets verify the effectiveness and efficiency of the proposed approaches.
| [
{
"version": "v1",
"created": "Tue, 7 Mar 2017 04:54:44 GMT"
}
] | 2017-03-08T00:00:00 | [
[
"Naseriparsa",
"Mehdi",
""
],
[
"Islam",
"Md. Saiful",
""
],
[
"Liu",
"Chengfei",
""
],
[
"Moser",
"Irene",
""
]
] | TITLE: No-But-Semantic-Match: Computing Semantically Matched XML Keyword Search
Results
ABSTRACT: Users are rarely familiar with the content of a data source they are
querying, and therefore cannot avoid using keywords that do not exist in the
data source. Traditional systems may respond with an empty result, causing
dissatisfaction, while the data source in effect holds semantically related
content. In this paper we study this no-but-semantic-match problem on XML
keyword search and propose a solution which enables us to present the top-k
semantically related results to the user. Our solution involves two steps: (a)
extracting semantically related candidate queries from the original query and
(b) processing candidate queries and retrieving the top-k semantically related
results. Candidate queries are generated by replacement of non-mapped keywords
with candidate keywords obtained from an ontological knowledge base. Candidate
results are scored using their cohesiveness and their similarity to the
original query. Since the number of queries to process can be large, with each
result having to be analyzed, we propose pruning techniques to retrieve the
top-$k$ results efficiently. We develop two query processing algorithms based
on our pruning techniques. Further, we exploit a property of the candidate
queries to propose a technique for processing multiple queries in batch, which
improves the performance substantially. Extensive experiments on two real
datasets verify the effectiveness and efficiency of the proposed approaches.
| no_new_dataset | 0.951504 |
1703.02244 | Ethan Rudd | Steve Cruz, Cora Coleman, Ethan M. Rudd, and Terrance E. Boult | Open Set Intrusion Recognition for Fine-Grained Attack Categorization | Pre-print of camera-ready version to appear at the IEEE Homeland
Security Technologies (HST) 2017 Conference | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Confidently distinguishing a malicious intrusion over a network is an
important challenge. Most intrusion detection system evaluations have been
performed in a closed set protocol in which only classes seen during training
are considered during classification. Thus far, there has been no realistic
application in which novel types of behaviors unseen at training -- unknown
classes as it were -- must be recognized for manual categorization. This paper
comparatively evaluates malware classification using both closed set and open
set protocols for intrusion recognition on the KDDCUP'99 dataset. In contrast
to much of the previous work, we employ a fine-grained recognition protocol, in
which the dataset is loosely open set -- i.e., recognizing individual intrusion
types -- e.g., "sendmail", "snmp guess", ..., etc., rather than more general
attack categories (e.g., "DoS","Probe","R2L","U2R","Normal"). We also employ
two different classifier types -- Gaussian RBF kernel SVMs, which are not
theoretically guaranteed to bound open space risk, and W-SVMs, which are
theoretically guaranteed to bound open space risk. We find that the W-SVM
offers superior performance under the open set regime, particularly as the cost
of misclassifying unknown classes at query time (i.e., classes not present in
the training set) increases. Results of performance tradeoff with respect to
cost of unknown as well as discussion of the ramifications of these findings in
an operational setting are presented.
| [
{
"version": "v1",
"created": "Tue, 7 Mar 2017 07:15:43 GMT"
}
] | 2017-03-08T00:00:00 | [
[
"Cruz",
"Steve",
""
],
[
"Coleman",
"Cora",
""
],
[
"Rudd",
"Ethan M.",
""
],
[
"Boult",
"Terrance E.",
""
]
] | TITLE: Open Set Intrusion Recognition for Fine-Grained Attack Categorization
ABSTRACT: Confidently distinguishing a malicious intrusion over a network is an
important challenge. Most intrusion detection system evaluations have been
performed in a closed set protocol in which only classes seen during training
are considered during classification. Thus far, there has been no realistic
application in which novel types of behaviors unseen at training -- unknown
classes as it were -- must be recognized for manual categorization. This paper
comparatively evaluates malware classification using both closed set and open
set protocols for intrusion recognition on the KDDCUP'99 dataset. In contrast
to much of the previous work, we employ a fine-grained recognition protocol, in
which the dataset is loosely open set -- i.e., recognizing individual intrusion
types -- e.g., "sendmail", "snmp guess", ..., etc., rather than more general
attack categories (e.g., "DoS","Probe","R2L","U2R","Normal"). We also employ
two different classifier types -- Gaussian RBF kernel SVMs, which are not
theoretically guaranteed to bound open space risk, and W-SVMs, which are
theoretically guaranteed to bound open space risk. We find that the W-SVM
offers superior performance under the open set regime, particularly as the cost
of misclassifying unknown classes at query time (i.e., classes not present in
the training set) increases. Results of performance tradeoff with respect to
cost of unknown as well as discussion of the ramifications of these findings in
an operational setting are presented.
| no_new_dataset | 0.956104 |
1703.02248 | Ethan Rudd | Khudran Alzhrani, Ethan M. Rudd, C. Edward Chow, and Terrance E. Boult | Automated U.S Diplomatic Cables Security Classification: Topic Model
Pruning vs. Classification Based on Clusters | Pre-print of camera-ready copy accepted to the 2017 IEEE Homeland
Security Technologies (HST) conference | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The U.S Government has been the target for cyber-attacks from all over the
world. Just recently, former President Obama accused the Russian government of
the leaking emails to Wikileaks and declared that the U.S. might be forced to
respond. While Russia denied involvement, it is clear that the U.S. has to take
some defensive measures to protect its data infrastructure. Insider threats
have been the cause of other sensitive information leaks too, including the
infamous Edward Snowden incident. Most of the recent leaks were in the form of
text. Due to the nature of text data, security classifications are assigned
manually. In an adversarial environment, insiders can leak texts through
E-mail, printers, or any untrusted channels. The optimal defense is to
automatically detect the unstructured text security class and enforce the
appropriate protection mechanism without degrading services or daily tasks.
Unfortunately, existing Data Leak Prevention (DLP) systems are not well suited
for detecting unstructured texts. In this paper, we compare two recent
approaches in the literature for text security classification, evaluating them
on actual sensitive text data from the WikiLeaks dataset.
| [
{
"version": "v1",
"created": "Tue, 7 Mar 2017 07:29:56 GMT"
}
] | 2017-03-08T00:00:00 | [
[
"Alzhrani",
"Khudran",
""
],
[
"Rudd",
"Ethan M.",
""
],
[
"Chow",
"C. Edward",
""
],
[
"Boult",
"Terrance E.",
""
]
] | TITLE: Automated U.S Diplomatic Cables Security Classification: Topic Model
Pruning vs. Classification Based on Clusters
ABSTRACT: The U.S Government has been the target for cyber-attacks from all over the
world. Just recently, former President Obama accused the Russian government of
the leaking emails to Wikileaks and declared that the U.S. might be forced to
respond. While Russia denied involvement, it is clear that the U.S. has to take
some defensive measures to protect its data infrastructure. Insider threats
have been the cause of other sensitive information leaks too, including the
infamous Edward Snowden incident. Most of the recent leaks were in the form of
text. Due to the nature of text data, security classifications are assigned
manually. In an adversarial environment, insiders can leak texts through
E-mail, printers, or any untrusted channels. The optimal defense is to
automatically detect the unstructured text security class and enforce the
appropriate protection mechanism without degrading services or daily tasks.
Unfortunately, existing Data Leak Prevention (DLP) systems are not well suited
for detecting unstructured texts. In this paper, we compare two recent
approaches in the literature for text security classification, evaluating them
on actual sensitive text data from the WikiLeaks dataset.
| no_new_dataset | 0.94801 |
1703.02344 | Krishnendu Chaudhury | Devashish Shankar, Sujay Narumanchi, H A Ananya, Pramod Kompalli,
Krishnendu Chaudhury | Deep Learning based Large Scale Visual Recommendation and Search for
E-Commerce | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we present a unified end-to-end approach to build a large
scale Visual Search and Recommendation system for e-commerce. Previous works
have targeted these problems in isolation. We believe a more effective and
elegant solution could be obtained by tackling them together. We propose a
unified Deep Convolutional Neural Network architecture, called VisNet, to learn
embeddings to capture the notion of visual similarity, across several semantic
granularities. We demonstrate the superiority of our approach for the task of
image retrieval, by comparing against the state-of-the-art on the Exact
Street2Shop dataset. We then share the design decisions and trade-offs made
while deploying the model to power Visual Recommendations across a catalog of
50M products, supporting 2K queries a second at Flipkart, India's largest
e-commerce company. The deployment of our solution has yielded a significant
business impact, as measured by the conversion-rate.
