id
stringlengths 9
16
| submitter
stringlengths 3
64
⌀ | authors
stringlengths 5
6.63k
| title
stringlengths 7
245
| comments
stringlengths 1
482
⌀ | journal-ref
stringlengths 4
382
⌀ | doi
stringlengths 9
151
⌀ | report-no
stringclasses 984
values | categories
stringlengths 5
108
| license
stringclasses 9
values | abstract
stringlengths 83
3.41k
| versions
listlengths 1
20
| update_date
timestamp[s]date 2007-05-23 00:00:00
2025-04-11 00:00:00
| authors_parsed
sequencelengths 1
427
| prompt
stringlengths 166
3.49k
| label
stringclasses 2
values | prob
float64 0.5
0.98
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1204.5507 | Ketan Rajawat | Ketan Rajawat, Emiliano Dall'Anese, and Georgios B. Giannakis | Dynamic Network Delay Cartography | Part of this paper has been published in the \emph{IEEE Statistical
Signal Processing Workshop}, Ann Arbor, MI, Aug. 2012 | null | 10.1109/TIT.2014.2311802 | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Path delays in IP networks are important metrics, required by network
operators for assessment, planning, and fault diagnosis. Monitoring delays of
all source-destination pairs in a large network is however challenging and
wasteful of resources. The present paper advocates a spatio-temporal Kalman
filtering approach to construct network-wide delay maps using measurements on
only a few paths. The proposed network cartography framework allows efficient
tracking and prediction of delays by relying on both topological as well as
historical data. Optimal paths for delay measurement are selected in an online
fashion by leveraging the notion of submodularity. The resulting predictor is
optimal in the class of linear predictors, and outperforms competing
alternatives on real-world datasets.
| [
{
"version": "v1",
"created": "Tue, 24 Apr 2012 22:31:47 GMT"
},
{
"version": "v2",
"created": "Sun, 11 Nov 2012 10:55:56 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Rajawat",
"Ketan",
""
],
[
"Dall'Anese",
"Emiliano",
""
],
[
"Giannakis",
"Georgios B.",
""
]
] | TITLE: Dynamic Network Delay Cartography
ABSTRACT: Path delays in IP networks are important metrics, required by network
operators for assessment, planning, and fault diagnosis. Monitoring delays of
all source-destination pairs in a large network is however challenging and
wasteful of resources. The present paper advocates a spatio-temporal Kalman
filtering approach to construct network-wide delay maps using measurements on
only a few paths. The proposed network cartography framework allows efficient
tracking and prediction of delays by relying on both topological as well as
historical data. Optimal paths for delay measurement are selected in an online
fashion by leveraging the notion of submodularity. The resulting predictor is
optimal in the class of linear predictors, and outperforms competing
alternatives on real-world datasets.
| no_new_dataset | 0.954137 |
1212.2262 | Jin Wang | Jin Wang, Ping Liu, Mary F.H.She, Saeid Nahavandi and and Abbas
Kouzani | Bag-of-Words Representation for Biomedical Time Series Classification | 10 pages, 7 figures. Submitted to IEEE Transaction on Biomedical
Engineering | null | 10.1016/j.bspc.2013.06.004 | null | cs.LG cs.AI | http://creativecommons.org/licenses/by-nc-sa/3.0/ | Automatic analysis of biomedical time series such as electroencephalogram
(EEG) and electrocardiographic (ECG) signals has attracted great interest in
the community of biomedical engineering due to its important applications in
medicine. In this work, a simple yet effective bag-of-words representation that
is able to capture both local and global structure similarity information is
proposed for biomedical time series representation. In particular, similar to
the bag-of-words model used in text document domain, the proposed method treats
a time series as a text document and extracts local segments from the time
series as words. The biomedical time series is then represented as a histogram
of codewords, each entry of which is the count of a codeword appeared in the
time series. Although the temporal order of the local segments is ignored, the
bag-of-words representation is able to capture high-level structural
information because both local and global structural information are well
utilized. The performance of the bag-of-words model is validated on three
datasets extracted from real EEG and ECG signals. The experimental results
demonstrate that the proposed method is not only insensitive to parameters of
the bag-of-words model such as local segment length and codebook size, but also
robust to noise.
| [
{
"version": "v1",
"created": "Tue, 11 Dec 2012 00:49:27 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Wang",
"Jin",
""
],
[
"Liu",
"Ping",
""
],
[
"She",
"Mary F. H.",
""
],
[
"Nahavandi",
"Saeid",
""
],
[
"Kouzani",
"and Abbas",
""
]
] | TITLE: Bag-of-Words Representation for Biomedical Time Series Classification
ABSTRACT: Automatic analysis of biomedical time series such as electroencephalogram
(EEG) and electrocardiographic (ECG) signals has attracted great interest in
the community of biomedical engineering due to its important applications in
medicine. In this work, a simple yet effective bag-of-words representation that
is able to capture both local and global structure similarity information is
proposed for biomedical time series representation. In particular, similar to
the bag-of-words model used in text document domain, the proposed method treats
a time series as a text document and extracts local segments from the time
series as words. The biomedical time series is then represented as a histogram
of codewords, each entry of which is the count of a codeword appeared in the
time series. Although the temporal order of the local segments is ignored, the
bag-of-words representation is able to capture high-level structural
information because both local and global structural information are well
utilized. The performance of the bag-of-words model is validated on three
datasets extracted from real EEG and ECG signals. The experimental results
demonstrate that the proposed method is not only insensitive to parameters of
the bag-of-words model such as local segment length and codebook size, but also
robust to noise.
| no_new_dataset | 0.952309 |
1302.6615 | Ratnadip Adhikari | Ratnadip Adhikari, R. K. Agrawal, Laxmi Kant | PSO based Neural Networks vs. Traditional Statistical Models for
Seasonal Time Series Forecasting | 4 figures, 4 tables, 31 references, conference proceedings | IEEE International Advanced Computing Conference (IACC), 2013 | 10.1109/IAdCC.2013.6514315 | null | cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Seasonality is a distinctive characteristic which is often observed in many
practical time series. Artificial Neural Networks (ANNs) are a class of
promising models for efficiently recognizing and forecasting seasonal patterns.
In this paper, the Particle Swarm Optimization (PSO) approach is used to
enhance the forecasting strengths of feedforward ANN (FANN) as well as Elman
ANN (EANN) models for seasonal data. Three widely popular versions of the basic
PSO algorithm, viz. Trelea-I, Trelea-II and Clerc-Type1 are considered here.
The empirical analysis is conducted on three real-world seasonal time series.
Results clearly show that each version of the PSO algorithm achieves notably
better forecasting accuracies than the standard Backpropagation (BP) training
method for both FANN and EANN models. The neural network forecasting results
are also compared with those from the three traditional statistical models,
viz. Seasonal Autoregressive Integrated Moving Average (SARIMA), Holt-Winters
(HW) and Support Vector Machine (SVM). The comparison demonstrates that both
PSO and BP based neural networks outperform SARIMA, HW and SVM models for all
three time series datasets. The forecasting performances of ANNs are further
improved through combining the outputs from the three PSO based models.
| [
{
"version": "v1",
"created": "Tue, 26 Feb 2013 22:25:16 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Adhikari",
"Ratnadip",
""
],
[
"Agrawal",
"R. K.",
""
],
[
"Kant",
"Laxmi",
""
]
] | TITLE: PSO based Neural Networks vs. Traditional Statistical Models for
Seasonal Time Series Forecasting
ABSTRACT: Seasonality is a distinctive characteristic which is often observed in many
practical time series. Artificial Neural Networks (ANNs) are a class of
promising models for efficiently recognizing and forecasting seasonal patterns.
In this paper, the Particle Swarm Optimization (PSO) approach is used to
enhance the forecasting strengths of feedforward ANN (FANN) as well as Elman
ANN (EANN) models for seasonal data. Three widely popular versions of the basic
PSO algorithm, viz. Trelea-I, Trelea-II and Clerc-Type1 are considered here.
The empirical analysis is conducted on three real-world seasonal time series.
Results clearly show that each version of the PSO algorithm achieves notably
better forecasting accuracies than the standard Backpropagation (BP) training
method for both FANN and EANN models. The neural network forecasting results
are also compared with those from the three traditional statistical models,
viz. Seasonal Autoregressive Integrated Moving Average (SARIMA), Holt-Winters
(HW) and Support Vector Machine (SVM). The comparison demonstrates that both
PSO and BP based neural networks outperform SARIMA, HW and SVM models for all
three time series datasets. The forecasting performances of ANNs are further
improved through combining the outputs from the three PSO based models.
| no_new_dataset | 0.950778 |
1306.1350 | Tuomo Sipola | Tuomo Sipola, Fengyu Cong, Tapani Ristaniemi, Vinoo Alluri, Petri
Toiviainen, Elvira Brattico, Asoke K. Nandi | Diffusion map for clustering fMRI spatial maps extracted by independent
component analysis | 6 pages. 8 figures. Copyright (c) 2013 IEEE. Published at 2013 IEEE
International Workshop on Machine Learning for Signal Processing | null | 10.1109/MLSP.2013.6661923 | null | cs.CE cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Functional magnetic resonance imaging (fMRI) produces data about activity
inside the brain, from which spatial maps can be extracted by independent
component analysis (ICA). In datasets, there are n spatial maps that contain p
voxels. The number of voxels is very high compared to the number of analyzed
spatial maps. Clustering of the spatial maps is usually based on correlation
matrices. This usually works well, although such a similarity matrix inherently
can explain only a certain amount of the total variance contained in the
high-dimensional data where n is relatively small but p is large. For
high-dimensional space, it is reasonable to perform dimensionality reduction
before clustering. In this research, we used the recently developed diffusion
map for dimensionality reduction in conjunction with spectral clustering. This
research revealed that the diffusion map based clustering worked as well as the
more traditional methods, and produced more compact clusters when needed.
| [
{
"version": "v1",
"created": "Thu, 6 Jun 2013 09:29:25 GMT"
},
{
"version": "v2",
"created": "Fri, 14 Jun 2013 06:44:37 GMT"
},
{
"version": "v3",
"created": "Sun, 14 Jul 2013 16:03:54 GMT"
},
{
"version": "v4",
"created": "Fri, 27 Sep 2013 08:58:30 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Sipola",
"Tuomo",
""
],
[
"Cong",
"Fengyu",
""
],
[
"Ristaniemi",
"Tapani",
""
],
[
"Alluri",
"Vinoo",
""
],
[
"Toiviainen",
"Petri",
""
],
[
"Brattico",
"Elvira",
""
],
[
"Nandi",
"Asoke K.",
""
]
] | TITLE: Diffusion map for clustering fMRI spatial maps extracted by independent
component analysis
ABSTRACT: Functional magnetic resonance imaging (fMRI) produces data about activity
inside the brain, from which spatial maps can be extracted by independent
component analysis (ICA). In datasets, there are n spatial maps that contain p
voxels. The number of voxels is very high compared to the number of analyzed
spatial maps. Clustering of the spatial maps is usually based on correlation
matrices. This usually works well, although such a similarity matrix inherently
can explain only a certain amount of the total variance contained in the
high-dimensional data where n is relatively small but p is large. For
high-dimensional space, it is reasonable to perform dimensionality reduction
before clustering. In this research, we used the recently developed diffusion
map for dimensionality reduction in conjunction with spectral clustering. This
research revealed that the diffusion map based clustering worked as well as the
more traditional methods, and produced more compact clusters when needed.
| no_new_dataset | 0.954605 |
1307.1599 | Uwe Aickelin | Chris Roadknight, Uwe Aickelin, Guoping Qiu, John Scholefield, Lindy
Durrant | Supervised Learning and Anti-learning of Colorectal Cancer Classes and
Survival Rates from Cellular Biology Parameters | IEEE International Conference on Systems, Man, and Cybernetics, pp
797-802, 2012 | null | 10.1109/ICSMC.2012.6377825 | null | cs.LG cs.CE stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we describe a dataset relating to cellular and physical
conditions of patients who are operated upon to remove colorectal tumours. This
data provides a unique insight into immunological status at the point of tumour
removal, tumour classification and post-operative survival. Attempts are made
to learn relationships between attributes (physical and immunological) and the
resulting tumour stage and survival. Results for conventional machine learning
approaches can be considered poor, especially for predicting tumour stages for
the most important types of cancer. This poor performance is further
investigated and compared with a synthetic, dataset based on the logical
exclusive-OR function and it is shown that there is a significant level of
'anti-learning' present in all supervised methods used and this can be
explained by the highly dimensional, complex and sparsely representative
dataset. For predicting the stage of cancer from the immunological attributes,
anti-learning approaches outperform a range of popular algorithms.
| [
{
"version": "v1",
"created": "Fri, 5 Jul 2013 12:53:28 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Roadknight",
"Chris",
""
],
[
"Aickelin",
"Uwe",
""
],
[
"Qiu",
"Guoping",
""
],
[
"Scholefield",
"John",
""
],
[
"Durrant",
"Lindy",
""
]
] | TITLE: Supervised Learning and Anti-learning of Colorectal Cancer Classes and
Survival Rates from Cellular Biology Parameters
ABSTRACT: In this paper, we describe a dataset relating to cellular and physical
conditions of patients who are operated upon to remove colorectal tumours. This
data provides a unique insight into immunological status at the point of tumour
removal, tumour classification and post-operative survival. Attempts are made
to learn relationships between attributes (physical and immunological) and the
resulting tumour stage and survival. Results for conventional machine learning
approaches can be considered poor, especially for predicting tumour stages for
the most important types of cancer. This poor performance is further
investigated and compared with a synthetic, dataset based on the logical
exclusive-OR function and it is shown that there is a significant level of
'anti-learning' present in all supervised methods used and this can be
explained by the highly dimensional, complex and sparsely representative
dataset. For predicting the stage of cancer from the immunological attributes,
anti-learning approaches outperform a range of popular algorithms.