| [
{
"version": "v1",
"created": "Tue, 7 Mar 2017 11:58:36 GMT"
}
] | 2017-03-08T00:00:00 | [
[
"Shankar",
"Devashish",
""
],
[
"Narumanchi",
"Sujay",
""
],
[
"Ananya",
"H A",
""
],
[
"Kompalli",
"Pramod",
""
],
[
"Chaudhury",
"Krishnendu",
""
]
] | TITLE: Deep Learning based Large Scale Visual Recommendation and Search for
E-Commerce
ABSTRACT: In this paper, we present a unified end-to-end approach to build a large
scale Visual Search and Recommendation system for e-commerce. Previous works
have targeted these problems in isolation. We believe a more effective and
elegant solution could be obtained by tackling them together. We propose a
unified Deep Convolutional Neural Network architecture, called VisNet, to learn
embeddings to capture the notion of visual similarity, across several semantic
granularities. We demonstrate the superiority of our approach for the task of
image retrieval, by comparing against the state-of-the-art on the Exact
Street2Shop dataset. We then share the design decisions and trade-offs made
while deploying the model to power Visual Recommendations across a catalog of
50M products, supporting 2K queries a second at Flipkart, India's largest
e-commerce company. The deployment of our solution has yielded a significant
business impact, as measured by the conversion-rate.
| no_new_dataset | 0.94366 |
1703.02433 | Bilal Farooq | Isma\"il Saadi, Melvin Wong, Bilal Farooq, Jacques Teller, Mario Cools | An investigation into machine learning approaches for forecasting
spatio-temporal demand in ride-hailing service | Currently under review for journal publication | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we present machine learning approaches for characterizing and
forecasting the short-term demand for on-demand ride-hailing services. We
propose the spatio-temporal estimation of the demand that is a function of
variable effects related to traffic, pricing and weather conditions. With
respect to the methodology, a single decision tree, bootstrap-aggregated
(bagged) decision trees, random forest, boosted decision trees, and artificial
neural network for regression have been adapted and systematically compared
using various statistics, e.g. R-square, Root Mean Square Error (RMSE), and
slope. To better assess the quality of the models, they have been tested on a
real case study using the data of DiDi Chuxing, the main on-demand ride hailing
service provider in China. In the current study, 199,584 time-slots describing
the spatio-temporal ride-hailing demand has been extracted with an
aggregated-time interval of 10 mins. All the methods are trained and validated
on the basis of two independent samples from this dataset. The results revealed
that boosted decision trees provide the best prediction accuracy (RMSE=16.41),
while avoiding the risk of over-fitting, followed by artificial neural network
(20.09), random forest (23.50), bagged decision trees (24.29) and single
decision tree (33.55).
| [
{
"version": "v1",
"created": "Tue, 7 Mar 2017 15:26:38 GMT"
}
] | 2017-03-08T00:00:00 | [
[
"Saadi",
"Ismaïl",
""
],
[
"Wong",
"Melvin",
""
],
[
"Farooq",
"Bilal",
""
],
[
"Teller",
"Jacques",
""
],
[
"Cools",
"Mario",
""
]
] | TITLE: An investigation into machine learning approaches for forecasting
spatio-temporal demand in ride-hailing service
ABSTRACT: In this paper, we present machine learning approaches for characterizing and
forecasting the short-term demand for on-demand ride-hailing services. We
propose the spatio-temporal estimation of the demand that is a function of
variable effects related to traffic, pricing and weather conditions. With
respect to the methodology, a single decision tree, bootstrap-aggregated
(bagged) decision trees, random forest, boosted decision trees, and artificial
neural network for regression have been adapted and systematically compared
using various statistics, e.g. R-square, Root Mean Square Error (RMSE), and
slope. To better assess the quality of the models, they have been tested on a
real case study using the data of DiDi Chuxing, the main on-demand ride hailing
service provider in China. In the current study, 199,584 time-slots describing
the spatio-temporal ride-hailing demand has been extracted with an
aggregated-time interval of 10 mins. All the methods are trained and validated
on the basis of two independent samples from this dataset. The results revealed
that boosted decision trees provide the best prediction accuracy (RMSE=16.41),
while avoiding the risk of over-fitting, followed by artificial neural network
(20.09), random forest (23.50), bagged decision trees (24.29) and single
decision tree (33.55).
| no_new_dataset | 0.947672 |
1703.02475 | Silu Huang | Silu Huang, Liqi Xu, Jialin Liu, Aaron Elmore, Aditya Parameswaran | OrpheusDB: Bolt-on Versioning for Relational Databases | null | null | null | null | cs.DB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Data science teams often collaboratively analyze datasets, generating dataset
versions at each stage of iterative exploration and analysis. There is a
pressing need for a system that can support dataset versioning, enabling such
teams to efficiently store, track, and query across dataset versions. We
introduce OrpheusDB, a dataset version control system that "bolts on"
versioning capabilities to a traditional relational database system, thereby
gaining the analytics capabilities of the database "for free". We develop and
evaluate multiple data models for representing versioned data, as well as a
light-weight partitioning scheme, LyreSplit, to further optimize the models for
reduced query latencies. With LyreSplit, OrpheusDB is on average 1000x faster
in finding effective (and better) partitionings than competing approaches,
while also reducing the latency of version retrieval by up to 20x relative to
schemes without partitioning. LyreSplit can be applied in an online fashion as
new versions are added, alongside an intelligent migration scheme that reduces
migration time by 10x on average.
| [
{
"version": "v1",
"created": "Tue, 7 Mar 2017 17:09:13 GMT"
}
] | 2017-03-08T00:00:00 | [
[
"Huang",
"Silu",
""
],
[
"Xu",
"Liqi",
""
],
[
"Liu",
"Jialin",
""
],
[
"Elmore",
"Aaron",
""
],
[
"Parameswaran",
"Aditya",
""
]
] | TITLE: OrpheusDB: Bolt-on Versioning for Relational Databases
ABSTRACT: Data science teams often collaboratively analyze datasets, generating dataset
versions at each stage of iterative exploration and analysis. There is a
pressing need for a system that can support dataset versioning, enabling such
teams to efficiently store, track, and query across dataset versions. We
introduce OrpheusDB, a dataset version control system that "bolts on"
versioning capabilities to a traditional relational database system, thereby
gaining the analytics capabilities of the database "for free". We develop and
evaluate multiple data models for representing versioned data, as well as a
light-weight partitioning scheme, LyreSplit, to further optimize the models for
reduced query latencies. With LyreSplit, OrpheusDB is on average 1000x faster
in finding effective (and better) partitionings than competing approaches,
while also reducing the latency of version retrieval by up to 20x relative to
schemes without partitioning. LyreSplit can be applied in an online fashion as
new versions are added, alongside an intelligent migration scheme that reduces
migration time by 10x on average.
| no_new_dataset | 0.940735 |
1703.02504 | Martin Jaggi | Jan Deriu, Aurelien Lucchi, Valeria De Luca, Aliaksei Severyn, Simon
M\"uller, Mark Cieliebak, Thomas Hofmann, Martin Jaggi | Leveraging Large Amounts of Weakly Supervised Data for Multi-Language
Sentiment Classification | appearing at WWW 2017 - 26th International World Wide Web Conference | null | null | null | cs.CL cs.IR cs.LG | http://creativecommons.org/licenses/by/4.0/ | This paper presents a novel approach for multi-lingual sentiment
classification in short texts. This is a challenging task as the amount of
training data in languages other than English is very limited. Previously
proposed multi-lingual approaches typically require to establish a
correspondence to English for which powerful classifiers are already available.
In contrast, our method does not require such supervision. We leverage large
amounts of weakly-supervised data in various languages to train a multi-layer
convolutional network and demonstrate the importance of using pre-training of
such networks. We thoroughly evaluate our approach on various multi-lingual
datasets, including the recent SemEval-2016 sentiment prediction benchmark
(Task 4), where we achieved state-of-the-art performance. We also compare the
performance of our model trained individually for each language to a variant
trained for all languages at once. We show that the latter model reaches
slightly worse - but still acceptable - performance when compared to the single
language model, while benefiting from better generalization properties across
languages.
| [
{
"version": "v1",
"created": "Tue, 7 Mar 2017 18:15:57 GMT"
}
] | 2017-03-08T00:00:00 | [
[
"Deriu",
"Jan",
""
],
[
"Lucchi",
"Aurelien",
""
],
[
"De Luca",
"Valeria",
""
],
[
"Severyn",
"Aliaksei",
""
],
[
"Müller",
"Simon",
""
],
[
"Cieliebak",
"Mark",
""
],
[
"Hofmann",
"Thomas",
""
],
[
"Jaggi",
"Martin",
""
]
] | TITLE: Leveraging Large Amounts of Weakly Supervised Data for Multi-Language
Sentiment Classification
ABSTRACT: This paper presents a novel approach for multi-lingual sentiment
classification in short texts. This is a challenging task as the amount of
training data in languages other than English is very limited. Previously
proposed multi-lingual approaches typically require to establish a
correspondence to English for which powerful classifiers are already available.
In contrast, our method does not require such supervision. We leverage large
amounts of weakly-supervised data in various languages to train a multi-layer
convolutional network and demonstrate the importance of using pre-training of
such networks. We thoroughly evaluate our approach on various multi-lingual
datasets, including the recent SemEval-2016 sentiment prediction benchmark
(Task 4), where we achieved state-of-the-art performance. We also compare the
performance of our model trained individually for each language to a variant
trained for all languages at once. We show that the latter model reaches
slightly worse - but still acceptable - performance when compared to the single
language model, while benefiting from better generalization properties across
languages.
| no_new_dataset | 0.949576 |
1310.2665 | Emilio Ferrara | Emilio Ferrara, Mohsen JafariAsbagh, Onur Varol, Vahed Qazvinian,
Filippo Menczer, Alessandro Flammini | Clustering Memes in Social Media | Proceedings of the 2013 IEEE/ACM International Conference on Advances
in Social Networks Analysis and Mining (ASONAM'13), 2013 | Advances in social networks analysis and mining (ASONAM), 2013
IEEE/ACM international conference on (pp. 548-555). IEEE | 10.1145/2492517.2492530 | null | cs.SI cs.CY physics.data-an physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The increasing pervasiveness of social media creates new opportunities to
study human social behavior, while challenging our capability to analyze their
massive data streams. One of the emerging tasks is to distinguish between
different kinds of activities, for example engineered misinformation campaigns
versus spontaneous communication. Such detection problems require a formal
definition of meme, or unit of information that can spread from person to
person through the social network. Once a meme is identified, supervised
learning methods can be applied to classify different types of communication.
The appropriate granularity of a meme, however, is hardly captured from
existing entities such as tags and keywords. Here we present a framework for
the novel task of detecting memes by clustering messages from large streams of
social data. We evaluate various similarity measures that leverage content,
metadata, network features, and their combinations. We also explore the idea of
pre-clustering on the basis of existing entities. A systematic evaluation is
carried out using a manually curated dataset as ground truth. Our analysis
shows that pre-clustering and a combination of heterogeneous features yield the
best trade-off between number of clusters and their quality, demonstrating that
a simple combination based on pairwise maximization of similarity is as
effective as a non-trivial optimization of parameters. Our approach is fully
automatic, unsupervised, and scalable for real-time detection of memes in
streaming data.
| [
{
"version": "v1",
"created": "Thu, 10 Oct 2013 00:10:46 GMT"
}
] | 2017-03-07T00:00:00 | [
[
"Ferrara",
"Emilio",
""
],
[
"JafariAsbagh",
"Mohsen",
""
],
[
"Varol",
"Onur",
""
],
[
"Qazvinian",
"Vahed",
""
],
[
"Menczer",
"Filippo",
""
],
[
"Flammini",
"Alessandro",
""
]
] | TITLE: Clustering Memes in Social Media
ABSTRACT: The increasing pervasiveness of social media creates new opportunities to
study human social behavior, while challenging our capability to analyze their
massive data streams. One of the emerging tasks is to distinguish between
different kinds of activities, for example engineered misinformation campaigns
versus spontaneous communication. Such detection problems require a formal
definition of meme, or unit of information that can spread from person to
person through the social network. Once a meme is identified, supervised
learning methods can be applied to classify different types of communication.
The appropriate granularity of a meme, however, is hardly captured from
existing entities such as tags and keywords. Here we present a framework for
the novel task of detecting memes by clustering messages from large streams of
social data. We evaluate various similarity measures that leverage content,
metadata, network features, and their combinations. We also explore the idea of
pre-clustering on the basis of existing entities. A systematic evaluation is
carried out using a manually curated dataset as ground truth. Our analysis
shows that pre-clustering and a combination of heterogeneous features yield the
best trade-off between number of clusters and their quality, demonstrating that
a simple combination based on pairwise maximization of similarity is as
effective as a non-trivial optimization of parameters. Our approach is fully
automatic, unsupervised, and scalable for real-time detection of memes in
streaming data.
| new_dataset | 0.965218 |
1406.7751 | Emilio Ferrara | Emilio Ferrara, Roberto Interdonato, Andrea Tagarelli | Online Popularity and Topical Interests through the Lens of Instagram | 11 pages, 11 figures, Proceedings of ACM Hypertext 2014 | Proceedings of the 25th ACM conference on Hypertext and social
media (pp. 24-34). ACM. 2014 | 10.1145/2631775.2631808 | null | cs.SI cs.CY physics.data-an physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Online socio-technical systems can be studied as proxy of the real world to
investigate human behavior and social interactions at scale. Here we focus on
Instagram, a media-sharing online platform whose popularity has been rising up
to gathering hundred millions users. Instagram exhibits a mixture of features
including social structure, social tagging and media sharing. The network of
social interactions among users models various dynamics including
follower/followee relations and users' communication by means of
posts/comments. Users can upload and tag media such as photos and pictures, and
they can "like" and comment each piece of information on the platform. In this
work we investigate three major aspects on our Instagram dataset: (i) the
structural characteristics of its network of heterogeneous interactions, to
unveil the emergence of self organization and topically-induced community
structure; (ii) the dynamics of content production and consumption, to
understand how global trends and popular users emerge; (iii) the behavior of
users labeling media with tags, to determine how they devote their attention
and to explore the variety of their topical interests. Our analysis provides
clues to understand human behavior dynamics on socio-technical systems,
specifically users and content popularity, the mechanisms of users'
interactions in online environments and how collective trends emerge from
individuals' topical interests.
| [
{
"version": "v1",
"created": "Mon, 30 Jun 2014 14:22:39 GMT"
}
] | 2017-03-07T00:00:00 | [
[
"Ferrara",
"Emilio",
""
],
[
"Interdonato",
"Roberto",
""
],
[
"Tagarelli",
"Andrea",
""
]
] | TITLE: Online Popularity and Topical Interests through the Lens of Instagram
ABSTRACT: Online socio-technical systems can be studied as proxy of the real world to
investigate human behavior and social interactions at scale. Here we focus on
Instagram, a media-sharing online platform whose popularity has been rising up
to gathering hundred millions users. Instagram exhibits a mixture of features
including social structure, social tagging and media sharing. The network of
social interactions among users models various dynamics including
follower/followee relations and users' communication by means of
posts/comments. Users can upload and tag media such as photos and pictures, and
they can "like" and comment each piece of information on the platform. In this
work we investigate three major aspects on our Instagram dataset: (i) the
structural characteristics of its network of heterogeneous interactions, to
unveil the emergence of self organization and topically-induced community
structure; (ii) the dynamics of content production and consumption, to
understand how global trends and popular users emerge; (iii) the behavior of
users labeling media with tags, to determine how they devote their attention
and to explore the variety of their topical interests. Our analysis provides
clues to understand human behavior dynamics on socio-technical systems,
specifically users and content popularity, the mechanisms of users'
interactions in online environments and how collective trends emerge from
individuals' topical interests.
| no_new_dataset | 0.766992 |
1411.0652 | Emilio Ferrara | Mohsen JafariAsbagh, Emilio Ferrara, Onur Varol, Filippo Menczer,
Alessandro Flammini | Clustering memes in social media streams | 25 pages, 8 figures, accepted on Social Network Analysis and Mining
(SNAM). The final publication is available at Springer via
http://dx.doi.org/10.1007/s13278-014-0237-x | Social Network Analysis and Mining, 4(1), 1-13. 2014 | 10.1007/s13278-014-0237-x | null | cs.SI cs.CY cs.LG physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The problem of clustering content in social media has pervasive applications,
including the identification of discussion topics, event detection, and content
recommendation. Here we describe a streaming framework for online detection and
clustering of memes in social media, specifically Twitter. A pre-clustering
procedure, namely protomeme detection, first isolates atomic tokens of
information carried by the tweets. Protomemes are thereafter aggregated, based
on multiple similarity measures, to obtain memes as cohesive groups of tweets
reflecting actual concepts or topics of discussion. The clustering algorithm
takes into account various dimensions of the data and metadata, including
natural language, the social network, and the patterns of information
diffusion. As a result, our system can build clusters of semantically,
structurally, and topically related tweets. The clustering process is based on
a variant of Online K-means that incorporates a memory mechanism, used to
"forget" old memes and replace them over time with the new ones. The evaluation
of our framework is carried out by using a dataset of Twitter trending topics.
Over a one-week period, we systematically determined whether our algorithm was
able to recover the trending hashtags. We show that the proposed method
outperforms baseline algorithms that only use content features, as well as a
state-of-the-art event detection method that assumes full knowledge of the
underlying follower network. We finally show that our online learning framework
is flexible, due to its independence of the adopted clustering algorithm, and
best suited to work in a streaming scenario.
| [
{
"version": "v1",
"created": "Mon, 3 Nov 2014 20:41:00 GMT"
}
] | 2017-03-07T00:00:00 | [
[
"JafariAsbagh",
"Mohsen",
""
],
[
"Ferrara",
"Emilio",
""
],
[
"Varol",
"Onur",
""
],
[
"Menczer",
"Filippo",
""
],
[
"Flammini",
"Alessandro",
""
]
] | TITLE: Clustering memes in social media streams
ABSTRACT: The problem of clustering content in social media has pervasive applications,
including the identification of discussion topics, event detection, and content
recommendation. Here we describe a streaming framework for online detection and
clustering of memes in social media, specifically Twitter. A pre-clustering
procedure, namely protomeme detection, first isolates atomic tokens of
information carried by the tweets. Protomemes are thereafter aggregated, based
on multiple similarity measures, to obtain memes as cohesive groups of tweets
reflecting actual concepts or topics of discussion. The clustering algorithm
takes into account various dimensions of the data and metadata, including
natural language, the social network, and the patterns of information
diffusion. As a result, our system can build clusters of semantically,
structurally, and topically related tweets. The clustering process is based on
a variant of Online K-means that incorporates a memory mechanism, used to
"forget" old memes and replace them over time with the new ones. The evaluation
of our framework is carried out by using a dataset of Twitter trending topics.