| new_dataset | 0.969091 |
1309.3323 | Ted Underwood | Ted Underwood, Michael L. Black, Loretta Auvil, Boris Capitanu | Mapping Mutable Genres in Structurally Complex Volumes | Preprint accepted for the 2013 IEEE International Conference on Big
Data. Revised to include corroborating evidence from a smaller workset | null | 10.1109/BigData.2013.6691676 | null | cs.CL cs.DL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To mine large digital libraries in humanistically meaningful ways, scholars
need to divide them by genre. This is a task that classification algorithms are
well suited to assist, but they need adjustment to address the specific
challenges of this domain. Digital libraries pose two problems of scale not
usually found in the article datasets used to test these algorithms. 1) Because
libraries span several centuries, the genres being identified may change
gradually across the time axis. 2) Because volumes are much longer than
articles, they tend to be internally heterogeneous, and the classification task
needs to begin with segmentation. We describe a multi-layered solution that
trains hidden Markov models to segment volumes, and uses ensembles of
overlapping classifiers to address historical change. We test this approach on
a collection of 469,200 volumes drawn from HathiTrust Digital Library. To
demonstrate the humanistic value of these methods, we extract 32,209 volumes of
fiction from the digital library, and trace the changing proportions of first-
and third-person narration in the corpus. We note that narrative points of view
seem to have strong associations with particular themes and genres.
| [
{
"version": "v1",
"created": "Thu, 12 Sep 2013 22:27:59 GMT"
},
{
"version": "v2",
"created": "Wed, 18 Sep 2013 17:37:27 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Underwood",
"Ted",
""
],
[
"Black",
"Michael L.",
""
],
[
"Auvil",
"Loretta",
""
],
[
"Capitanu",
"Boris",
""
]
] | TITLE: Mapping Mutable Genres in Structurally Complex Volumes
ABSTRACT: To mine large digital libraries in humanistically meaningful ways, scholars
need to divide them by genre. This is a task that classification algorithms are
well suited to assist, but they need adjustment to address the specific
challenges of this domain. Digital libraries pose two problems of scale not
usually found in the article datasets used to test these algorithms. 1) Because
libraries span several centuries, the genres being identified may change
gradually across the time axis. 2) Because volumes are much longer than
articles, they tend to be internally heterogeneous, and the classification task
needs to begin with segmentation. We describe a multi-layered solution that
trains hidden Markov models to segment volumes, and uses ensembles of
overlapping classifiers to address historical change. We test this approach on
a collection of 469,200 volumes drawn from HathiTrust Digital Library. To
demonstrate the humanistic value of these methods, we extract 32,209 volumes of
fiction from the digital library, and trace the changing proportions of first-
and third-person narration in the corpus. We note that narrative points of view
seem to have strong associations with particular themes and genres.
| no_new_dataset | 0.945651 |
1310.3101 | Eric Strobl | Eric Strobl, Shyam Visweswaran | Deep Multiple Kernel Learning | 4 pages, 1 figure, 1 table, conference paper | IEEE 12th International Conference on Machine Learning and
Applications (ICMLA 2013) | 10.1109/ICMLA.2013.84 | null | stat.ML cs.LG | http://creativecommons.org/licenses/publicdomain/ | Deep learning methods have predominantly been applied to large artificial
neural networks. Despite their state-of-the-art performance, these large
networks typically do not generalize well to datasets with limited sample
sizes. In this paper, we take a different approach by learning multiple layers
of kernels. We combine kernels at each layer and then optimize over an estimate
of the support vector machine leave-one-out error rather than the dual
objective function. Our experiments on a variety of datasets show that each
layer successively increases performance with only a few base kernels.
| [
{
"version": "v1",
"created": "Fri, 11 Oct 2013 12:14:00 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Strobl",
"Eric",
""
],
[
"Visweswaran",
"Shyam",
""
]
] | TITLE: Deep Multiple Kernel Learning
ABSTRACT: Deep learning methods have predominantly been applied to large artificial
neural networks. Despite their state-of-the-art performance, these large
networks typically do not generalize well to datasets with limited sample
sizes. In this paper, we take a different approach by learning multiple layers
of kernels. We combine kernels at each layer and then optimize over an estimate
of the support vector machine leave-one-out error rather than the dual
objective function. Our experiments on a variety of datasets show that each
layer successively increases performance with only a few base kernels.
| no_new_dataset | 0.949995 |
1312.6808 | Feng Xia | Feng Xia, Nana Yaw Asabere, Joel J.P.C. Rodrigues, Filippo Basso,
Nakema Deonauth, Wei Wang | Socially-Aware Venue Recommendation for Conference Participants | null | The 10th IEEE International Conference on Ubiquitous Intelligence
and Computing (UIC), Vietri sul Mare, Italy, December 2013 | 10.1109/UIC-ATC.2013.81 | null | cs.IR cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Current research environments are witnessing high enormities of presentations
occurring in different sessions at academic conferences. This situation makes
it difficult for researchers (especially juniors) to attend the right
presentation session(s) for effective collaboration. In this paper, we propose
an innovative venue recommendation algorithm to enhance smart conference
participation. Our proposed algorithm, Social Aware Recommendation of Venues
and Environments (SARVE), computes the Pearson Correlation and social
characteristic information of conference participants. SARVE further
incorporates the current context of both the smart conference community and
participants in order to model a recommendation process using distributed
community detection. Through the integration of the above computations and
techniques, we are able to recommend presentation sessions of active
participant presenters that may be of high interest to a particular
participant. We evaluate SARVE using a real world dataset. Our experimental
results demonstrate that SARVE outperforms other state-of-the-art methods.
| [
{
"version": "v1",
"created": "Tue, 24 Dec 2013 12:33:30 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Xia",
"Feng",
""
],
[
"Asabere",
"Nana Yaw",
""
],
[
"Rodrigues",
"Joel J. P. C.",
""
],
[
"Basso",
"Filippo",
""
],
[
"Deonauth",
"Nakema",
""
],
[
"Wang",
"Wei",
""
]
] | TITLE: Socially-Aware Venue Recommendation for Conference Participants
ABSTRACT: Current research environments are witnessing high enormities of presentations
occurring in different sessions at academic conferences. This situation makes
it difficult for researchers (especially juniors) to attend the right
presentation session(s) for effective collaboration. In this paper, we propose
an innovative venue recommendation algorithm to enhance smart conference
participation. Our proposed algorithm, Social Aware Recommendation of Venues
and Environments (SARVE), computes the Pearson Correlation and social
characteristic information of conference participants. SARVE further
incorporates the current context of both the smart conference community and
participants in order to model a recommendation process using distributed
community detection. Through the integration of the above computations and
techniques, we are able to recommend presentation sessions of active
participant presenters that may be of high interest to a particular
participant. We evaluate SARVE using a real world dataset. Our experimental
results demonstrate that SARVE outperforms other state-of-the-art methods.
| no_new_dataset | 0.952574 |
1401.3201 | Chongjing Sun | Chongjing Sun, Philip S. Yu, Xiangnan Kong and Yan Fu | Privacy Preserving Social Network Publication Against Mutual Friend
Attacks | 10 pages, 11 figures, extended version of a paper in the 4th IEEE
International Workshop on Privacy Aspects of Data Mining(PADM2013) | null | 10.1109/ICDMW.2013.71 | null | cs.DB cs.CR cs.SI | http://creativecommons.org/licenses/by/3.0/ | Publishing social network data for research purposes has raised serious
concerns for individual privacy. There exist many privacy-preserving works that
can deal with different attack models. In this paper, we introduce a novel
privacy attack model and refer it as a mutual friend attack. In this model, the
adversary can re-identify a pair of friends by using their number of mutual
friends. To address this issue, we propose a new anonymity concept, called
k-NMF anonymity, i.e., k-anonymity on the number of mutual friends, which
ensures that there exist at least k-1 other friend pairs in the graph that
share the same number of mutual friends. We devise algorithms to achieve the
k-NMF anonymity while preserving the original vertex set in the sense that we
allow the occasional addition but no deletion of vertices. Further we give an
algorithm to ensure the k-degree anonymity in addition to the k-NMF anonymity.
The experimental results on real-word datasets demonstrate that our approach
can preserve the privacy and utility of social networks effectively against
mutual friend attacks.
| [
{
"version": "v1",
"created": "Fri, 11 Oct 2013 03:52:41 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Sun",
"Chongjing",
""
],
[
"Yu",
"Philip S.",
""
],
[
"Kong",
"Xiangnan",
""
],
[
"Fu",
"Yan",
""
]
] | TITLE: Privacy Preserving Social Network Publication Against Mutual Friend
Attacks
ABSTRACT: Publishing social network data for research purposes has raised serious
concerns for individual privacy. There exist many privacy-preserving works that
can deal with different attack models. In this paper, we introduce a novel
privacy attack model and refer it as a mutual friend attack. In this model, the
adversary can re-identify a pair of friends by using their number of mutual
friends. To address this issue, we propose a new anonymity concept, called
k-NMF anonymity, i.e., k-anonymity on the number of mutual friends, which
ensures that there exist at least k-1 other friend pairs in the graph that
share the same number of mutual friends. We devise algorithms to achieve the
k-NMF anonymity while preserving the original vertex set in the sense that we
allow the occasional addition but no deletion of vertices. Further we give an
algorithm to ensure the k-degree anonymity in addition to the k-NMF anonymity.
The experimental results on real-word datasets demonstrate that our approach
can preserve the privacy and utility of social networks effectively against
mutual friend attacks.
| no_new_dataset | 0.94743 |
1404.6560 | Jad Hachem | Jad Hachem, Nikhil Karamchandani, Suhas Diggavi | Content Caching and Delivery over Heterogeneous Wireless Networks | A shorter version is to appear in IEEE INFOCOM 2015 | null | 10.1109/INFOCOM.2015.7218445 | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Emerging heterogeneous wireless architectures consist of a dense deployment
of local-coverage wireless access points (APs) with high data rates, along with
sparsely-distributed, large-coverage macro-cell base stations (BS). We design a
coded caching-and-delivery scheme for such architectures that equips APs with
storage, enabling content pre-fetching prior to knowing user demands. Users
requesting content are served by connecting to local APs with cached content,
as well as by listening to a BS broadcast transmission. For any given content
popularity profile, the goal is to design the caching-and-delivery scheme so as
to optimally trade off the transmission cost at the BS against the storage cost
at the APs and the user cost of connecting to multiple APs. We design a coded
caching scheme for non-uniform content popularity that dynamically allocates
user access to APs based on requested content. We demonstrate the approximate
optimality of our scheme with respect to information-theoretic bounds. We
numerically evaluate it on a YouTube dataset and quantify the trade-off between
transmission rate, storage, and access cost. Our numerical results also suggest
the intriguing possibility that, to gain most of the benefits of coded caching,
it suffices to divide the content into a small number of popularity classes.
| [
{
"version": "v1",
"created": "Fri, 25 Apr 2014 21:22:55 GMT"
},
{
"version": "v2",
"created": "Thu, 12 Mar 2015 08:48:08 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Hachem",
"Jad",
""
],
[
"Karamchandani",
"Nikhil",
""
],
[
"Diggavi",
"Suhas",
""
]
] | TITLE: Content Caching and Delivery over Heterogeneous Wireless Networks
ABSTRACT: Emerging heterogeneous wireless architectures consist of a dense deployment
of local-coverage wireless access points (APs) with high data rates, along with
sparsely-distributed, large-coverage macro-cell base stations (BS). We design a
coded caching-and-delivery scheme for such architectures that equips APs with
storage, enabling content pre-fetching prior to knowing user demands. Users
requesting content are served by connecting to local APs with cached content,
as well as by listening to a BS broadcast transmission. For any given content
popularity profile, the goal is to design the caching-and-delivery scheme so as
to optimally trade off the transmission cost at the BS against the storage cost
at the APs and the user cost of connecting to multiple APs. We design a coded
caching scheme for non-uniform content popularity that dynamically allocates
user access to APs based on requested content. We demonstrate the approximate
optimality of our scheme with respect to information-theoretic bounds. We
numerically evaluate it on a YouTube dataset and quantify the trade-off between
transmission rate, storage, and access cost. Our numerical results also suggest
the intriguing possibility that, to gain most of the benefits of coded caching,
it suffices to divide the content into a small number of popularity classes.
| no_new_dataset | 0.943295 |
1408.4792 | Adnan Anwar | Adnan Anwar, Abdun Naser Mahmood | Enhanced Estimation of Autoregressive Wind Power Prediction Model Using
Constriction Factor Particle Swarm Optimization | The 9th IEEE Conference on Industrial Electronics and Applications
(ICIEA) 2014 | null | 10.1109/ICIEA.2014.6931336 | null | cs.CE cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Accurate forecasting is important for cost-effective and efficient monitoring
and control of the renewable energy based power generation. Wind based power is
one of the most difficult energy to predict accurately, due to the widely
varying and unpredictable nature of wind energy. Although Autoregressive (AR)
techniques have been widely used to create wind power models, they have shown
limited accuracy in forecasting, as well as difficulty in determining the
correct parameters for an optimized AR model. In this paper, Constriction
Factor Particle Swarm Optimization (CF-PSO) is employed to optimally determine
the parameters of an Autoregressive (AR) model for accurate prediction of the
wind power output behaviour. Appropriate lag order of the proposed model is
selected based on Akaike information criterion. The performance of the proposed
PSO based AR model is compared with four well-established approaches;
Forward-backward approach, Geometric lattice approach, Least-squares approach
and Yule-Walker approach, that are widely used for error minimization of the AR
model. To validate the proposed approach, real-life wind power data of
\textit{Capital Wind Farm} was obtained from Australian Energy Market Operator.