Over a one-week period, we systematically determined whether our algorithm was
able to recover the trending hashtags. We show that the proposed method
outperforms baseline algorithms that only use content features, as well as a
state-of-the-art event detection method that assumes full knowledge of the
underlying follower network. We finally show that our online learning framework
is flexible, due to its independence of the adopted clustering algorithm, and
best suited to work in a streaming scenario.
| no_new_dataset | 0.944893 |
1509.01608 | Emilio Ferrara | Santa Agreste, Salvatore Catanese, Pasquale De Meo, Emilio Ferrara,
Giacomo Fiumara | Network Structure and Resilience of Mafia Syndicates | 22 pages, 10 figures, 1 table | Information Sciences, 351, 30-47. 2016 | 10.1016/j.ins.2016.02.027 | null | cs.SI cs.CY physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we present the results of the study of Sicilian Mafia
organization by using Social Network Analysis. The study investigates the
network structure of a Mafia organization, describing its evolution and
highlighting its plasticity to interventions targeting membership and its
resilience to disruption caused by police operations. We analyze two different
datasets about Mafia gangs built by examining different digital trails and
judicial documents spanning a period of ten years: the former dataset includes
the phone contacts among suspected individuals, the latter is constituted by
the relationships among individuals actively involved in various criminal
offenses. Our report illustrates the limits of traditional investigation
methods like tapping: criminals high up in the organization hierarchy do not
occupy the most central positions in the criminal network, and oftentimes do
not appear in the reconstructed criminal network at all. However, we also
suggest possible strategies of intervention, as we show that although criminal
networks (i.e., the network encoding mobsters and crime relationships) are
extremely resilient to different kind of attacks, contact networks (i.e., the
network reporting suspects and reciprocated phone calls) are much more
vulnerable and their analysis can yield extremely valuable insights.
| [
{
"version": "v1",
"created": "Fri, 4 Sep 2015 21:13:16 GMT"
}
] | 2017-03-07T00:00:00 | [
[
"Agreste",
"Santa",
""
],
[
"Catanese",
"Salvatore",
""
],
[
"De Meo",
"Pasquale",
""
],
[
"Ferrara",
"Emilio",
""
],
[
"Fiumara",
"Giacomo",
""
]
] | TITLE: Network Structure and Resilience of Mafia Syndicates
ABSTRACT: In this paper we present the results of the study of Sicilian Mafia
organization by using Social Network Analysis. The study investigates the
network structure of a Mafia organization, describing its evolution and
highlighting its plasticity to interventions targeting membership and its
resilience to disruption caused by police operations. We analyze two different
datasets about Mafia gangs built by examining different digital trails and
judicial documents spanning a period of ten years: the former dataset includes
the phone contacts among suspected individuals, the latter is constituted by
the relationships among individuals actively involved in various criminal
offenses. Our report illustrates the limits of traditional investigation
methods like tapping: criminals high up in the organization hierarchy do not
occupy the most central positions in the criminal network, and oftentimes do
not appear in the reconstructed criminal network at all. However, we also
suggest possible strategies of intervention, as we show that although criminal
networks (i.e., the network encoding mobsters and crime relationships) are
extremely resilient to different kind of attacks, contact networks (i.e., the
network reporting suspects and reciprocated phone calls) are much more
vulnerable and their analysis can yield extremely valuable insights.
| no_new_dataset | 0.817793 |
1510.05318 | Emilio Ferrara | Yoon-Sik Cho, Greg Ver Steeg, Emilio Ferrara, Aram Galstyan | Latent Space Model for Multi-Modal Social Data | 12 pages, 7 figures, 2 tables | Proceedings of the 25th International Conference on World Wide Web
(pp. 447-458). 2016 | 10.1145/2872427.2883031 | null | cs.SI cs.LG physics.data-an physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the emergence of social networking services, researchers enjoy the
increasing availability of large-scale heterogenous datasets capturing online
user interactions and behaviors. Traditional analysis of techno-social systems
data has focused mainly on describing either the dynamics of social
interactions, or the attributes and behaviors of the users. However,
overwhelming empirical evidence suggests that the two dimensions affect one
another, and therefore they should be jointly modeled and analyzed in a
multi-modal framework. The benefits of such an approach include the ability to
build better predictive models, leveraging social network information as well
as user behavioral signals. To this purpose, here we propose the Constrained
Latent Space Model (CLSM), a generalized framework that combines Mixed
Membership Stochastic Blockmodels (MMSB) and Latent Dirichlet Allocation (LDA)
incorporating a constraint that forces the latent space to concurrently
describe the multiple data modalities. We derive an efficient inference
algorithm based on Variational Expectation Maximization that has a
computational cost linear in the size of the network, thus making it feasible
to analyze massive social datasets. We validate the proposed framework on two
problems: prediction of social interactions from user attributes and behaviors,
and behavior prediction exploiting network information. We perform experiments
with a variety of multi-modal social systems, spanning location-based social
networks (Gowalla), social media services (Instagram, Orkut), e-commerce and
review sites (Amazon, Ciao), and finally citation networks (Cora). The results
indicate significant improvement in prediction accuracy over state of the art
methods, and demonstrate the flexibility of the proposed approach for
addressing a variety of different learning problems commonly occurring with
multi-modal social data.
| [
{
"version": "v1",
"created": "Sun, 18 Oct 2015 22:16:38 GMT"
}
] | 2017-03-07T00:00:00 | [
[
"Cho",
"Yoon-Sik",
""
],
[
"Steeg",
"Greg Ver",
""
],
[
"Ferrara",
"Emilio",
""
],
[
"Galstyan",
"Aram",
""
]
] | TITLE: Latent Space Model for Multi-Modal Social Data
ABSTRACT: With the emergence of social networking services, researchers enjoy the
increasing availability of large-scale heterogenous datasets capturing online
user interactions and behaviors. Traditional analysis of techno-social systems
data has focused mainly on describing either the dynamics of social
interactions, or the attributes and behaviors of the users. However,
overwhelming empirical evidence suggests that the two dimensions affect one
another, and therefore they should be jointly modeled and analyzed in a
multi-modal framework. The benefits of such an approach include the ability to
build better predictive models, leveraging social network information as well
as user behavioral signals. To this purpose, here we propose the Constrained
Latent Space Model (CLSM), a generalized framework that combines Mixed
Membership Stochastic Blockmodels (MMSB) and Latent Dirichlet Allocation (LDA)
incorporating a constraint that forces the latent space to concurrently
describe the multiple data modalities. We derive an efficient inference
algorithm based on Variational Expectation Maximization that has a
computational cost linear in the size of the network, thus making it feasible
to analyze massive social datasets. We validate the proposed framework on two
problems: prediction of social interactions from user attributes and behaviors,
and behavior prediction exploiting network information. We perform experiments
with a variety of multi-modal social systems, spanning location-based social
networks (Gowalla), social media services (Instagram, Orkut), e-commerce and
review sites (Amazon, Ciao), and finally citation networks (Cora). The results
indicate significant improvement in prediction accuracy over state of the art
methods, and demonstrate the flexibility of the proposed approach for
addressing a variety of different learning problems commonly occurring with
multi-modal social data.
| no_new_dataset | 0.95096 |
1604.06899 | Philipp Singer | Philipp Singer, Emilio Ferrara, Farshad Kooti, Markus Strohmaier and
Kristina Lerman | Evidence of Online Performance Deterioration in User Sessions on Reddit | Published in PlosOne | PLoS ONE 11(8): e0161636, 2016 | 10.1371/journal.pone.0161636 | null | cs.SI cs.HC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This article presents evidence of performance deterioration in online user
sessions quantified by studying a massive dataset containing over 55 million
comments posted on Reddit in April 2015. After segmenting the sessions (i.e.,
periods of activity without a prolonged break) depending on their intensity
(i.e., how many posts users produced during sessions), we observe a general
decrease in the quality of comments produced by users over the course of
sessions. We propose mixed-effects models that capture the impact of session
intensity on comments, including their length, quality, and the responses they
generate from the community. Our findings suggest performance deterioration:
Sessions of increasing intensity are associated with the production of shorter,
progressively less complex comments, which receive declining quality scores (as
rated by other users), and are less and less engaging (i.e., they attract fewer
responses). Our contribution evokes a connection between cognitive and
attention dynamics and the usage of online social peer production platforms,
specifically the effects of deterioration of user performance.
| [
{
"version": "v1",
"created": "Sat, 23 Apr 2016 12:22:24 GMT"
},
{
"version": "v2",
"created": "Fri, 26 Aug 2016 10:30:25 GMT"
}
] | 2017-03-07T00:00:00 | [
[
"Singer",
"Philipp",
""
],
[
"Ferrara",
"Emilio",
""
],
[
"Kooti",
"Farshad",
""
],
[
"Strohmaier",
"Markus",
""
],
[
"Lerman",
"Kristina",
""
]
] | TITLE: Evidence of Online Performance Deterioration in User Sessions on Reddit
ABSTRACT: This article presents evidence of performance deterioration in online user
sessions quantified by studying a massive dataset containing over 55 million
comments posted on Reddit in April 2015. After segmenting the sessions (i.e.,
periods of activity without a prolonged break) depending on their intensity
(i.e., how many posts users produced during sessions), we observe a general
decrease in the quality of comments produced by users over the course of
sessions. We propose mixed-effects models that capture the impact of session
intensity on comments, including their length, quality, and the responses they
generate from the community. Our findings suggest performance deterioration:
Sessions of increasing intensity are associated with the production of shorter,
progressively less complex comments, which receive declining quality scores (as
rated by other users), and are less and less engaging (i.e., they attract fewer
responses). Our contribution evokes a connection between cognitive and
attention dynamics and the usage of online social peer production platforms,
specifically the effects of deterioration of user performance.
| no_new_dataset | 0.950641 |
1605.00659 | Emilio Ferrara | Emilio Ferrara, Wen-Qiang Wang, Onur Varol, Alessandro Flammini, Aram
Galstyan | Predicting online extremism, content adopters, and interaction
reciprocity | 9 pages, 3 figures, 8 tables | International Conference on Social Informatics (pp. 22-39).