Experimental evaluation based on a number of different datasets demonstrate
that the performance of the AR model is significantly improved compared with
benchmark methods.
| [
{
"version": "v1",
"created": "Thu, 21 Aug 2014 00:46:51 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Anwar",
"Adnan",
""
],
[
"Mahmood",
"Abdun Naser",
""
]
] | TITLE: Enhanced Estimation of Autoregressive Wind Power Prediction Model Using
Constriction Factor Particle Swarm Optimization
ABSTRACT: Accurate forecasting is important for cost-effective and efficient monitoring
and control of the renewable energy based power generation. Wind based power is
one of the most difficult energy to predict accurately, due to the widely
varying and unpredictable nature of wind energy. Although Autoregressive (AR)
techniques have been widely used to create wind power models, they have shown
limited accuracy in forecasting, as well as difficulty in determining the
correct parameters for an optimized AR model. In this paper, Constriction
Factor Particle Swarm Optimization (CF-PSO) is employed to optimally determine
the parameters of an Autoregressive (AR) model for accurate prediction of the
wind power output behaviour. Appropriate lag order of the proposed model is
selected based on Akaike information criterion. The performance of the proposed
PSO based AR model is compared with four well-established approaches;
Forward-backward approach, Geometric lattice approach, Least-squares approach
and Yule-Walker approach, that are widely used for error minimization of the AR
model. To validate the proposed approach, real-life wind power data of
\textit{Capital Wind Farm} was obtained from Australian Energy Market Operator.
Experimental evaluation based on a number of different datasets demonstrate
that the performance of the AR model is significantly improved compared with
benchmark methods.
| no_new_dataset | 0.952838 |
1410.1606 | Xiang Xiang | Xiang Xiang, Minh Dao, Gregory D. Hager, Trac D. Tran | Hierarchical Sparse and Collaborative Low-Rank Representation for
Emotion Recognition | 5 pages, 5 figures; accepted to IEEE ICASSP 2015; programs available
at https://github.com/eglxiang/icassp15_emotion/ | null | 10.1109/ICASSP.2015.7178684 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we design a Collaborative-Hierarchical Sparse and Low-Rank
(C-HiSLR) model that is natural for recognizing human emotion in visual data.
Previous attempts require explicit expression components, which are often
unavailable and difficult to recover. Instead, our model exploits the lowrank
property over expressive facial frames and rescue inexact sparse
representations by incorporating group sparsity. For the CK+ dataset, C-HiSLR
on raw expressive faces performs as competitive as the Sparse Representation
based Classification (SRC) applied on manually prepared emotions. C-HiSLR
performs even better than SRC in terms of true positive rate.
| [
{
"version": "v1",
"created": "Tue, 7 Oct 2014 03:45:20 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Apr 2015 18:02:33 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Xiang",
"Xiang",
""
],
[
"Dao",
"Minh",
""
],
[
"Hager",
"Gregory D.",
""
],
[
"Tran",
"Trac D.",
""
]
] | TITLE: Hierarchical Sparse and Collaborative Low-Rank Representation for
Emotion Recognition
ABSTRACT: In this paper, we design a Collaborative-Hierarchical Sparse and Low-Rank
(C-HiSLR) model that is natural for recognizing human emotion in visual data.
Previous attempts require explicit expression components, which are often
unavailable and difficult to recover. Instead, our model exploits the lowrank
property over expressive facial frames and rescue inexact sparse
representations by incorporating group sparsity. For the CK+ dataset, C-HiSLR
on raw expressive faces performs as competitive as the Sparse Representation
based Classification (SRC) applied on manually prepared emotions. C-HiSLR
performs even better than SRC in terms of true positive rate.
| no_new_dataset | 0.955981 |
1410.5358 | Claudio Cusano | Claudio Cusano, Paolo Napoletano, Raimondo Schettini | Remote sensing image classification exploiting multiple kernel learning | Accepted for publication on the IEEE Geoscience and Remote Sensing
letters | null | 10.1109/LGRS.2015.2476365 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a strategy for land use classification which exploits Multiple
Kernel Learning (MKL) to automatically determine a suitable combination of a
set of features without requiring any heuristic knowledge about the
classification task. We present a novel procedure that allows MKL to achieve
good performance in the case of small training sets. Experimental results on
publicly available datasets demonstrate the feasibility of the proposed
approach.
| [
{
"version": "v1",
"created": "Mon, 20 Oct 2014 17:15:50 GMT"
},
{
"version": "v2",
"created": "Fri, 19 Dec 2014 13:17:27 GMT"
},
{
"version": "v3",
"created": "Tue, 1 Sep 2015 09:25:50 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Cusano",
"Claudio",
""
],
[
"Napoletano",
"Paolo",
""
],
[
"Schettini",
"Raimondo",
""
]
] | TITLE: Remote sensing image classification exploiting multiple kernel learning
ABSTRACT: We propose a strategy for land use classification which exploits Multiple
Kernel Learning (MKL) to automatically determine a suitable combination of a
set of features without requiring any heuristic knowledge about the
classification task. We present a novel procedure that allows MKL to achieve
good performance in the case of small training sets. Experimental results on
publicly available datasets demonstrate the feasibility of the proposed
approach.
| no_new_dataset | 0.946646 |
1410.5605 | Paolo Napoletano | Paolo Napoletano, Giuseppe Boccignone, Francesco Tisato | Attentive monitoring of multiple video streams driven by a Bayesian
foraging strategy | Accepted to IEEE Transactions on Image Processing | null | 10.1109/TIP.2015.2431438 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we shall consider the problem of deploying attention to subsets
of the video streams for collating the most relevant data and information of
interest related to a given task. We formalize this monitoring problem as a
foraging problem. We propose a probabilistic framework to model observer's
attentive behavior as the behavior of a forager. The forager, moment to moment,
focuses its attention on the most informative stream/camera, detects
interesting objects or activities, or switches to a more profitable stream. The
approach proposed here is suitable to be exploited for multi-stream video
summarization. Meanwhile, it can serve as a preliminary step for more
sophisticated video surveillance, e.g. activity and behavior analysis.
Experimental results achieved on the UCR Videoweb Activities Dataset, a
publicly available dataset, are presented to illustrate the utility of the
proposed technique.
| [
{
"version": "v1",
"created": "Tue, 21 Oct 2014 10:13:51 GMT"
},
{
"version": "v2",
"created": "Thu, 22 Jan 2015 11:21:47 GMT"
},
{
"version": "v3",
"created": "Mon, 27 Apr 2015 13:02:21 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Napoletano",
"Paolo",
""
],
[
"Boccignone",
"Giuseppe",
""
],
[
"Tisato",
"Francesco",
""
]
] | TITLE: Attentive monitoring of multiple video streams driven by a Bayesian
foraging strategy
ABSTRACT: In this paper we shall consider the problem of deploying attention to subsets
of the video streams for collating the most relevant data and information of
interest related to a given task. We formalize this monitoring problem as a
foraging problem. We propose a probabilistic framework to model observer's
attentive behavior as the behavior of a forager. The forager, moment to moment,
focuses its attention on the most informative stream/camera, detects
interesting objects or activities, or switches to a more profitable stream. The
approach proposed here is suitable to be exploited for multi-stream video
summarization. Meanwhile, it can serve as a preliminary step for more
sophisticated video surveillance, e.g. activity and behavior analysis.
Experimental results achieved on the UCR Videoweb Activities Dataset, a
publicly available dataset, are presented to illustrate the utility of the
proposed technique.
| new_dataset | 0.868994 |
1411.1215 | Blesson Varghese | Muhammed Asif Saleem, Blesson Varghese and Adam Barker | BigExcel: A Web-Based Framework for Exploring Big Data in Social
Sciences | 8 pages | Workshop of Big Humanities Data at the IEEE International
Conference on Big Data (IEEE BigData) 2014, Washington D. C., USA | 10.1109/BigData.2014.7004458 | null | cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper argues that there are three fundamental challenges that need to be
overcome in order to foster the adoption of big data technologies in
non-computer science related disciplines: addressing issues of accessibility of
such technologies for non-computer scientists, supporting the ad hoc
exploration of large data sets with minimal effort and the availability of
lightweight web-based frameworks for quick and easy analytics. In this paper,
we address the above three challenges through the development of 'BigExcel', a
three tier web-based framework for exploring big data to facilitate the
management of user interactions with large data sets, the construction of
queries to explore the data set and the management of the infrastructure. The
feasibility of BigExcel is demonstrated through two Yahoo Sandbox datasets. The
first dataset is the Yahoo Buzz Score data set we use for quantitatively
predicting trending technologies and the second is the Yahoo n-gram corpus we
use for qualitatively inferring the coverage of important events. A
demonstration of the BigExcel framework and source code is available at
http://bigdata.cs.st-andrews.ac.uk/projects/bigexcel-exploring-big-data-for-social-sciences/.
| [
{
"version": "v1",
"created": "Wed, 5 Nov 2014 10:22:27 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Saleem",
"Muhammed Asif",
""
],
[
"Varghese",
"Blesson",
""
],
[
"Barker",
"Adam",
""
]
] | TITLE: BigExcel: A Web-Based Framework for Exploring Big Data in Social
Sciences
ABSTRACT: This paper argues that there are three fundamental challenges that need to be
overcome in order to foster the adoption of big data technologies in
non-computer science related disciplines: addressing issues of accessibility of
such technologies for non-computer scientists, supporting the ad hoc
exploration of large data sets with minimal effort and the availability of
lightweight web-based frameworks for quick and easy analytics. In this paper,
we address the above three challenges through the development of 'BigExcel', a
three tier web-based framework for exploring big data to facilitate the
management of user interactions with large data sets, the construction of
queries to explore the data set and the management of the infrastructure. The
feasibility of BigExcel is demonstrated through two Yahoo Sandbox datasets. The
first dataset is the Yahoo Buzz Score data set we use for quantitatively
predicting trending technologies and the second is the Yahoo n-gram corpus we
use for qualitatively inferring the coverage of important events. A
demonstration of the BigExcel framework and source code is available at
http://bigdata.cs.st-andrews.ac.uk/projects/bigexcel-exploring-big-data-for-social-sciences/.
| no_new_dataset | 0.945801 |
1501.06180 | Shanshan Zhang | Shanshan Zhang, Christian Bauckhage, Dominik A. Klein, Armin B.
Cremers | Exploring Human Vision Driven Features for Pedestrian Detection | Accepted for publication in IEEE Transactions on Circuits and Systems
for Video Technology (TCSVT) | null | 10.1109/TCSVT.2015.2397199 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivated by the center-surround mechanism in the human visual attention
system, we propose to use average contrast maps for the challenge of pedestrian
detection in street scenes due to the observation that pedestrians indeed
exhibit discriminative contrast texture. Our main contributions are first to
design a local, statistical multi-channel descriptorin order to incorporate
both color and gradient information. Second, we introduce a multi-direction and
multi-scale contrast scheme based on grid-cells in order to integrate
expressive local variations. Contributing to the issue of selecting most
discriminative features for assessing and classification, we perform extensive
comparisons w.r.t. statistical descriptors, contrast measurements, and scale
structures. This way, we obtain reasonable results under various
configurations. Empirical findings from applying our optimized detector on the
INRIA and Caltech pedestrian datasets show that our features yield
state-of-the-art performance in pedestrian detection.
| [
{
"version": "v1",
"created": "Sun, 25 Jan 2015 16:52:41 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Zhang",
"Shanshan",
""
],
[
"Bauckhage",
"Christian",
""
],
[
"Klein",
"Dominik A.",
""
],
[
"Cremers",
"Armin B.",
""
]
] | TITLE: Exploring Human Vision Driven Features for Pedestrian Detection
ABSTRACT: Motivated by the center-surround mechanism in the human visual attention
system, we propose to use average contrast maps for the challenge of pedestrian
detection in street scenes due to the observation that pedestrians indeed
exhibit discriminative contrast texture. Our main contributions are first to
design a local, statistical multi-channel descriptorin order to incorporate
both color and gradient information. Second, we introduce a multi-direction and
multi-scale contrast scheme based on grid-cells in order to integrate
expressive local variations. Contributing to the issue of selecting most
discriminative features for assessing and classification, we perform extensive
comparisons w.r.t. statistical descriptors, contrast measurements, and scale
structures. This way, we obtain reasonable results under various
configurations. Empirical findings from applying our optimized detector on the
INRIA and Caltech pedestrian datasets show that our features yield
state-of-the-art performance in pedestrian detection.
| no_new_dataset | 0.94474 |
1502.06084 | Jieming Zhu | Jieming Zhu, Pinjia He, Zibin Zheng, Michael R. Lyu | A Privacy-Preserving QoS Prediction Framework for Web Service
Recommendation | This paper is published in IEEE International Conference on Web
Services (ICWS'15) | null | 10.1109/ICWS.2015.41 | null | cs.CR cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | QoS-based Web service recommendation has recently gained much attention for
providing a promising way to help users find high-quality services. To
facilitate such recommendations, existing studies suggest the use of
collaborative filtering techniques for personalized QoS prediction. These
approaches, by leveraging partially observed QoS values from users, can achieve
high accuracy of QoS predictions on the unobserved ones. However, the
requirement to collect users' QoS data likely puts user privacy at risk, thus
making them unwilling to contribute their usage data to a Web service
recommender system. As a result, privacy becomes a critical challenge in
developing practical Web service recommender systems. In this paper, we make
the first attempt to cope with the privacy concerns for Web service
recommendation. Specifically, we propose a simple yet effective
privacy-preserving framework by applying data obfuscation techniques, and
further develop two representative privacy-preserving QoS prediction approaches
under this framework. Evaluation results from a publicly-available QoS dataset
of real-world Web services demonstrate the feasibility and effectiveness of our
privacy-preserving QoS prediction approaches. We believe our work can serve as
a good starting point to inspire more research efforts on privacy-preserving
Web service recommendation.
| [
{
"version": "v1",
"created": "Sat, 21 Feb 2015 08:14:39 GMT"
},
{
"version": "v2",
"created": "Sat, 2 May 2015 04:43:00 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Zhu",
"Jieming",
""
],
[
"He",
"Pinjia",
""
],
[
"Zheng",
"Zibin",
""
],
[
"Lyu",
"Michael R.",
""
]
] | TITLE: A Privacy-Preserving QoS Prediction Framework for Web Service
Recommendation
ABSTRACT: QoS-based Web service recommendation has recently gained much attention for
providing a promising way to help users find high-quality services. To
facilitate such recommendations, existing studies suggest the use of
collaborative filtering techniques for personalized QoS prediction. These
approaches, by leveraging partially observed QoS values from users, can achieve
high accuracy of QoS predictions on the unobserved ones. However, the
requirement to collect users' QoS data likely puts user privacy at risk, thus
making them unwilling to contribute their usage data to a Web service
recommender system. As a result, privacy becomes a critical challenge in
developing practical Web service recommender systems. In this paper, we make
the first attempt to cope with the privacy concerns for Web service
recommendation. Specifically, we propose a simple yet effective
privacy-preserving framework by applying data obfuscation techniques, and
further develop two representative privacy-preserving QoS prediction approaches
under this framework. Evaluation results from a publicly-available QoS dataset
of real-world Web services demonstrate the feasibility and effectiveness of our
privacy-preserving QoS prediction approaches. We believe our work can serve as
a good starting point to inspire more research efforts on privacy-preserving
Web service recommendation.
| no_new_dataset | 0.945601 |
1502.07411 | Chunhua Shen | Fayao Liu, Chunhua Shen, Guosheng Lin, Ian Reid | Learning Depth from Single Monocular Images Using Deep Convolutional
Neural Fields | Appearing in IEEE T. Pattern Analysis and Machine Intelligence.
Journal version of arXiv:1411.6387 . Test code is available at
https://bitbucket.org/fayao/dcnf-fcsp | null | 10.1109/TPAMI.2015.2505283 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this article, we tackle the problem of depth estimation from single
monocular images. Compared with depth estimation using multiple images such as
stereo depth perception, depth from monocular images is much more challenging.
Prior work typically focuses on exploiting geometric priors or additional
sources of information, most using hand-crafted features. Recently, there is
mounting evidence that features from deep convolutional neural networks (CNN)
set new records for various vision applications. On the other hand, considering
the continuous characteristic of the depth values, depth estimations can be
naturally formulated as a continuous conditional random field (CRF) learning
problem. Therefore, here we present a deep convolutional neural field model for
estimating depths from single monocular images, aiming to jointly explore the
capacity of deep CNN and continuous CRF. In particular, we propose a deep
structured learning scheme which learns the unary and pairwise potentials of
continuous CRF in a unified deep CNN framework. We then further propose an
equally effective model based on fully convolutional networks and a novel
superpixel pooling method, which is $\sim 10$ times faster, to speedup the
patch-wise convolutions in the deep model. With this more efficient model, we
are able to design deeper networks to pursue better performance. Experiments on
both indoor and outdoor scene datasets demonstrate that the proposed method
outperforms state-of-the-art depth estimation approaches.
| [
{
"version": "v1",
"created": "Thu, 26 Feb 2015 01:26:22 GMT"
},
{
"version": "v2",
"created": "Thu, 19 Mar 2015 03:31:44 GMT"
},
{
"version": "v3",
"created": "Sat, 18 Apr 2015 10:13:39 GMT"
},
{
"version": "v4",
"created": "Wed, 30 Sep 2015 14:19:19 GMT"
},
{
"version": "v5",
"created": "Thu, 8 Oct 2015 06:02:00 GMT"
},
{
"version": "v6",
"created": "Wed, 25 Nov 2015 00:03:31 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Liu",
"Fayao",
""
],
[
"Shen",
"Chunhua",
""
],
[
"Lin",
"Guosheng",
""
],
[
"Reid",
"Ian",
""
]
] | TITLE: Learning Depth from Single Monocular Images Using Deep Convolutional
Neural Fields
ABSTRACT: In this article, we tackle the problem of depth estimation from single
monocular images. Compared with depth estimation using multiple images such as
stereo depth perception, depth from monocular images is much more challenging.
Prior work typically focuses on exploiting geometric priors or additional
sources of information, most using hand-crafted features. Recently, there is
mounting evidence that features from deep convolutional neural networks (CNN)
set new records for various vision applications. On the other hand, considering
the continuous characteristic of the depth values, depth estimations can be
naturally formulated as a continuous conditional random field (CRF) learning
problem. Therefore, here we present a deep convolutional neural field model for
estimating depths from single monocular images, aiming to jointly explore the
capacity of deep CNN and continuous CRF. In particular, we propose a deep
structured learning scheme which learns the unary and pairwise potentials of
continuous CRF in a unified deep CNN framework. We then further propose an
equally effective model based on fully convolutional networks and a novel
superpixel pooling method, which is $\sim 10$ times faster, to speedup the
patch-wise convolutions in the deep model. With this more efficient model, we
are able to design deeper networks to pursue better performance. Experiments on
both indoor and outdoor scene datasets demonstrate that the proposed method
outperforms state-of-the-art depth estimation approaches.
| no_new_dataset | 0.945751 |
1503.02318 | Arturo Deza | Arturo Deza, Devi Parikh | Understanding Image Virality | Pre-print, IEEE Conference on Computer Vision and Pattern Recognition
(CVPR), 2015 | null | 10.1109/CVPR.2015.7298791 | null | cs.SI cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Virality of online content on social networking websites is an important but
esoteric phenomenon often studied in fields like marketing, psychology and data
mining. In this paper we study viral images from a computer vision perspective.
We introduce three new image datasets from Reddit, and define a virality score
using Reddit metadata. We train classifiers with state-of-the-art image
features to predict virality of individual images, relative virality in pairs
of images, and the dominant topic of a viral image. We also compare machine
performance to human performance on these tasks. We find that computers perform
poorly with low level features, and high level information is critical for
predicting virality. We encode semantic information through relative
attributes. We identify the 5 key visual attributes that correlate with
virality. We create an attribute-based characterization of images that can
predict relative virality with 68.10% accuracy (SVM+Deep Relative Attributes)
-- better than humans at 60.12%. Finally, we study how human prediction of
image virality varies with different `contexts' in which the images are viewed,
such as the influence of neighbouring images, images recently viewed, as well
as the image title or caption. This work is a first step in understanding the
complex but important phenomenon of image virality. Our datasets and
annotations will be made publicly available.
| [
{
"version": "v1",
"created": "Sun, 8 Mar 2015 20:29:28 GMT"
},
{
"version": "v2",
"created": "Tue, 14 Apr 2015 18:04:29 GMT"
},
{
"version": "v3",
"created": "Tue, 26 May 2015 16:57:18 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Deza",
"Arturo",
""
],
[
"Parikh",
"Devi",
""
]
] | TITLE: Understanding Image Virality
ABSTRACT: Virality of online content on social networking websites is an important but
esoteric phenomenon often studied in fields like marketing, psychology and data
mining. In this paper we study viral images from a computer vision perspective.
We introduce three new image datasets from Reddit, and define a virality score
using Reddit metadata. We train classifiers with state-of-the-art image
features to predict virality of individual images, relative virality in pairs
of images, and the dominant topic of a viral image. We also compare machine
performance to human performance on these tasks. We find that computers perform
poorly with low level features, and high level information is critical for
predicting virality. We encode semantic information through relative
attributes. We identify the 5 key visual attributes that correlate with
virality. We create an attribute-based characterization of images that can
predict relative virality with 68.10% accuracy (SVM+Deep Relative Attributes)
-- better than humans at 60.12%. Finally, we study how human prediction of
image virality varies with different `contexts' in which the images are viewed,
such as the influence of neighbouring images, images recently viewed, as well
as the image title or caption. This work is a first step in understanding the
complex but important phenomenon of image virality. Our datasets and
annotations will be made publicly available.
| new_dataset | 0.966505 |
1504.00788 | Gianmarco De Francisci Morales | Muhammad Anis Uddin Nasir, Gianmarco De Francisci Morales, David
Garc\'ia-Soriano, Nicolas Kourtellis, Marco Serafini | The Power of Both Choices: Practical Load Balancing for Distributed
Stream Processing Engines | 31st IEEE International Conference on Data Engineering (ICDE), 2015 | null | 10.1109/ICDE.2015.7113279 | null | cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the problem of load balancing in distributed stream processing
engines, which is exacerbated in the presence of skew. We introduce Partial Key
Grouping (PKG), a new stream partitioning scheme that adapts the classical
"power of two choices" to a distributed streaming setting by leveraging two
novel techniques: key splitting and local load estimation. In so doing, it
achieves better load balancing than key grouping while being more scalable than
shuffle grouping. We test PKG on several large datasets, both real-world and
synthetic. Compared to standard hashing, PKG reduces the load imbalance by up
to several orders of magnitude, and often achieves nearly-perfect load balance.
This result translates into an improvement of up to 60% in throughput and up to
45% in latency when deployed on a real Storm cluster.
| [
{
"version": "v1",
"created": "Fri, 3 Apr 2015 09:24:22 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Nasir",
"Muhammad Anis Uddin",
""
],
[
"Morales",
"Gianmarco De Francisci",
""
],
[
"García-Soriano",
"David",
""
],
[
"Kourtellis",
"Nicolas",
""
],
[
"Serafini",
"Marco",
""
]
] | TITLE: The Power of Both Choices: Practical Load Balancing for Distributed
Stream Processing Engines
ABSTRACT: We study the problem of load balancing in distributed stream processing
engines, which is exacerbated in the presence of skew. We introduce Partial Key
Grouping (PKG), a new stream partitioning scheme that adapts the classical
"power of two choices" to a distributed streaming setting by leveraging two
novel techniques: key splitting and local load estimation. In so doing, it
achieves better load balancing than key grouping while being more scalable than
shuffle grouping. We test PKG on several large datasets, both real-world and
synthetic. Compared to standard hashing, PKG reduces the load imbalance by up
to several orders of magnitude, and often achieves nearly-perfect load balance.
This result translates into an improvement of up to 60% in throughput and up to
45% in latency when deployed on a real Storm cluster.
| no_new_dataset | 0.951369 |
1504.01000 | Alejandro Frery | Avik Bhattacharya, Arnab Muhuri, Shaunak De, Surendar Manickam,
Alejandro C. Frery | Modifying the Yamaguchi Four-Component Decomposition Scattering Powers
Using a Stochastic Distance | Accepted for publication in IEEE J-STARS (IEEE Journal of Selected
Topics in Applied Earth Observations and Remote Sensing) | null | 10.1109/JSTARS.2015.2420683 | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Model-based decompositions have gained considerable attention after the
initial work of Freeman and Durden. This decomposition which assumes the target
to be reflection symmetric was later relaxed in the Yamaguchi et al.
decomposition with the addition of the helix parameter. Since then many
decomposition have been proposed where either the scattering model was modified
to fit the data or the coherency matrix representing the second order
statistics of the full polarimetric data is rotated to fit the scattering
model. In this paper we propose to modify the Yamaguchi four-component
decomposition (Y4O) scattering powers using the concept of statistical
information theory for matrices. In order to achieve this modification we
propose a method to estimate the polarization orientation angle (OA) from
full-polarimetric SAR images using the Hellinger distance. In this method, the
OA is estimated by maximizing the Hellinger distance between the un-rotated and
the rotated $T_{33}$ and the $T_{22}$ components of the coherency matrix
$\mathbf{[T]}$. Then, the powers of the Yamaguchi four-component model-based
decomposition (Y4O) are modified using the maximum relative stochastic distance
between the $T_{33}$ and the $T_{22}$ components of the coherency matrix at the
estimated OA. The results show that the overall double-bounce powers over
rotated urban areas have significantly improved with the reduction of volume
powers. The percentage of pixels with negative powers have also decreased from
the Y4O decomposition. The proposed method is both qualitatively and
quantitatively compared with the results obtained from the Y4O and the Y4R
decompositions for a Radarsat-2 C-band San-Francisco dataset and an UAVSAR
L-band Hayward dataset.
| [
{
"version": "v1",
"created": "Sat, 4 Apr 2015 10:05:41 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Bhattacharya",
"Avik",
""
],
[
"Muhuri",
"Arnab",
""
],
[
"De",
"Shaunak",
""
],
[
"Manickam",
"Surendar",
""
],
[
"Frery",
"Alejandro C.",
""
]
] | TITLE: Modifying the Yamaguchi Four-Component Decomposition Scattering Powers
Using a Stochastic Distance
ABSTRACT: Model-based decompositions have gained considerable attention after the
initial work of Freeman and Durden. This decomposition which assumes the target
to be reflection symmetric was later relaxed in the Yamaguchi et al.