Springer. 2016 | 10.1007/978-3-319-47874-6_3 | null | cs.SI cs.LG physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a machine learning framework that leverages a mixture of metadata,
network, and temporal features to detect extremist users, and predict content
adopters and interaction reciprocity in social media. We exploit a unique
dataset containing millions of tweets generated by more than 25 thousand users
who have been manually identified, reported, and suspended by Twitter due to
their involvement with extremist campaigns. We also leverage millions of tweets
generated by a random sample of 25 thousand regular users who were exposed to,
or consumed, extremist content. We carry out three forecasting tasks, (i) to
detect extremist users, (ii) to estimate whether regular users will adopt
extremist content, and finally (iii) to predict whether users will reciprocate
contacts initiated by extremists. All forecasting tasks are set up in two
scenarios: a post hoc (time independent) prediction task on aggregated data,
and a simulated real-time prediction task. The performance of our framework is
extremely promising, yielding in the different forecasting scenarios up to 93%
AUC for extremist user detection, up to 80% AUC for content adoption
prediction, and finally up to 72% AUC for interaction reciprocity forecasting.
We conclude by providing a thorough feature analysis that helps determine which
are the emerging signals that provide predictive power in different scenarios.
| [
{
"version": "v1",
"created": "Mon, 2 May 2016 20:00:36 GMT"
}
] | 2017-03-07T00:00:00 | [
[
"Ferrara",
"Emilio",
""
],
[
"Wang",
"Wen-Qiang",
""
],
[
"Varol",
"Onur",
""
],
[
"Flammini",
"Alessandro",
""
],
[
"Galstyan",
"Aram",
""
]
] | TITLE: Predicting online extremism, content adopters, and interaction
reciprocity
ABSTRACT: We present a machine learning framework that leverages a mixture of metadata,
network, and temporal features to detect extremist users, and predict content
adopters and interaction reciprocity in social media. We exploit a unique
dataset containing millions of tweets generated by more than 25 thousand users
who have been manually identified, reported, and suspended by Twitter due to
their involvement with extremist campaigns. We also leverage millions of tweets
generated by a random sample of 25 thousand regular users who were exposed to,
or consumed, extremist content. We carry out three forecasting tasks, (i) to
detect extremist users, (ii) to estimate whether regular users will adopt
extremist content, and finally (iii) to predict whether users will reciprocate
contacts initiated by extremists. All forecasting tasks are set up in two
scenarios: a post hoc (time independent) prediction task on aggregated data,
and a simulated real-time prediction task. The performance of our framework is
extremely promising, yielding in the different forecasting scenarios up to 93%
AUC for extremist user detection, up to 80% AUC for content adoption
prediction, and finally up to 72% AUC for interaction reciprocity forecasting.
We conclude by providing a thorough feature analysis that helps determine which
are the emerging signals that provide predictive power in different scenarios.
| no_new_dataset | 0.84966 |
1611.04847 | Arun Kadavankandy | Arun Kadavankandy (MAESTRO), Konstantin Avrachenkov (MAESTRO), Laura
Cottatellucci, Rajesh Sundaresan (ECE) | The Power of Side-information in Subgraph Detection | null | null | null | null | cs.LG cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we tackle the problem of hidden community detection. We
consider Belief Propagation (BP) applied to the problem of detecting a hidden
Erd\H{o}s-R\'enyi (ER) graph embedded in a larger and sparser ER graph, in the
presence of side-information. We derive two related algorithms based on BP to
perform subgraph detection in the presence of two kinds of side-information.
The first variant of side-information consists of a set of nodes, called cues,
known to be from the subgraph. The second variant of side-information consists
of a set of nodes that are cues with a given probability. It was shown in past
works that BP without side-information fails to detect the subgraph correctly
when an effective signal-to-noise ratio (SNR) parameter falls below a
threshold. In contrast, in the presence of non-trivial side-information, we
show that the BP algorithm achieves asymptotically zero error for any value of
the SNR parameter. We validate our results through simulations on synthetic
datasets as well as on a few real world networks.
| [
{
"version": "v1",
"created": "Thu, 10 Nov 2016 10:13:10 GMT"
},
{
"version": "v2",
"created": "Wed, 23 Nov 2016 09:45:20 GMT"
},
{
"version": "v3",
"created": "Mon, 6 Mar 2017 13:26:53 GMT"
}
] | 2017-03-07T00:00:00 | [
[
"Kadavankandy",
"Arun",
"",
"MAESTRO"
],
[
"Avrachenkov",
"Konstantin",
"",
"MAESTRO"
],
[
"Cottatellucci",
"Laura",
"",
"ECE"
],
[
"Sundaresan",
"Rajesh",
"",
"ECE"
]
] | TITLE: The Power of Side-information in Subgraph Detection
ABSTRACT: In this work, we tackle the problem of hidden community detection. We
consider Belief Propagation (BP) applied to the problem of detecting a hidden
Erd\H{o}s-R\'enyi (ER) graph embedded in a larger and sparser ER graph, in the
presence of side-information. We derive two related algorithms based on BP to
perform subgraph detection in the presence of two kinds of side-information.
The first variant of side-information consists of a set of nodes, called cues,
known to be from the subgraph. The second variant of side-information consists
of a set of nodes that are cues with a given probability. It was shown in past
works that BP without side-information fails to detect the subgraph correctly
when an effective signal-to-noise ratio (SNR) parameter falls below a
threshold. In contrast, in the presence of non-trivial side-information, we
show that the BP algorithm achieves asymptotically zero error for any value of
the SNR parameter. We validate our results through simulations on synthetic
datasets as well as on a few real world networks.
| no_new_dataset | 0.944995 |
1702.04280 | Afshin Dehghan | Afshin Dehghan and Enrique G. Ortiz and Guang Shu and Syed Zain Masood | DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional
Neural Network | 10 Pages | null | null | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes the details of Sighthound's fully automated age, gender
and emotion recognition system. The backbone of our system consists of several
deep convolutional neural networks that are not only computationally
inexpensive, but also provide state-of-the-art results on several competitive
benchmarks. To power our novel deep networks, we collected large labeled
datasets through a semi-supervised pipeline to reduce the annotation
effort/time. We tested our system on several public benchmarks and report
outstanding results. Our age, gender and emotion recognition models are
available to developers through the Sighthound Cloud API at
https://www.sighthound.com/products/cloud
| [
{
"version": "v1",
"created": "Tue, 14 Feb 2017 16:34:05 GMT"
},
{
"version": "v2",
"created": "Sat, 4 Mar 2017 01:43:04 GMT"
}
] | 2017-03-07T00:00:00 | [
[
"Dehghan",
"Afshin",
""
],
[
"Ortiz",
"Enrique G.",
""
],
[
"Shu",
"Guang",
""
],
[
"Masood",
"Syed Zain",
""
]
] | TITLE: DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional
Neural Network
ABSTRACT: This paper describes the details of Sighthound's fully automated age, gender
and emotion recognition system. The backbone of our system consists of several
deep convolutional neural networks that are not only computationally
inexpensive, but also provide state-of-the-art results on several competitive
benchmarks. To power our novel deep networks, we collected large labeled
datasets through a semi-supervised pipeline to reduce the annotation
effort/time. We tested our system on several public benchmarks and report
outstanding results. Our age, gender and emotion recognition models are
available to developers through the Sighthound Cloud API at
https://www.sighthound.com/products/cloud
| no_new_dataset | 0.955026 |
1702.08272 | Phil Ammirato | Phil Ammirato, Patrick Poirson, Eunbyung Park, Jana Kosecka, Alexander
C. Berg | A Dataset for Developing and Benchmarking Active Vision | To appear at ICRA 2017 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a new public dataset with a focus on simulating robotic vision
tasks in everyday indoor environments using real imagery. The dataset includes
20,000+ RGB-D images and 50,000+ 2D bounding boxes of object instances densely
captured in 9 unique scenes. We train a fast object category detector for
instance detection on our data. Using the dataset we show that, although
increasingly accurate and fast, the state of the art for object detection is
still severely impacted by object scale, occlusion, and viewing direction all
of which matter for robotics applications. We next validate the dataset for
simulating active vision, and use the dataset to develop and evaluate a
deep-network-based system for next best move prediction for object
classification using reinforcement learning. Our dataset is available for
download at cs.unc.edu/~ammirato/active_vision_dataset_website/.