decomposition with the addition of the helix parameter. Since then many
decomposition have been proposed where either the scattering model was modified
to fit the data or the coherency matrix representing the second order
statistics of the full polarimetric data is rotated to fit the scattering
model. In this paper we propose to modify the Yamaguchi four-component
decomposition (Y4O) scattering powers using the concept of statistical
information theory for matrices. In order to achieve this modification we
propose a method to estimate the polarization orientation angle (OA) from
full-polarimetric SAR images using the Hellinger distance. In this method, the
OA is estimated by maximizing the Hellinger distance between the un-rotated and
the rotated $T_{33}$ and the $T_{22}$ components of the coherency matrix
$\mathbf{[T]}$. Then, the powers of the Yamaguchi four-component model-based
decomposition (Y4O) are modified using the maximum relative stochastic distance
between the $T_{33}$ and the $T_{22}$ components of the coherency matrix at the
estimated OA. The results show that the overall double-bounce powers over
rotated urban areas have significantly improved with the reduction of volume
powers. The percentage of pixels with negative powers have also decreased from
the Y4O decomposition. The proposed method is both qualitatively and
quantitatively compared with the results obtained from the Y4O and the Y4R
decompositions for a Radarsat-2 C-band San-Francisco dataset and an UAVSAR
L-band Hayward dataset.
| no_new_dataset | 0.952353 |
1504.03810 | Smitha M.L. | B.H. Shekar, Smitha M.L. | Text Localization in Video Using Multiscale Weber's Local Descriptor | IEEE SPICES, 2015 | null | 10.1109/SPICES.2015.7091559 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose a novel approach for detecting the text present in
videos and scene images based on the Multiscale Weber's Local Descriptor
(MWLD). Given an input video, the shots are identified and the key frames are
extracted based on their spatio-temporal relationship. From each key frame, we
detect the local region information using WLD with different radius and
neighborhood relationship of pixel values and hence obtained intensity enhanced
key frames at multiple scales. These multiscale WLD key frames are merged
together and then the horizontal gradients are computed using morphological
operations. The obtained results are then binarized and the false positives are
eliminated based on geometrical properties. Finally, we employ connected
component analysis and morphological dilation operation to determine the text
regions that aids in text localization. The experimental results obtained on
publicly available standard Hua, Horizontal-1 and Horizontal-2 video dataset
illustrate that the proposed method can accurately detect and localize texts of
various sizes, fonts and colors in videos.
| [
{
"version": "v1",
"created": "Wed, 15 Apr 2015 07:56:05 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Shekar",
"B. H.",
""
],
[
"L.",
"Smitha M.",
""
]
] | TITLE: Text Localization in Video Using Multiscale Weber's Local Descriptor
ABSTRACT: In this paper, we propose a novel approach for detecting the text present in
videos and scene images based on the Multiscale Weber's Local Descriptor
(MWLD). Given an input video, the shots are identified and the key frames are
extracted based on their spatio-temporal relationship. From each key frame, we
detect the local region information using WLD with different radius and
neighborhood relationship of pixel values and hence obtained intensity enhanced
key frames at multiple scales. These multiscale WLD key frames are merged
together and then the horizontal gradients are computed using morphological
operations. The obtained results are then binarized and the false positives are
eliminated based on geometrical properties. Finally, we employ connected
component analysis and morphological dilation operation to determine the text
regions that aids in text localization. The experimental results obtained on
publicly available standard Hua, Horizontal-1 and Horizontal-2 video dataset
illustrate that the proposed method can accurately detect and localize texts of
various sizes, fonts and colors in videos.
| no_new_dataset | 0.957198 |
1504.04663 | Jinxue Zhang | Jinxue Zhang, Rui Zhang, Jingchao Sun, Yanchao Zhang, Chi Zhang | TrueTop: A Sybil-Resilient System for User Influence Measurement on
Twitter | Accepted by IEEE/ACM Transactions on Networking. This is the Final
version | null | 10.1109/TNET.2015.2494059 | null | cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Influential users have great potential for accelerating information
dissemination and acquisition on Twitter. How to measure the influence of
Twitter users has attracted significant academic and industrial attention.
Existing influential measurement techniques, however, are vulnerable to sybil
users that are thriving on Twitter. Although sybil defenses for online social
networks have been extensively investigated, they commonly assume unique
mappings from human-established trust relationships to online social
associations and thus do not apply to Twitter where users can freely follow
each other. This paper presents TrueTop, the first sybil-resilient system to
measure the influence of Twitter users. TrueTop is firmly rooted in two
observations from real Twitter datasets. First, although non-sybil users may
incautiously follow strangers, they tend to be more careful and selective in
retweeting, replying to, and mentioning other Twitter users. Second,
influential users usually get much more retweets, replies, and mentions than
non-influential users. Detailed theoretical studies and synthetic simulations
show that TrueTop can generate very accurate influence measurement results and
also have strong resilience to sybil attacks.
| [
{
"version": "v1",
"created": "Sat, 18 Apr 2015 00:07:10 GMT"
},
{
"version": "v2",
"created": "Thu, 18 Jun 2015 00:35:04 GMT"
},
{
"version": "v3",
"created": "Tue, 20 Oct 2015 22:49:34 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Zhang",
"Jinxue",
""
],
[
"Zhang",
"Rui",
""
],
[
"Sun",
"Jingchao",
""
],
[
"Zhang",
"Yanchao",
""
],
[
"Zhang",
"Chi",
""
]
] | TITLE: TrueTop: A Sybil-Resilient System for User Influence Measurement on
Twitter
ABSTRACT: Influential users have great potential for accelerating information
dissemination and acquisition on Twitter. How to measure the influence of
Twitter users has attracted significant academic and industrial attention.
Existing influential measurement techniques, however, are vulnerable to sybil
users that are thriving on Twitter. Although sybil defenses for online social
networks have been extensively investigated, they commonly assume unique
mappings from human-established trust relationships to online social
associations and thus do not apply to Twitter where users can freely follow
each other. This paper presents TrueTop, the first sybil-resilient system to
measure the influence of Twitter users. TrueTop is firmly rooted in two
observations from real Twitter datasets. First, although non-sybil users may
incautiously follow strangers, they tend to be more careful and selective in
retweeting, replying to, and mentioning other Twitter users. Second,
influential users usually get much more retweets, replies, and mentions than
non-influential users. Detailed theoretical studies and synthetic simulations
show that TrueTop can generate very accurate influence measurement results and
also have strong resilience to sybil attacks.
| no_new_dataset | 0.943191 |
1505.04868 | Limin Wang | Limin Wang, Yu Qiao, Xiaoou Tang | Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors | IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
2015 | null | 10.1109/CVPR.2015.7299059 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Visual features are of vital importance for human action understanding in
videos. This paper presents a new video representation, called
trajectory-pooled deep-convolutional descriptor (TDD), which shares the merits
of both hand-crafted features and deep-learned features. Specifically, we
utilize deep architectures to learn discriminative convolutional feature maps,
and conduct trajectory-constrained pooling to aggregate these convolutional
features into effective descriptors. To enhance the robustness of TDDs, we
design two normalization methods to transform convolutional feature maps,
namely spatiotemporal normalization and channel normalization. The advantages
of our features come from (i) TDDs are automatically learned and contain high
discriminative capacity compared with those hand-crafted features; (ii) TDDs
take account of the intrinsic characteristics of temporal dimension and
introduce the strategies of trajectory-constrained sampling and pooling for
aggregating deep-learned features. We conduct experiments on two challenging
datasets: HMDB51 and UCF101. Experimental results show that TDDs outperform
previous hand-crafted features and deep-learned features. Our method also
achieves superior performance to the state of the art on these datasets (HMDB51
65.9%, UCF101 91.5%).
| [
{
"version": "v1",
"created": "Tue, 19 May 2015 04:36:42 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Wang",
"Limin",
""
],
[
"Qiao",
"Yu",
""
],
[
"Tang",
"Xiaoou",
""
]
] | TITLE: Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors
ABSTRACT: Visual features are of vital importance for human action understanding in
videos. This paper presents a new video representation, called
trajectory-pooled deep-convolutional descriptor (TDD), which shares the merits
of both hand-crafted features and deep-learned features. Specifically, we
utilize deep architectures to learn discriminative convolutional feature maps,
and conduct trajectory-constrained pooling to aggregate these convolutional
features into effective descriptors. To enhance the robustness of TDDs, we
design two normalization methods to transform convolutional feature maps,
namely spatiotemporal normalization and channel normalization. The advantages
of our features come from (i) TDDs are automatically learned and contain high
discriminative capacity compared with those hand-crafted features; (ii) TDDs
take account of the intrinsic characteristics of temporal dimension and
introduce the strategies of trajectory-constrained sampling and pooling for
aggregating deep-learned features. We conduct experiments on two challenging
datasets: HMDB51 and UCF101. Experimental results show that TDDs outperform
previous hand-crafted features and deep-learned features. Our method also
achieves superior performance to the state of the art on these datasets (HMDB51
65.9%, UCF101 91.5%).
| no_new_dataset | 0.946745 |
1506.08754 | Andrew Moran | Andrew Moran, Vijay Gadepally, Matthew Hubbell, Jeremy Kepner | Improving Big Data Visual Analytics with Interactive Virtual Reality | 6 pages, 8 figures, 2015 IEEE High Performance Extreme Computing
Conference (HPEC '15); corrected typos | null | 10.1109/HPEC.2015.7322473 | null | cs.HC cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For decades, the growth and volume of digital data collection has made it
challenging to digest large volumes of information and extract underlying
structure. Coined 'Big Data', massive amounts of information has quite often
been gathered inconsistently (e.g from many sources, of various forms, at
different rates, etc.). These factors impede the practices of not only
processing data, but also analyzing and displaying it in an efficient manner to
the user. Many efforts have been completed in the data mining and visual
analytics community to create effective ways to further improve analysis and
achieve the knowledge desired for better understanding. Our approach for
improved big data visual analytics is two-fold, focusing on both visualization
and interaction. Given geo-tagged information, we are exploring the benefits of
visualizing datasets in the original geospatial domain by utilizing a virtual
reality platform. After running proven analytics on the data, we intend to
represent the information in a more realistic 3D setting, where analysts can
achieve an enhanced situational awareness and rely on familiar perceptions to
draw in-depth conclusions on the dataset. In addition, developing a
human-computer interface that responds to natural user actions and inputs
creates a more intuitive environment. Tasks can be performed to manipulate the
dataset and allow users to dive deeper upon request, adhering to desired
demands and intentions. Due to the volume and popularity of social media, we
developed a 3D tool visualizing Twitter on MIT's campus for analysis. Utilizing
emerging technologies of today to create a fully immersive tool that promotes
visualization and interaction can help ease the process of understanding and
representing big data.
| [
{
"version": "v1",
"created": "Mon, 29 Jun 2015 17:50:20 GMT"
},
{
"version": "v2",
"created": "Tue, 6 Oct 2015 18:19:42 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Moran",
"Andrew",
""
],
[
"Gadepally",
"Vijay",
""
],
[
"Hubbell",
"Matthew",
""
],
[
"Kepner",
"Jeremy",
""
]
] | TITLE: Improving Big Data Visual Analytics with Interactive Virtual Reality
ABSTRACT: For decades, the growth and volume of digital data collection has made it
challenging to digest large volumes of information and extract underlying
structure. Coined 'Big Data', massive amounts of information has quite often
been gathered inconsistently (e.g from many sources, of various forms, at
different rates, etc.). These factors impede the practices of not only
processing data, but also analyzing and displaying it in an efficient manner to
the user. Many efforts have been completed in the data mining and visual
analytics community to create effective ways to further improve analysis and
achieve the knowledge desired for better understanding. Our approach for
improved big data visual analytics is two-fold, focusing on both visualization
and interaction. Given geo-tagged information, we are exploring the benefits of
visualizing datasets in the original geospatial domain by utilizing a virtual
reality platform. After running proven analytics on the data, we intend to
represent the information in a more realistic 3D setting, where analysts can
achieve an enhanced situational awareness and rely on familiar perceptions to
draw in-depth conclusions on the dataset. In addition, developing a
human-computer interface that responds to natural user actions and inputs
creates a more intuitive environment. Tasks can be performed to manipulate the
dataset and allow users to dive deeper upon request, adhering to desired
demands and intentions. Due to the volume and popularity of social media, we
developed a 3D tool visualizing Twitter on MIT's campus for analysis. Utilizing
emerging technologies of today to create a fully immersive tool that promotes
visualization and interaction can help ease the process of understanding and
representing big data.
| no_new_dataset | 0.942876 |
1507.00389 | Nasir Ahmad | Nasir Ahmad, Sybil Derrible, Tarsha Eason and Heriberto Cabezas | Using Fisher Information In Big Data | null | null | 10.1098/rsos.160582 | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this era of Big Data, proficient use of data mining is the key to capture
useful information from any dataset. As numerous data mining techniques make
use of information theory concepts, in this paper, we discuss how Fisher
information (FI) can be applied to analyze patterns in Big Data. The main
advantage of FI is its ability to combine multiple variables together to inform
us on the overall trends and stability of a system. It can therefore detect
whether a system is losing dynamic order and stability, which may serve as a
signal of an impending regime shift. In this work, we first provide a brief
overview of Fisher information theory, followed by a simple step-by-step
numerical example on how to compute FI. Finally, as a numerical demonstration,
we calculate the evolution of FI for GDP per capita (current US Dollar) and
total population of the USA from 1960 to 2013.
| [
{
"version": "v1",
"created": "Wed, 1 Jul 2015 23:23:18 GMT"
},
{
"version": "v2",
"created": "Mon, 20 Jul 2015 16:19:49 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Ahmad",
"Nasir",
""
],
[
"Derrible",
"Sybil",
""
],
[
"Eason",
"Tarsha",
""
],
[
"Cabezas",
"Heriberto",
""
]
] | TITLE: Using Fisher Information In Big Data
ABSTRACT: In this era of Big Data, proficient use of data mining is the key to capture
useful information from any dataset. As numerous data mining techniques make
use of information theory concepts, in this paper, we discuss how Fisher
information (FI) can be applied to analyze patterns in Big Data. The main
advantage of FI is its ability to combine multiple variables together to inform
us on the overall trends and stability of a system. It can therefore detect
whether a system is losing dynamic order and stability, which may serve as a
signal of an impending regime shift. In this work, we first provide a brief
overview of Fisher information theory, followed by a simple step-by-step
numerical example on how to compute FI. Finally, as a numerical demonstration,
we calculate the evolution of FI for GDP per capita (current US Dollar) and
total population of the USA from 1960 to 2013.
| no_new_dataset | 0.948202 |
1507.01269 | Tianpei Xie | Tianpei Xie, Nasser M. Nasrabadi and Alfred O. Hero III | Semi-supervised Multi-sensor Classification via Consensus-based
Multi-View Maximum Entropy Discrimination | 5 pages, 4 figures, Accepted in 40th IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP 15) | null | 10.1109/ICASSP.2015.7178308 | null | cs.IT cs.AI cs.LG math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we consider multi-sensor classification when there is a large
number of unlabeled samples. The problem is formulated under the multi-view
learning framework and a Consensus-based Multi-View Maximum Entropy
Discrimination (CMV-MED) algorithm is proposed. By iteratively maximizing the
stochastic agreement between multiple classifiers on the unlabeled dataset, the
algorithm simultaneously learns multiple high accuracy classifiers. We
demonstrate that our proposed method can yield improved performance over
previous multi-view learning approaches by comparing performance on three real
multi-sensor data sets.
| [
{
"version": "v1",
"created": "Sun, 5 Jul 2015 20:23:22 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Xie",
"Tianpei",
""
],
[
"Nasrabadi",
"Nasser M.",
""
],
[
"Hero",
"Alfred O.",
"III"
]
] | TITLE: Semi-supervised Multi-sensor Classification via Consensus-based
Multi-View Maximum Entropy Discrimination
ABSTRACT: In this paper, we consider multi-sensor classification when there is a large
number of unlabeled samples. The problem is formulated under the multi-view
learning framework and a Consensus-based Multi-View Maximum Entropy
Discrimination (CMV-MED) algorithm is proposed. By iteratively maximizing the
stochastic agreement between multiple classifiers on the unlabeled dataset, the
algorithm simultaneously learns multiple high accuracy classifiers. We
demonstrate that our proposed method can yield improved performance over
previous multi-view learning approaches by comparing performance on three real
multi-sensor data sets.
| no_new_dataset | 0.94743 |
1508.01055 | Roman Fedorov | Roman Fedorov, Alessandro Camerada, Piero Fraternali, Marco
Tagliasacchi | Estimating snow cover from publicly available images | submitted to IEEE Transactions on Multimedia | null | 10.1109/TMM.2016.2535356 | null | cs.MM cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we study the problem of estimating snow cover in mountainous
regions, that is, the spatial extent of the earth surface covered by snow. We
argue that publicly available visual content, in the form of user generated
photographs and image feeds from outdoor webcams, can both be leveraged as
additional measurement sources, complementing existing ground, satellite and
airborne sensor data. To this end, we describe two content acquisition and
processing pipelines that are tailored to such sources, addressing the specific
challenges posed by each of them, e.g., identifying the mountain peaks,
filtering out images taken in bad weather conditions, handling varying
illumination conditions. The final outcome is summarized in a snow cover index,
which indicates for a specific mountain and day of the year, the fraction of
visible area covered by snow, possibly at different elevations. We created a
manually labelled dataset to assess the accuracy of the image snow covered area
estimation, achieving 90.0% precision at 91.1% recall. In addition, we show
that seasonal trends related to air temperature are captured by the snow cover
index.
| [
{
"version": "v1",
"created": "Wed, 5 Aug 2015 12:46:26 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Fedorov",
"Roman",
""
],
[
"Camerada",
"Alessandro",
""
],
[
"Fraternali",
"Piero",
""
],
[
"Tagliasacchi",
"Marco",
""
]
] | TITLE: Estimating snow cover from publicly available images
ABSTRACT: In this paper we study the problem of estimating snow cover in mountainous
regions, that is, the spatial extent of the earth surface covered by snow. We
argue that publicly available visual content, in the form of user generated
photographs and image feeds from outdoor webcams, can both be leveraged as
additional measurement sources, complementing existing ground, satellite and
airborne sensor data. To this end, we describe two content acquisition and
processing pipelines that are tailored to such sources, addressing the specific
challenges posed by each of them, e.g., identifying the mountain peaks,
filtering out images taken in bad weather conditions, handling varying
illumination conditions. The final outcome is summarized in a snow cover index,
which indicates for a specific mountain and day of the year, the fraction of
visible area covered by snow, possibly at different elevations. We created a
manually labelled dataset to assess the accuracy of the image snow covered area
estimation, achieving 90.0% precision at 91.1% recall. In addition, we show
that seasonal trends related to air temperature are captured by the snow cover
index.
| new_dataset | 0.95995 |
1509.04186 | Gaurav Sharma | Gaurav Sharma, Frederic Jurie and Cordelia Schmid | Expanded Parts Model for Semantic Description of Humans in Still Images | Accepted for publication in IEEE Transactions on Pattern Analysis and
Machine Intelligence (TPAMI) | null | 10.1109/TPAMI.2016.2537325 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce an Expanded Parts Model (EPM) for recognizing human attributes
(e.g. young, short hair, wearing suit) and actions (e.g. running, jumping) in
still images. An EPM is a collection of part templates which are learnt
discriminatively to explain specific scale-space regions in the images (in
human centric coordinates). This is in contrast to current models which consist
of a relatively few (i.e. a mixture of) 'average' templates. EPM uses only a
subset of the parts to score an image and scores the image sparsely in space,
i.e. it ignores redundant and random background in an image. To learn our
model, we propose an algorithm which automatically mines parts and learns
corresponding discriminative templates together with their respective locations
from a large number of candidate parts. We validate our method on three recent
challenging datasets of human attributes and actions. We obtain convincing
qualitative and state-of-the-art quantitative results on the three datasets.
| [
{
"version": "v1",
"created": "Mon, 14 Sep 2015 16:33:04 GMT"
},
{
"version": "v2",
"created": "Thu, 25 Feb 2016 12:14:05 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Sharma",
"Gaurav",
""
],
[
"Jurie",
"Frederic",
""
],
[
"Schmid",
"Cordelia",
""
]
] | TITLE: Expanded Parts Model for Semantic Description of Humans in Still Images
ABSTRACT: We introduce an Expanded Parts Model (EPM) for recognizing human attributes
(e.g. young, short hair, wearing suit) and actions (e.g. running, jumping) in
still images. An EPM is a collection of part templates which are learnt
discriminatively to explain specific scale-space regions in the images (in
human centric coordinates). This is in contrast to current models which consist
of a relatively few (i.e. a mixture of) 'average' templates. EPM uses only a
subset of the parts to score an image and scores the image sparsely in space,
i.e. it ignores redundant and random background in an image. To learn our
model, we propose an algorithm which automatically mines parts and learns
corresponding discriminative templates together with their respective locations
from a large number of candidate parts. We validate our method on three recent
challenging datasets of human attributes and actions. We obtain convincing
qualitative and state-of-the-art quantitative results on the three datasets.
| no_new_dataset | 0.950549 |
1509.04619 | Hamid Tizhoosh | Zehra Camlica, H.R. Tizhoosh, Farzad Khalvati | Medical Image Classification via SVM using LBP Features from
Saliency-Based Folded Data | To appear in proceedings of The 14th International Conference on
Machine Learning and Applications (IEEE ICMLA 2015), Miami, Florida, USA,
2015 | null | 10.1109/ICMLA.2015.131 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Good results on image classification and retrieval using support vector
machines (SVM) with local binary patterns (LBPs) as features have been
extensively reported in the literature where an entire image is retrieved or
classified. In contrast, in medical imaging, not all parts of the image may be
equally significant or relevant to the image retrieval application at hand. For
instance, in lung x-ray image, the lung region may contain a tumour, hence
being highly significant whereas the surrounding area does not contain
significant information from medical diagnosis perspective. In this paper, we
propose to detect salient regions of images during training and fold the data
to reduce the effect of irrelevant regions. As a result, smaller image areas
will be used for LBP features calculation and consequently classification by
SVM. We use IRMA 2009 dataset with 14,410 x-ray images to verify the
performance of the proposed approach. The results demonstrate the benefits of
saliency-based folding approach that delivers comparable classification
accuracies with state-of-the-art but exhibits lower computational cost and
storage requirements, factors highly important for big data analytics.
| [
{
"version": "v1",
"created": "Tue, 15 Sep 2015 16:08:08 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Camlica",
"Zehra",
""
],
[
"Tizhoosh",
"H. R.",
""
],
[
"Khalvati",
"Farzad",
""
]
] | TITLE: Medical Image Classification via SVM using LBP Features from
Saliency-Based Folded Data
ABSTRACT: Good results on image classification and retrieval using support vector
machines (SVM) with local binary patterns (LBPs) as features have been
extensively reported in the literature where an entire image is retrieved or
classified. In contrast, in medical imaging, not all parts of the image may be
equally significant or relevant to the image retrieval application at hand. For
instance, in lung x-ray image, the lung region may contain a tumour, hence
being highly significant whereas the surrounding area does not contain
significant information from medical diagnosis perspective. In this paper, we
propose to detect salient regions of images during training and fold the data
to reduce the effect of irrelevant regions. As a result, smaller image areas
will be used for LBP features calculation and consequently classification by
SVM. We use IRMA 2009 dataset with 14,410 x-ray images to verify the
performance of the proposed approach. The results demonstrate the benefits of
saliency-based folding approach that delivers comparable classification
accuracies with state-of-the-art but exhibits lower computational cost and
storage requirements, factors highly important for big data analytics.
| no_new_dataset | 0.952662 |
1510.03125 | Chunhua Shen | Qichang Hu, Sakrapee Paisitkriangkrai, Chunhua Shen, Anton van den
Hengel, Fatih Porikli | Fast detection of multiple objects in traffic scenes with a common
detection framework | Appearing in IEEE Transactions on Intelligent Transportation Systems | null | 10.1109/TITS.2015.2496795 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Traffic scene perception (TSP) aims to real-time extract accurate on-road
environment information, which in- volves three phases: detection of objects of
interest, recognition of detected objects, and tracking of objects in motion.
Since recognition and tracking often rely on the results from detection, the
ability to detect objects of interest effectively plays a crucial role in TSP.
In this paper, we focus on three important classes of objects: traffic signs,
cars, and cyclists. We propose to detect all the three important objects in a
single learning based detection framework. The proposed framework consists of a
dense feature extractor and detectors of three important classes. Once the
dense features have been extracted, these features are shared with all
detectors. The advantage of using one common framework is that the detection
speed is much faster, since all dense features need only to be evaluated once
in the testing phase. In contrast, most previous works have designed specific
detectors using different features for each of these objects. To enhance the
feature robustness to noises and image deformations, we introduce spatially
pooled features as a part of aggregated channel features. In order to further
improve the generalization performance, we propose an object subcategorization
method as a means of capturing intra-class variation of objects. We
experimentally demonstrate the effectiveness and efficiency of the proposed
framework in three detection applications: traffic sign detection, car
detection, and cyclist detection. The proposed framework achieves the
competitive performance with state-of- the-art approaches on several benchmark
datasets.
| [
{
"version": "v1",
"created": "Mon, 12 Oct 2015 02:30:22 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Hu",
"Qichang",
""
],
[
"Paisitkriangkrai",
"Sakrapee",
""
],
[
"Shen",
"Chunhua",
""
],
[
"Hengel",
"Anton van den",
""
],
[
"Porikli",
"Fatih",
""
]
] | TITLE: Fast detection of multiple objects in traffic scenes with a common
detection framework
ABSTRACT: Traffic scene perception (TSP) aims to real-time extract accurate on-road
environment information, which in- volves three phases: detection of objects of
interest, recognition of detected objects, and tracking of objects in motion.
Since recognition and tracking often rely on the results from detection, the
ability to detect objects of interest effectively plays a crucial role in TSP.
In this paper, we focus on three important classes of objects: traffic signs,
cars, and cyclists. We propose to detect all the three important objects in a
single learning based detection framework. The proposed framework consists of a
dense feature extractor and detectors of three important classes. Once the
dense features have been extracted, these features are shared with all
detectors. The advantage of using one common framework is that the detection
speed is much faster, since all dense features need only to be evaluated once
in the testing phase. In contrast, most previous works have designed specific
detectors using different features for each of these objects. To enhance the
feature robustness to noises and image deformations, we introduce spatially
pooled features as a part of aggregated channel features. In order to further
improve the generalization performance, we propose an object subcategorization
method as a means of capturing intra-class variation of objects. We
experimentally demonstrate the effectiveness and efficiency of the proposed
framework in three detection applications: traffic sign detection, car
detection, and cyclist detection. The proposed framework achieves the
competitive performance with state-of- the-art approaches on several benchmark
datasets.
| no_new_dataset | 0.946051 |
1510.03336 | Subutai Ahmad | Alexander Lavin, Subutai Ahmad | Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly
Benchmark | 14th International Conference on Machine Learning and Applications
(IEEE ICMLA), 2015. Fixed typo in equation and formatting | null | 10.1109/ICMLA.2015.141 | null | cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | Much of the world's data is streaming, time-series data, where anomalies give
significant information in critical situations; examples abound in domains such
as finance, IT, security, medical, and energy. Yet detecting anomalies in
streaming data is a difficult task, requiring detectors to process data in
real-time, not batches, and learn while simultaneously making predictions.