| [
{
"version": "v1",
"created": "Mon, 27 Feb 2017 13:23:35 GMT"
},
{
"version": "v2",
"created": "Fri, 3 Mar 2017 20:06:58 GMT"
}
] | 2017-03-07T00:00:00 | [
[
"Ammirato",
"Phil",
""
],
[
"Poirson",
"Patrick",
""
],
[
"Park",
"Eunbyung",
""
],
[
"Kosecka",
"Jana",
""
],
[
"Berg",
"Alexander C.",
""
]
] | TITLE: A Dataset for Developing and Benchmarking Active Vision
ABSTRACT: We present a new public dataset with a focus on simulating robotic vision
tasks in everyday indoor environments using real imagery. The dataset includes
20,000+ RGB-D images and 50,000+ 2D bounding boxes of object instances densely
captured in 9 unique scenes. We train a fast object category detector for
instance detection on our data. Using the dataset we show that, although
increasingly accurate and fast, the state of the art for object detection is
still severely impacted by object scale, occlusion, and viewing direction all
of which matter for robotics applications. We next validate the dataset for
simulating active vision, and use the dataset to develop and evaluate a
deep-network-based system for next best move prediction for object
classification using reinforcement learning. Our dataset is available for
download at cs.unc.edu/~ammirato/active_vision_dataset_website/.
| new_dataset | 0.959497 |
1703.01319 | Philipp Mayr | Thomas Kr\"amer, Fakhri Momeni, Philipp Mayr | Coverage of Author Identifiers in Web of Science and Scopus | 23 pages, 1 figure, technical report | null | null | null | cs.DL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As digital collections of scientific literature are widespread and used
frequently in knowledge-intense working environments, it has become a challenge
to identify author names correctly. The treatment of homonyms is crucial for
the reliable resolution of author names. Apart from varying handling of first,
middle and last names, vendors as well as the digital library community created
tools to address the problem of author name disambiguation. This technical
report focuses on two widespread collections of scientific literature, Web of
Science (WoS) and Scopus, and the coverage with author identification
information such as Researcher ID, ORCID and Scopus Author Identifier in the
period 1996 - 2014. The goal of this study is to describe the significant
differences of the two collections with respect to overall distribution of
author identifiers and its use across different subject domains. We found that
the STM disciplines show the best coverage of author identifiers in our dataset
of 6,032,000 publications which are both covered by WoS and Scopus. In our
dataset we found 184,823 distinct ResearcherIDs and 70,043 distinct ORCIDs. In
the appendix of this report we list a complete overview of all WoS subject
areas and the amount of author identifiers in these subject areas.
| [
{
"version": "v1",
"created": "Fri, 3 Mar 2017 19:53:46 GMT"
}
] | 2017-03-07T00:00:00 | [
[
"Krämer",
"Thomas",
""
],
[
"Momeni",
"Fakhri",
""
],
[
"Mayr",
"Philipp",
""
]
] | TITLE: Coverage of Author Identifiers in Web of Science and Scopus
ABSTRACT: As digital collections of scientific literature are widespread and used
frequently in knowledge-intense working environments, it has become a challenge
to identify author names correctly. The treatment of homonyms is crucial for
the reliable resolution of author names. Apart from varying handling of first,
middle and last names, vendors as well as the digital library community created
tools to address the problem of author name disambiguation. This technical
report focuses on two widespread collections of scientific literature, Web of
Science (WoS) and Scopus, and the coverage with author identification
information such as Researcher ID, ORCID and Scopus Author Identifier in the
period 1996 - 2014. The goal of this study is to describe the significant
differences of the two collections with respect to overall distribution of
author identifiers and its use across different subject domains. We found that
the STM disciplines show the best coverage of author identifiers in our dataset
of 6,032,000 publications which are both covered by WoS and Scopus. In our
dataset we found 184,823 distinct ResearcherIDs and 70,043 distinct ORCIDs. In
the appendix of this report we list a complete overview of all WoS subject
areas and the amount of author identifiers in these subject areas.
| new_dataset | 0.960915 |
1703.01402 | Terrance DeVries | Terrance DeVries, Dhanesh Ramachandram | Skin Lesion Classification Using Deep Multi-scale Convolutional Neural
Networks | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a deep learning approach to the ISIC 2017 Skin Lesion
Classification Challenge using a multi-scale convolutional neural network. Our
approach utilizes an Inception-v3 network pre-trained on the ImageNet dataset,
which is fine-tuned for skin lesion classification using two different scales
of input images.
| [
{
"version": "v1",
"created": "Sat, 4 Mar 2017 06:32:15 GMT"
}
] | 2017-03-07T00:00:00 | [
[
"DeVries",
"Terrance",
""
],
[
"Ramachandram",
"Dhanesh",
""
]
] | TITLE: Skin Lesion Classification Using Deep Multi-scale Convolutional Neural
Networks
ABSTRACT: We present a deep learning approach to the ISIC 2017 Skin Lesion
Classification Challenge using a multi-scale convolutional neural network. Our
approach utilizes an Inception-v3 network pre-trained on the ImageNet dataset,
which is fine-tuned for skin lesion classification using two different scales
of input images.
| no_new_dataset | 0.95297 |
1703.01426 | Pankesh Patel | Amelie Gyrard, Martin Serrano, Pankesh Patel | Building Interoperable and Cross-Domain Semantic Web of Things
Applications | 22 pages | null | null | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Web of Things (WoT) is rapidly growing in popularity getting the interest
of not only technologist and scientific communities but industrial, system
integrators and solution providers. The key aspect of the WoT to succeed is the
relatively, easy-to-build ecosystems nature inherited from the web and the
capacity for building end-to-end solutions. At the WoT connecting physical
devices such as sensors, RFID tags or any devices that can send data through
the Internet using the Web is almost automatic. The WoT shared data can be used
to build smarter solutions that offer business services in the form of IoT
applications. In this chapter, we review the main WoT challenges, with
particular interest on highlighting those that rely on combining heterogeneous
IoT data for the design of smarter services and applications and that benefit
from data interoperability. Semantic web technologies help for overcoming with
such challenges by addressing, among other ones the following objectives: 1)
semantically annotating and unifying heterogeneous data, 2) enriching semantic
WoT datasets with external knowledge graphs, and 3) providing an analysis of
data by means of reasoning mechanisms to infer meaningful information. To
overcome the challenge of building interoperable semantics-based IoT
applications, the Machine-to-Machine Measurement (M3) semantic engine has been
designed to semantically annotate WoT data, build the logic of smarter services
and deduce meaningful knowledge by linking it to the external knowledge graphs
available on the web. M3 assists application and business developers in
designing interoperable Semantic Web of Things applications. Contributions in
the context of European semantic-based WoT projects are discussed and a
particular use case within FIESTA-IoT project is presented.
| [
{
"version": "v1",
"created": "Sat, 4 Mar 2017 09:41:59 GMT"
}
] | 2017-03-07T00:00:00 | [
[
"Gyrard",
"Amelie",
""
],
[
"Serrano",
"Martin",
""
],
[
"Patel",
"Pankesh",
""
]
] | TITLE: Building Interoperable and Cross-Domain Semantic Web of Things
Applications
ABSTRACT: The Web of Things (WoT) is rapidly growing in popularity getting the interest
of not only technologist and scientific communities but industrial, system
integrators and solution providers. The key aspect of the WoT to succeed is the
relatively, easy-to-build ecosystems nature inherited from the web and the
capacity for building end-to-end solutions. At the WoT connecting physical
devices such as sensors, RFID tags or any devices that can send data through
the Internet using the Web is almost automatic. The WoT shared data can be used
to build smarter solutions that offer business services in the form of IoT
applications. In this chapter, we review the main WoT challenges, with
particular interest on highlighting those that rely on combining heterogeneous
IoT data for the design of smarter services and applications and that benefit
from data interoperability. Semantic web technologies help for overcoming with
such challenges by addressing, among other ones the following objectives: 1)
semantically annotating and unifying heterogeneous data, 2) enriching semantic
WoT datasets with external knowledge graphs, and 3) providing an analysis of
data by means of reasoning mechanisms to infer meaningful information. To
overcome the challenge of building interoperable semantics-based IoT
applications, the Machine-to-Machine Measurement (M3) semantic engine has been
designed to semantically annotate WoT data, build the logic of smarter services
and deduce meaningful knowledge by linking it to the external knowledge graphs
available on the web. M3 assists application and business developers in
designing interoperable Semantic Web of Things applications. Contributions in
the context of European semantic-based WoT projects are discussed and a
particular use case within FIESTA-IoT project is presented.
| no_new_dataset | 0.944842 |
1703.01437 | Bharath Bhat | Rahul Anand Sharma, Bharath Bhat, Vineet Gandhi, C.V.Jawahar | Automated Top View Registration of Broadcast Football Videos | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose a novel method to register football broadcast video
frames on the static top view model of the playing surface. The proposed method
is fully automatic in contrast to the current state of the art which requires
manual initialization of point correspondences between the image and the static
model. Automatic registration using existing approaches has been difficult due
to the lack of sufficient point correspondences. We investigate an alternate
approach exploiting the edge information from the line markings on the field.