There are no benchmarks to adequately test and score the efficacy of real-time
anomaly detectors. Here we propose the Numenta Anomaly Benchmark (NAB), which
attempts to provide a controlled and repeatable environment of open-source
tools to test and measure anomaly detection algorithms on streaming data. The
perfect detector would detect all anomalies as soon as possible, trigger no
false alarms, work with real-world time-series data across a variety of
domains, and automatically adapt to changing statistics. Rewarding these
characteristics is formalized in NAB, using a scoring algorithm designed for
streaming data. NAB evaluates detectors on a benchmark dataset with labeled,
real-world time-series data. We present these components, and give results and
analyses for several open source, commercially-used algorithms. The goal for
NAB is to provide a standard, open source framework with which the research
community can compare and evaluate different algorithms for detecting anomalies
in streaming data.
| [
{
"version": "v1",
"created": "Mon, 12 Oct 2015 15:30:34 GMT"
},
{
"version": "v2",
"created": "Tue, 13 Oct 2015 23:09:58 GMT"
},
{
"version": "v3",
"created": "Mon, 16 Nov 2015 20:52:44 GMT"
},
{
"version": "v4",
"created": "Tue, 17 Nov 2015 17:17:06 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Lavin",
"Alexander",
""
],
[
"Ahmad",
"Subutai",
""
]
] | TITLE: Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly
Benchmark
ABSTRACT: Much of the world's data is streaming, time-series data, where anomalies give
significant information in critical situations; examples abound in domains such
as finance, IT, security, medical, and energy. Yet detecting anomalies in
streaming data is a difficult task, requiring detectors to process data in
real-time, not batches, and learn while simultaneously making predictions.
There are no benchmarks to adequately test and score the efficacy of real-time
anomaly detectors. Here we propose the Numenta Anomaly Benchmark (NAB), which
attempts to provide a controlled and repeatable environment of open-source
tools to test and measure anomaly detection algorithms on streaming data. The
perfect detector would detect all anomalies as soon as possible, trigger no
false alarms, work with real-world time-series data across a variety of
domains, and automatically adapt to changing statistics. Rewarding these
characteristics is formalized in NAB, using a scoring algorithm designed for
streaming data. NAB evaluates detectors on a benchmark dataset with labeled,
real-world time-series data. We present these components, and give results and
analyses for several open source, commercially-used algorithms. The goal for
NAB is to provide a standard, open source framework with which the research
community can compare and evaluate different algorithms for detecting anomalies
in streaming data.
| no_new_dataset | 0.913599 |
1510.04039 | Nadine Kroher | Nadine Kroher, Emilia G\'omez | Automatic Transcription of Flamenco Singing from Polyphonic Music
Recordings | Submitted to the IEEE Transactions on Audio, Speech and Language
Processing | null | 10.1109/TASLP.2016.2531284 | null | cs.SD cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Automatic note-level transcription is considered one of the most challenging
tasks in music information retrieval. The specific case of flamenco singing
transcription poses a particular challenge due to its complex melodic
progressions, intonation inaccuracies, the use of a high degree of
ornamentation and the presence of guitar accompaniment. In this study, we
explore the limitations of existing state of the art transcription systems for
the case of flamenco singing and propose a specific solution for this genre: We
first extract the predominant melody and apply a novel contour filtering
process to eliminate segments of the pitch contour which originate from the
guitar accompaniment. We formulate a set of onset detection functions based on
volume and pitch characteristics to segment the resulting vocal pitch contour
into discrete note events. A quantised pitch label is assigned to each note
event by combining global pitch class probabilities with local pitch contour
statistics. The proposed system outperforms state of the art singing
transcription systems with respect to voicing accuracy, onset detection and
overall performance when evaluated on flamenco singing datasets.
| [
{
"version": "v1",
"created": "Wed, 14 Oct 2015 10:53:00 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Kroher",
"Nadine",
""
],
[
"Gómez",
"Emilia",
""
]
] | TITLE: Automatic Transcription of Flamenco Singing from Polyphonic Music
Recordings
ABSTRACT: Automatic note-level transcription is considered one of the most challenging
tasks in music information retrieval. The specific case of flamenco singing
transcription poses a particular challenge due to its complex melodic
progressions, intonation inaccuracies, the use of a high degree of
ornamentation and the presence of guitar accompaniment. In this study, we
explore the limitations of existing state of the art transcription systems for
the case of flamenco singing and propose a specific solution for this genre: We
first extract the predominant melody and apply a novel contour filtering
process to eliminate segments of the pitch contour which originate from the
guitar accompaniment. We formulate a set of onset detection functions based on
volume and pitch characteristics to segment the resulting vocal pitch contour
into discrete note events. A quantised pitch label is assigned to each note
event by combining global pitch class probabilities with local pitch contour
statistics. The proposed system outperforms state of the art singing
transcription systems with respect to voicing accuracy, onset detection and
overall performance when evaluated on flamenco singing datasets.
| no_new_dataset | 0.946399 |
1510.07320 | S. Hussain Raza | S. Hussain Raza, Matthias Grundmann, Irfan Essa | Geometric Context from Videos | Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference
on | null | 10.1109/CVPR.2013.396 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a novel algorithm for estimating the broad 3D geometric structure
of outdoor video scenes. Leveraging spatio-temporal video segmentation, we
decompose a dynamic scene captured by a video into geometric classes, based on
predictions made by region-classifiers that are trained on appearance and
motion features. By examining the homogeneity of the prediction, we combine
predictions across multiple segmentation hierarchy levels alleviating the need
to determine the granularity a priori. We built a novel, extensive dataset on
geometric context of video to evaluate our method, consisting of over 100
ground-truth annotated outdoor videos with over 20,000 frames. To further scale
beyond this dataset, we propose a semi-supervised learning framework to expand
the pool of labeled data with high confidence predictions obtained from
unlabeled data. Our system produces an accurate prediction of geometric context
of video achieving 96% accuracy across main geometric classes.
| [
{
"version": "v1",
"created": "Sun, 25 Oct 2015 22:58:30 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Raza",
"S. Hussain",
""
],
[
"Grundmann",
"Matthias",
""
],
[
"Essa",
"Irfan",
""
]
] | TITLE: Geometric Context from Videos
ABSTRACT: We present a novel algorithm for estimating the broad 3D geometric structure
of outdoor video scenes. Leveraging spatio-temporal video segmentation, we
decompose a dynamic scene captured by a video into geometric classes, based on
predictions made by region-classifiers that are trained on appearance and
motion features. By examining the homogeneity of the prediction, we combine
predictions across multiple segmentation hierarchy levels alleviating the need
to determine the granularity a priori. We built a novel, extensive dataset on
geometric context of video to evaluate our method, consisting of over 100
ground-truth annotated outdoor videos with over 20,000 frames. To further scale
beyond this dataset, we propose a semi-supervised learning framework to expand
the pool of labeled data with high confidence predictions obtained from
unlabeled data. Our system produces an accurate prediction of geometric context
of video achieving 96% accuracy across main geometric classes.
| new_dataset | 0.954223 |
1510.07323 | S. Hussain Raza | S. Hussain Raza, Ahmad Humayun, Matthias Grundmann, David Anderson,
Irfan Essa | Finding Temporally Consistent Occlusion Boundaries in Videos using
Geometric Context | Applications of Computer Vision (WACV), 2015 IEEE Winter Conference
on | null | 10.1109/WACV.2015.141 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present an algorithm for finding temporally consistent occlusion
boundaries in videos to support segmentation of dynamic scenes. We learn
occlusion boundaries in a pairwise Markov random field (MRF) framework. We
first estimate the probability of an spatio-temporal edge being an occlusion
boundary by using appearance, flow, and geometric features. Next, we enforce
occlusion boundary continuity in a MRF model by learning pairwise occlusion
probabilities using a random forest. Then, we temporally smooth boundaries to
remove temporal inconsistencies in occlusion boundary estimation. Our proposed
framework provides an efficient approach for finding temporally consistent
occlusion boundaries in video by utilizing causality, redundancy in videos, and
semantic layout of the scene. We have developed a dataset with fully annotated
ground-truth occlusion boundaries of over 30 videos ($5000 frames). This
dataset is used to evaluate temporal occlusion boundaries and provides a much
needed baseline for future studies. We perform experiments to demonstrate the
role of scene layout, and temporal information for occlusion reasoning in
dynamic scenes.
| [
{
"version": "v1",
"created": "Sun, 25 Oct 2015 23:20:38 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Raza",
"S. Hussain",
""
],
[
"Humayun",
"Ahmad",
""
],
[
"Grundmann",
"Matthias",
""
],
[
"Anderson",
"David",
""
],
[
"Essa",
"Irfan",
""
]
] | TITLE: Finding Temporally Consistent Occlusion Boundaries in Videos using
Geometric Context
ABSTRACT: We present an algorithm for finding temporally consistent occlusion
boundaries in videos to support segmentation of dynamic scenes. We learn
occlusion boundaries in a pairwise Markov random field (MRF) framework. We
first estimate the probability of an spatio-temporal edge being an occlusion
boundary by using appearance, flow, and geometric features. Next, we enforce
occlusion boundary continuity in a MRF model by learning pairwise occlusion
probabilities using a random forest. Then, we temporally smooth boundaries to
remove temporal inconsistencies in occlusion boundary estimation. Our proposed
framework provides an efficient approach for finding temporally consistent
occlusion boundaries in video by utilizing causality, redundancy in videos, and
semantic layout of the scene. We have developed a dataset with fully annotated
ground-truth occlusion boundaries of over 30 videos ($5000 frames). This
dataset is used to evaluate temporal occlusion boundaries and provides a much
needed baseline for future studies. We perform experiments to demonstrate the
role of scene layout, and temporal information for occlusion reasoning in
dynamic scenes.
| new_dataset | 0.955026 |
1511.04240 | Dylan Campbell | Dylan Campbell, Lars Petersson | An Adaptive Data Representation for Robust Point-Set Registration and
Merging | Manuscript in press 2015 IEEE International Conference on Computer
Vision | null | 10.1109/ICCV.2015.488 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a framework for rigid point-set registration and merging
using a robust continuous data representation. Our point-set representation is
constructed by training a one-class support vector machine with a Gaussian
radial basis function kernel and subsequently approximating the output function
with a Gaussian mixture model. We leverage the representation's sparse
parametrisation and robustness to noise, outliers and occlusions in an
efficient registration algorithm that minimises the L2 distance between our
support vector--parametrised Gaussian mixtures. In contrast, existing
techniques, such as Iterative Closest Point and Gaussian mixture approaches,
manifest a narrower region of convergence and are less robust to occlusions and
missing data, as demonstrated in the evaluation on a range of 2D and 3D
datasets. Finally, we present a novel algorithm, GMMerge, that parsimoniously
and equitably merges aligned mixture models, allowing the framework to be used
for reconstruction and mapping.
| [
{
"version": "v1",
"created": "Fri, 13 Nov 2015 11:23:40 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Campbell",
"Dylan",
""
],
[
"Petersson",
"Lars",
""
]
] | TITLE: An Adaptive Data Representation for Robust Point-Set Registration and
Merging
ABSTRACT: This paper presents a framework for rigid point-set registration and merging
using a robust continuous data representation. Our point-set representation is
constructed by training a one-class support vector machine with a Gaussian
radial basis function kernel and subsequently approximating the output function
with a Gaussian mixture model. We leverage the representation's sparse
parametrisation and robustness to noise, outliers and occlusions in an
efficient registration algorithm that minimises the L2 distance between our
support vector--parametrised Gaussian mixtures. In contrast, existing
techniques, such as Iterative Closest Point and Gaussian mixture approaches,
manifest a narrower region of convergence and are less robust to occlusions and
missing data, as demonstrated in the evaluation on a range of 2D and 3D
datasets. Finally, we present a novel algorithm, GMMerge, that parsimoniously
and equitably merges aligned mixture models, allowing the framework to be used
for reconstruction and mapping.
| no_new_dataset | 0.949716 |
1512.04042 | Shixia Liu | Shixia Liu, Jialun Yin, Xiting Wang, Weiwei Cui, Kelei Cao, Jian Pei | Online Visual Analytics of Text Streams | IEEE TVCG 2016 | null | 10.1109/TVCG.2015.2509990 | null | cs.IR cs.HC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present an online visual analytics approach to helping users explore and
understand hierarchical topic evolution in high-volume text streams. The key
idea behind this approach is to identify representative topics in incoming
documents and align them with the existing representative topics that they
immediately follow (in time). To this end, we learn a set of streaming tree
cuts from topic trees based on user-selected focus nodes. A dynamic Bayesian
network model has been developed to derive the tree cuts in the incoming topic
trees to balance the fitness of each tree cut and the smoothness between
adjacent tree cuts. By connecting the corresponding topics at different times,
we are able to provide an overview of the evolving hierarchical topics. A
sedimentation-based visualization has been designed to enable the interactive
analysis of streaming text data from global patterns to local details. We
evaluated our method on real-world datasets and the results are generally
favorable.