We formulate the registration problem as a nearest neighbour search over a
synthetically generated dictionary of edge map and homography pairs. The
synthetic dictionary generation allows us to exhaustively cover a wide variety
of camera angles and positions and reduce this problem to a minimal per-frame
edge map matching procedure. We show that the per-frame results can be improved
in videos using an optimization framework for temporal camera stabilization. We
demonstrate the efficacy of our approach by presenting extensive results on a
dataset collected from matches of football World Cup 2014.
| [
{
"version": "v1",
"created": "Sat, 4 Mar 2017 10:51:09 GMT"
}
] | 2017-03-07T00:00:00 | [
[
"Sharma",
"Rahul Anand",
""
],
[
"Bhat",
"Bharath",
""
],
[
"Gandhi",
"Vineet",
""
],
[
"Jawahar",
"C. V.",
""
]
] | TITLE: Automated Top View Registration of Broadcast Football Videos
ABSTRACT: In this paper, we propose a novel method to register football broadcast video
frames on the static top view model of the playing surface. The proposed method
is fully automatic in contrast to the current state of the art which requires
manual initialization of point correspondences between the image and the static
model. Automatic registration using existing approaches has been difficult due
to the lack of sufficient point correspondences. We investigate an alternate
approach exploiting the edge information from the line markings on the field.
We formulate the registration problem as a nearest neighbour search over a
synthetically generated dictionary of edge map and homography pairs. The
synthetic dictionary generation allows us to exhaustively cover a wide variety
of camera angles and positions and reduce this problem to a minimal per-frame
edge map matching procedure. We show that the per-frame results can be improved
in videos using an optimization framework for temporal camera stabilization. We
demonstrate the efficacy of our approach by presenting extensive results on a
dataset collected from matches of football World Cup 2014.
| no_new_dataset | 0.889577 |
1703.01442 | Abbas Hosseini | Seyed Abbas Hosseini, Keivan Alizadeh, Ali Khodadadi, Ali Arabzadeh,
Mehrdad Farajtabar, Hongyuan Zha, Hamid R. Rabiee | Recurrent Poisson Factorization for Temporal Recommendation | Submitted to KDD 2017 | Halifax, Nova Scotia - Canada - sigkdd, Codes
are available at https://github.com/AHosseini/RPF | null | null | null | cs.SI cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Poisson factorization is a probabilistic model of users and items for
recommendation systems, where the so-called implicit consumer data is modeled
by a factorized Poisson distribution. There are many variants of Poisson
factorization methods who show state-of-the-art performance on real-world
recommendation tasks. However, most of them do not explicitly take into account
the temporal behavior and the recurrent activities of users which is essential
to recommend the right item to the right user at the right time. In this paper,
we introduce Recurrent Poisson Factorization (RPF) framework that generalizes
the classical PF methods by utilizing a Poisson process for modeling the
implicit feedback. RPF treats time as a natural constituent of the model and
brings to the table a rich family of time-sensitive factorization models. To
elaborate, we instantiate several variants of RPF who are capable of handling
dynamic user preferences and item specification (DRPF), modeling the
social-aspect of product adoption (SRPF), and capturing the consumption
heterogeneity among users and items (HRPF). We also develop a variational
algorithm for approximate posterior inference that scales up to massive data
sets. Furthermore, we demonstrate RPF's superior performance over many
state-of-the-art methods on synthetic dataset, and large scale real-world
datasets on music streaming logs, and user-item interactions in M-Commerce
platforms.
| [
{
"version": "v1",
"created": "Sat, 4 Mar 2017 11:20:51 GMT"
}
] | 2017-03-07T00:00:00 | [
[
"Hosseini",
"Seyed Abbas",
""
],
[
"Alizadeh",
"Keivan",
""
],
[
"Khodadadi",
"Ali",
""
],
[
"Arabzadeh",
"Ali",
""
],
[
"Farajtabar",
"Mehrdad",
""
],
[
"Zha",
"Hongyuan",
""
],
[
"Rabiee",
"Hamid R.",
""
]
] | TITLE: Recurrent Poisson Factorization for Temporal Recommendation
ABSTRACT: Poisson factorization is a probabilistic model of users and items for
recommendation systems, where the so-called implicit consumer data is modeled
by a factorized Poisson distribution. There are many variants of Poisson
factorization methods who show state-of-the-art performance on real-world
recommendation tasks. However, most of them do not explicitly take into account
the temporal behavior and the recurrent activities of users which is essential
to recommend the right item to the right user at the right time. In this paper,
we introduce Recurrent Poisson Factorization (RPF) framework that generalizes
the classical PF methods by utilizing a Poisson process for modeling the
implicit feedback. RPF treats time as a natural constituent of the model and
brings to the table a rich family of time-sensitive factorization models. To
elaborate, we instantiate several variants of RPF who are capable of handling
dynamic user preferences and item specification (DRPF), modeling the
social-aspect of product adoption (SRPF), and capturing the consumption
heterogeneity among users and items (HRPF). We also develop a variational
algorithm for approximate posterior inference that scales up to massive data
sets. Furthermore, we demonstrate RPF's superior performance over many
state-of-the-art methods on synthetic dataset, and large scale real-world
datasets on music streaming logs, and user-item interactions in M-Commerce
platforms.
| no_new_dataset | 0.940572 |
1703.01513 | Lingxi Xie | Lingxi Xie, Alan Yuille | Genetic CNN | Submitted to CVPR 2017 (10 pages, 5 figures) | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The deep Convolutional Neural Network (CNN) is the state-of-the-art solution
for large-scale visual recognition. Following basic principles such as
increasing the depth and constructing highway connections, researchers have
manually designed a lot of fixed network structures and verified their
effectiveness.
In this paper, we discuss the possibility of learning deep network structures
automatically. Note that the number of possible network structures increases
exponentially with the number of layers in the network, which inspires us to
adopt the genetic algorithm to efficiently traverse this large search space. We
first propose an encoding method to represent each network structure in a
fixed-length binary string, and initialize the genetic algorithm by generating
a set of randomized individuals. In each generation, we define standard genetic
operations, e.g., selection, mutation and crossover, to eliminate weak
individuals and then generate more competitive ones. The competitiveness of
each individual is defined as its recognition accuracy, which is obtained via
training the network from scratch and evaluating it on a validation set. We run
the genetic process on two small datasets, i.e., MNIST and CIFAR10,
demonstrating its ability to evolve and find high-quality structures which are
little studied before. These structures are also transferrable to the
large-scale ILSVRC2012 dataset.
| [
{
"version": "v1",
"created": "Sat, 4 Mar 2017 19:44:16 GMT"
}
] | 2017-03-07T00:00:00 | [
[
"Xie",
"Lingxi",
""
],
[
"Yuille",
"Alan",
""
]
] | TITLE: Genetic CNN
ABSTRACT: The deep Convolutional Neural Network (CNN) is the state-of-the-art solution
for large-scale visual recognition. Following basic principles such as
increasing the depth and constructing highway connections, researchers have
manually designed a lot of fixed network structures and verified their
effectiveness.
In this paper, we discuss the possibility of learning deep network structures
automatically. Note that the number of possible network structures increases
exponentially with the number of layers in the network, which inspires us to
adopt the genetic algorithm to efficiently traverse this large search space. We
first propose an encoding method to represent each network structure in a
fixed-length binary string, and initialize the genetic algorithm by generating
a set of randomized individuals. In each generation, we define standard genetic
operations, e.g., selection, mutation and crossover, to eliminate weak
individuals and then generate more competitive ones. The competitiveness of
each individual is defined as its recognition accuracy, which is obtained via
training the network from scratch and evaluating it on a validation set. We run
the genetic process on two small datasets, i.e., MNIST and CIFAR10,
demonstrating its ability to evolve and find high-quality structures which are
little studied before. These structures are also transferrable to the
large-scale ILSVRC2012 dataset.
| no_new_dataset | 0.947914 |
1703.01553 | He Jiang | He Jiang, Jingxuan Zhang, Xiaochen Li, Zhilei Ren, David Lo | A More Accurate Model for Finding Tutorial Segments Explaining APIs | 11 pages, 11 figures, In Proc. of 23rd IEEE International Conference
on Software Analysis, Evolution, and Reengineering (SANER'16), pp.157-167 | null | 10.1109/SANER.2016.59 | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Developers prefer to utilize third-party libraries when they implement some
functionalities and Application Programming Interfaces (APIs) are frequently
used by them. Facing an unfamiliar API, developers tend to consult tutorials as
learning resources. Unfortunately, the segments explaining a specific API
scatter across tutorials. Hence, it remains a challenging issue to find the
relevant segments. In this study, we propose a more accurate model to find the
exact tutorial fragments explaining APIs. This new model consists of a text
classifier with domain specific features. More specifically, we discover two
important indicators to complement traditional text based features, namely
co-occurrence APIs and knowledge based API extensions. In addition, we
incorporate Word2Vec, a semantic similarity metric to enhance the new model.
Extensive experiments over two publicly available tutorial datasets show that
our new model could find up to 90% fragments explaining APIs and improve the
state-of-the-art model by up to 30% in terms of F-measure.
| [
{
"version": "v1",
"created": "Sun, 5 Mar 2017 03:42:38 GMT"
}
] | 2017-03-07T00:00:00 | [
[
"Jiang",
"He",
""
],
[
"Zhang",
"Jingxuan",
""
],
[
"Li",
"Xiaochen",
""
],
[
"Ren",
"Zhilei",
""
],
[
"Lo",
"David",
""
]
] | TITLE: A More Accurate Model for Finding Tutorial Segments Explaining APIs
ABSTRACT: Developers prefer to utilize third-party libraries when they implement some
functionalities and Application Programming Interfaces (APIs) are frequently
used by them. Facing an unfamiliar API, developers tend to consult tutorials as
learning resources. Unfortunately, the segments explaining a specific API
scatter across tutorials. Hence, it remains a challenging issue to find the
relevant segments. In this study, we propose a more accurate model to find the
exact tutorial fragments explaining APIs. This new model consists of a text
classifier with domain specific features. More specifically, we discover two
important indicators to complement traditional text based features, namely
co-occurrence APIs and knowledge based API extensions. In addition, we
incorporate Word2Vec, a semantic similarity metric to enhance the new model.
Extensive experiments over two publicly available tutorial datasets show that
our new model could find up to 90% fragments explaining APIs and improve the
state-of-the-art model by up to 30% in terms of F-measure.
| no_new_dataset | 0.950319 |
1703.01605 | Yongwei Nie | Yongwei Nie, Xu Cao, Chengjiang Long, Ping Li, Guiqing Li | L2GSCI: Local to Global Seam Cutting and Integrating for Accurate Face
Contour Extraction | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Current face alignment algorithms can robustly find a set of landmarks along
face contour. However, the landmarks are sparse and lack curve details,
especially in chin and cheek areas where a lot of concave-convex bending
information exists. In this paper, we propose a local to global seam cutting
and integrating algorithm (L2GSCI) to extract continuous and accurate face
contour. Our method works in three steps with the help of a rough initial
curve. First, we sample small and overlapped squares along the initial curve.
Second, the seam cutting part of L2GSCI extracts a local seam in each square
region. Finally, the seam integrating part of L2GSCI connects all the redundant
seams together to form a continuous and complete face curve. Overall, the
proposed method is much more straightforward than existing face alignment
algorithms, but can achieve pixel-level continuous face curves rather than
discrete and sparse landmarks. Moreover, experiments on two face benchmark
datasets (i.e., LFPW and HELEN) show that our method can precisely reveal
concave-convex bending details of face contours, which has significantly
improved the performance when compared with the state-ofthe- art face alignment
approaches.
| [
{
"version": "v1",
"created": "Sun, 5 Mar 2017 15:06:28 GMT"
}
] | 2017-03-07T00:00:00 | [
[
"Nie",
"Yongwei",
""
],
[
"Cao",
"Xu",
""
],
[
"Long",
"Chengjiang",
""
],
[
"Li",
"Ping",
""
],
[
"Li",
"Guiqing",
""
]
] | TITLE: L2GSCI: Local to Global Seam Cutting and Integrating for Accurate Face
Contour Extraction
ABSTRACT: Current face alignment algorithms can robustly find a set of landmarks along
face contour. However, the landmarks are sparse and lack curve details,
especially in chin and cheek areas where a lot of concave-convex bending
information exists. In this paper, we propose a local to global seam cutting
and integrating algorithm (L2GSCI) to extract continuous and accurate face
contour. Our method works in three steps with the help of a rough initial
curve. First, we sample small and overlapped squares along the initial curve.
Second, the seam cutting part of L2GSCI extracts a local seam in each square
region. Finally, the seam integrating part of L2GSCI connects all the redundant
seams together to form a continuous and complete face curve. Overall, the
proposed method is much more straightforward than existing face alignment
algorithms, but can achieve pixel-level continuous face curves rather than
discrete and sparse landmarks. Moreover, experiments on two face benchmark
datasets (i.e., LFPW and HELEN) show that our method can precisely reveal
concave-convex bending details of face contours, which has significantly
improved the performance when compared with the state-ofthe- art face alignment
approaches.
| no_new_dataset | 0.951323 |
1703.01726 | Xiaogang Zhang | Xiao-gang Zhang, Shou-qian Sun, Ke-jun Zhang | A Novel Comprehensive Approach for Estimating Concept Semantic
Similarity in WordNet | 11pages, 2 tables | null | null | null | cs.CL | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Computation of semantic similarity between concepts is an important
foundation for many research works. This paper focuses on IC computing methods
and IC measures, which estimate the semantic similarities between concepts by
exploiting the topological parameters of the taxonomy. Based on analyzing
representative IC computing methods and typical semantic similarity measures,
we propose a new hybrid IC computing method. Through adopting the parameter
dhyp and lch, we utilize the new IC computing method and propose a novel
comprehensive measure of semantic similarity between concepts. An experiment
based on WordNet "is a" taxonomy has been designed to test representative
measures and our measure on benchmark dataset R&G, and the results show that
our measure can obviously improve the similarity accuracy. We evaluate the
proposed approach by comparing the correlation coefficients between five
measures and the artificial data. The results show that our proposal
outperforms the previous measures.
| [
{
"version": "v1",
"created": "Mon, 6 Mar 2017 05:07:12 GMT"
}
] | 2017-03-07T00:00:00 | [
[
"Zhang",
"Xiao-gang",
""
],
[
"Sun",
"Shou-qian",
""
],
[
"Zhang",
"Ke-jun",
""
]
] | TITLE: A Novel Comprehensive Approach for Estimating Concept Semantic
Similarity in WordNet
ABSTRACT: Computation of semantic similarity between concepts is an important
foundation for many research works. This paper focuses on IC computing methods
and IC measures, which estimate the semantic similarities between concepts by
exploiting the topological parameters of the taxonomy. Based on analyzing
representative IC computing methods and typical semantic similarity measures,
we propose a new hybrid IC computing method. Through adopting the parameter
dhyp and lch, we utilize the new IC computing method and propose a novel
comprehensive measure of semantic similarity between concepts. An experiment
based on WordNet "is a" taxonomy has been designed to test representative
measures and our measure on benchmark dataset R&G, and the results show that
our measure can obviously improve the similarity accuracy. We evaluate the
proposed approach by comparing the correlation coefficients between five
measures and the artificial data. The results show that our proposal
outperforms the previous measures.
| no_new_dataset | 0.944177 |
1703.01883 | Guido Borghi | Marco Venturelli, Guido Borghi, Roberto Vezzani, Rita Cucchiara | Deep Head Pose Estimation from Depth Data for In-car Automotive
Applications | 2nd International Workshop on Understanding Human Activities through
3D Sensors (ICPR 2016) | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, deep learning approaches have achieved promising results in various
fields of computer vision. In this paper, we tackle the problem of head pose
estimation through a Convolutional Neural Network (CNN). Differently from other
proposals in the literature, the described system is able to work directly and
based only on raw depth data. Moreover, the head pose estimation is solved as a
regression problem and does not rely on visual facial features like facial
landmarks. We tested our system on a well known public dataset, Biwi Kinect
Head Pose, showing that our approach achieves state-of-art results and is able
to meet real time performance requirements.
| [
{
"version": "v1",
"created": "Mon, 6 Mar 2017 14:11:55 GMT"
}
] | 2017-03-07T00:00:00 | [
[
"Venturelli",
"Marco",
""
],
[
"Borghi",
"Guido",
""
],
[
"Vezzani",
"Roberto",
""
],
[
"Cucchiara",
"Rita",
""
]
] | TITLE: Deep Head Pose Estimation from Depth Data for In-car Automotive
Applications
ABSTRACT: Recently, deep learning approaches have achieved promising results in various
fields of computer vision. In this paper, we tackle the problem of head pose
estimation through a Convolutional Neural Network (CNN). Differently from other
proposals in the literature, the described system is able to work directly and
based only on raw depth data. Moreover, the head pose estimation is solved as a
regression problem and does not rely on visual facial features like facial
landmarks. We tested our system on a well known public dataset, Biwi Kinect
Head Pose, showing that our approach achieves state-of-art results and is able
to meet real time performance requirements.
| no_new_dataset | 0.948489 |
1703.01918 | Ronald Kemker | Ronald Kemker, Carl Salvaggio, and Christopher Kanan | High-Resolution Multispectral Dataset for Semantic Segmentation | 9 pages, 8 Figures | null | null | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Unmanned aircraft have decreased the cost required to collect remote sensing
imagery, which has enabled researchers to collect high-spatial resolution data
from multiple sensor modalities more frequently and easily. The increase in
data will push the need for semantic segmentation frameworks that are able to
classify non-RGB imagery, but this type of algorithmic development requires an
increase in publicly available benchmark datasets with class labels. In this
paper, we introduce a high-resolution multispectral dataset with image labels.
This new benchmark dataset has been pre-split into training/testing folds in
order to standardize evaluation and continue to push state-of-the-art
classification frameworks for non-RGB imagery.
| [
{
"version": "v1",
"created": "Mon, 6 Mar 2017 15:16:56 GMT"
}
] | 2017-03-07T00:00:00 | [
[
"Kemker",
"Ronald",
""
],
[
"Salvaggio",
"Carl",
""
],
[
"Kanan",
"Christopher",
""
]
] | TITLE: High-Resolution Multispectral Dataset for Semantic Segmentation
ABSTRACT: Unmanned aircraft have decreased the cost required to collect remote sensing
imagery, which has enabled researchers to collect high-spatial resolution data
from multiple sensor modalities more frequently and easily. The increase in
data will push the need for semantic segmentation frameworks that are able to
classify non-RGB imagery, but this type of algorithmic development requires an
increase in publicly available benchmark datasets with class labels. In this
paper, we introduce a high-resolution multispectral dataset with image labels.
This new benchmark dataset has been pre-split into training/testing folds in
order to standardize evaluation and continue to push state-of-the-art
classification frameworks for non-RGB imagery.
| new_dataset | 0.956836 |
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