| [
{
"version": "v1",
"created": "Sun, 13 Dec 2015 12:22:21 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Liu",
"Shixia",
""
],
[
"Yin",
"Jialun",
""
],
[
"Wang",
"Xiting",
""
],
[
"Cui",
"Weiwei",
""
],
[
"Cao",
"Kelei",
""
],
[
"Pei",
"Jian",
""
]
] | TITLE: Online Visual Analytics of Text Streams
ABSTRACT: We present an online visual analytics approach to helping users explore and
understand hierarchical topic evolution in high-volume text streams. The key
idea behind this approach is to identify representative topics in incoming
documents and align them with the existing representative topics that they
immediately follow (in time). To this end, we learn a set of streaming tree
cuts from topic trees based on user-selected focus nodes. A dynamic Bayesian
network model has been developed to derive the tree cuts in the incoming topic
trees to balance the fitness of each tree cut and the smoothness between
adjacent tree cuts. By connecting the corresponding topics at different times,
we are able to provide an overview of the evolving hierarchical topics. A
sedimentation-based visualization has been designed to enable the interactive
analysis of streaming text data from global patterns to local details. We
evaluated our method on real-world datasets and the results are generally
favorable.
| no_new_dataset | 0.952574 |
1512.04133 | George Cushen | George Cushen | A Person Re-Identification System For Mobile Devices | Appearing in Proceedings of the 11th IEEE/ACM International
Conference on Signal Image Technology & Internet Systems (SITIS 2015) | null | 10.1109/SITIS.2015.96 | null | cs.CV cs.CR cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Person re-identification is a critical security task for recognizing a person
across spatially disjoint sensors. Previous work can be computationally
intensive and is mainly based on low-level cues extracted from RGB data and
implemented on a PC for a fixed sensor network (such as traditional CCTV). We
present a practical and efficient framework for mobile devices (such as smart
phones and robots) where high-level semantic soft biometrics are extracted from
RGB and depth data. By combining these cues, our approach attempts to provide
robustness to noise, illumination, and minor variations in clothing. This
mobile approach may be particularly useful for the identification of persons in
areas ill-served by fixed sensors or for tasks where the sensor position and
direction need to dynamically adapt to a target. Results on the BIWI dataset
are preliminary but encouraging. Further evaluation and demonstration of the
system will be available on our website.
| [
{
"version": "v1",
"created": "Sun, 13 Dec 2015 22:33:17 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Cushen",
"George",
""
]
] | TITLE: A Person Re-Identification System For Mobile Devices
ABSTRACT: Person re-identification is a critical security task for recognizing a person
across spatially disjoint sensors. Previous work can be computationally
intensive and is mainly based on low-level cues extracted from RGB data and
implemented on a PC for a fixed sensor network (such as traditional CCTV). We
present a practical and efficient framework for mobile devices (such as smart
phones and robots) where high-level semantic soft biometrics are extracted from
RGB and depth data. By combining these cues, our approach attempts to provide
robustness to noise, illumination, and minor variations in clothing. This
mobile approach may be particularly useful for the identification of persons in
areas ill-served by fixed sensors or for tasks where the sensor position and
direction need to dynamically adapt to a target. Results on the BIWI dataset
are preliminary but encouraging. Further evaluation and demonstration of the
system will be available on our website.
| no_new_dataset | 0.946448 |
1601.07471 | Vinay Venkataraman | Vinay Venkataraman, Pavan Turaga | Shape Distributions of Nonlinear Dynamical Systems for Video-based
Inference | IEEE Transactions on Pattern Analysis and Machine Intelligence | null | 10.1109/TPAMI.2016.2533388 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a shape-theoretic framework for dynamical analysis of
nonlinear dynamical systems which appear frequently in several video-based
inference tasks. Traditional approaches to dynamical modeling have included
linear and nonlinear methods with their respective drawbacks. A novel approach
we propose is the use of descriptors of the shape of the dynamical attractor as
a feature representation of nature of dynamics. The proposed framework has two
main advantages over traditional approaches: a) representation of the dynamical
system is derived directly from the observational data, without any inherent
assumptions, and b) the proposed features show stability under different
time-series lengths where traditional dynamical invariants fail. We illustrate
our idea using nonlinear dynamical models such as Lorenz and Rossler systems,
where our feature representations (shape distribution) support our hypothesis
that the local shape of the reconstructed phase space can be used as a
discriminative feature. Our experimental analyses on these models also indicate
that the proposed framework show stability for different time-series lengths,
which is useful when the available number of samples are small/variable. The
specific applications of interest in this paper are: 1) activity recognition
using motion capture and RGBD sensors, 2) activity quality assessment for
applications in stroke rehabilitation, and 3) dynamical scene classification.
We provide experimental validation through action and gesture recognition
experiments on motion capture and Kinect datasets. In all these scenarios, we
show experimental evidence of the favorable properties of the proposed
representation.
| [
{
"version": "v1",
"created": "Wed, 27 Jan 2016 18:01:38 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Venkataraman",
"Vinay",
""
],
[
"Turaga",
"Pavan",
""
]
] | TITLE: Shape Distributions of Nonlinear Dynamical Systems for Video-based
Inference
ABSTRACT: This paper presents a shape-theoretic framework for dynamical analysis of
nonlinear dynamical systems which appear frequently in several video-based
inference tasks. Traditional approaches to dynamical modeling have included
linear and nonlinear methods with their respective drawbacks. A novel approach
we propose is the use of descriptors of the shape of the dynamical attractor as
a feature representation of nature of dynamics. The proposed framework has two
main advantages over traditional approaches: a) representation of the dynamical
system is derived directly from the observational data, without any inherent
assumptions, and b) the proposed features show stability under different
time-series lengths where traditional dynamical invariants fail. We illustrate
our idea using nonlinear dynamical models such as Lorenz and Rossler systems,
where our feature representations (shape distribution) support our hypothesis
that the local shape of the reconstructed phase space can be used as a
discriminative feature. Our experimental analyses on these models also indicate
that the proposed framework show stability for different time-series lengths,
which is useful when the available number of samples are small/variable. The
specific applications of interest in this paper are: 1) activity recognition
using motion capture and RGBD sensors, 2) activity quality assessment for
applications in stroke rehabilitation, and 3) dynamical scene classification.
We provide experimental validation through action and gesture recognition
experiments on motion capture and Kinect datasets. In all these scenarios, we
show experimental evidence of the favorable properties of the proposed
representation.
| no_new_dataset | 0.950365 |
1602.06401 | Nikos Bikakis | Nikos Bikakis, John Liagouris, Maria Krommyda, George Papastefanatos,
Timos Sellis | graphVizdb: A Scalable Platform for Interactive Large Graph
Visualization | 32nd IEEE International Conference on Data Engineering (ICDE '16) | null | 10.1109/ICDE.2016.7498340 | null | cs.HC cs.DB cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a novel platform for the interactive visualization of very large
graphs. The platform enables the user to interact with the visualized graph in
a way that is very similar to the exploration of maps at multiple levels. Our
approach involves an offline preprocessing phase that builds the layout of the
graph by assigning coordinates to its nodes with respect to a Euclidean plane.
The respective points are indexed with a spatial data structure, i.e., an
R-tree, and stored in a database. Multiple abstraction layers of the graph
based on various criteria are also created offline, and they are indexed
similarly so that the user can explore the dataset at different levels of
granularity, depending on her particular needs. Then, our system translates
user operations into simple and very efficient spatial operations (i.e., window
queries) in the backend. This technique allows for a fine-grained access to
very large graphs with extremely low latency and memory requirements and
without compromising the functionality of the tool. Our web-based prototype
supports three main operations: (1) interactive navigation, (2) multi-level
exploration, and (3) keyword search on the graph metadata.
| [
{
"version": "v1",
"created": "Sat, 20 Feb 2016 12:49:09 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Bikakis",
"Nikos",
""
],
[
"Liagouris",
"John",
""
],
[
"Krommyda",
"Maria",
""
],
[
"Papastefanatos",
"George",
""
],
[
"Sellis",
"Timos",
""
]
] | TITLE: graphVizdb: A Scalable Platform for Interactive Large Graph
Visualization
ABSTRACT: We present a novel platform for the interactive visualization of very large
graphs. The platform enables the user to interact with the visualized graph in
a way that is very similar to the exploration of maps at multiple levels. Our
approach involves an offline preprocessing phase that builds the layout of the
graph by assigning coordinates to its nodes with respect to a Euclidean plane.
The respective points are indexed with a spatial data structure, i.e., an
R-tree, and stored in a database. Multiple abstraction layers of the graph
based on various criteria are also created offline, and they are indexed
similarly so that the user can explore the dataset at different levels of
granularity, depending on her particular needs. Then, our system translates
user operations into simple and very efficient spatial operations (i.e., window
queries) in the backend. This technique allows for a fine-grained access to
very large graphs with extremely low latency and memory requirements and
without compromising the functionality of the tool. Our web-based prototype
supports three main operations: (1) interactive navigation, (2) multi-level
exploration, and (3) keyword search on the graph metadata.
| no_new_dataset | 0.947672 |
1603.07936 | Subhajit Sidhanta Subhajit Sidhanta | Subhajit Sidhanta, Wojciech Golab, and Supratik Mukhopadhyay | OptEx: A Deadline-Aware Cost Optimization Model for Spark | 10 pages, IEEE CCGrid 2016 | null | 10.1109/CCGrid.2016.10 | null | cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present OptEx, a closed-form model of job execution on Apache Spark, a
popular parallel processing engine. To the best of our knowledge, OptEx is the
first work that analytically models job completion time on Spark. The model can
be used to estimate the completion time of a given Spark job on a cloud, with
respect to the size of the input dataset, the number of iterations, the number
of nodes comprising the underlying cluster. Experimental results demonstrate
that OptEx yields a mean relative error of 6% in estimating the job completion
time. Furthermore, the model can be applied for estimating the cost optimal
cluster composition for running a given Spark job on a cloud under a completion
deadline specified in the SLO (i.e., Service Level Objective). We show
experimentally that OptEx is able to correctly estimate the cost optimal
cluster composition for running a given Spark job under an SLO deadline with an
accuracy of 98%.
| [
{
"version": "v1",
"created": "Fri, 25 Mar 2016 15:28:56 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Sidhanta",
"Subhajit",
""
],
[
"Golab",
"Wojciech",
""
],
[
"Mukhopadhyay",
"Supratik",
""
]
] | TITLE: OptEx: A Deadline-Aware Cost Optimization Model for Spark
ABSTRACT: We present OptEx, a closed-form model of job execution on Apache Spark, a
popular parallel processing engine. To the best of our knowledge, OptEx is the
first work that analytically models job completion time on Spark. The model can
be used to estimate the completion time of a given Spark job on a cloud, with
respect to the size of the input dataset, the number of iterations, the number
of nodes comprising the underlying cluster. Experimental results demonstrate
that OptEx yields a mean relative error of 6% in estimating the job completion
time. Furthermore, the model can be applied for estimating the cost optimal
cluster composition for running a given Spark job on a cloud under a completion
deadline specified in the SLO (i.e., Service Level Objective). We show
experimentally that OptEx is able to correctly estimate the cost optimal
cluster composition for running a given Spark job under an SLO deadline with an
accuracy of 98%.
| no_new_dataset | 0.943034 |
1604.04906 | Johannes Stegmaier | Johannes Stegmaier, Julian Arz, Benjamin Schott, Jens C. Otte, Andrei
Kobitski, G. Ulrich Nienhaus, Uwe Str\"ahle, Peter Sanders, Ralf Mikut | Generating Semi-Synthetic Validation Benchmarks for Embryomics | Accepted publication at IEEE International Symposium on Biomedical
Imaging: From Nano to Macro (ISBI), 2016 | null | 10.1109/ISBI.2016.7493359 | null | cs.CV q-bio.CB q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Systematic validation is an essential part of algorithm development. The
enormous dataset sizes and the complexity observed in many recent time-resolved
3D fluorescence microscopy imaging experiments, however, prohibit a
comprehensive manual ground truth generation. Moreover, existing simulated
benchmarks in this field are often too simple or too specialized to
sufficiently validate the observed image analysis problems. We present a new
semi-synthetic approach to generate realistic 3D+t benchmarks that combines
challenging cellular movement dynamics of real embryos with simulated
fluorescent nuclei and artificial image distortions including various
parametrizable options like cell numbers, acquisition deficiencies or multiview
simulations. We successfully applied the approach to simulate the development
of a zebrafish embryo with thousands of cells over 14 hours of its early
existence.
| [
{
"version": "v1",
"created": "Sun, 17 Apr 2016 18:29:48 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Stegmaier",
"Johannes",
""
],
[
"Arz",
"Julian",
""
],
[
"Schott",
"Benjamin",
""
],
[
"Otte",
"Jens C.",
""
],
[
"Kobitski",
"Andrei",
""
],
[
"Nienhaus",
"G. Ulrich",
""
],
[
"Strähle",
"Uwe",
""
],
[
"Sanders",
"Peter",
""
],
[
"Mikut",
"Ralf",
""
]
] | TITLE: Generating Semi-Synthetic Validation Benchmarks for Embryomics
ABSTRACT: Systematic validation is an essential part of algorithm development. The
enormous dataset sizes and the complexity observed in many recent time-resolved
3D fluorescence microscopy imaging experiments, however, prohibit a
comprehensive manual ground truth generation. Moreover, existing simulated
benchmarks in this field are often too simple or too specialized to
sufficiently validate the observed image analysis problems. We present a new
semi-synthetic approach to generate realistic 3D+t benchmarks that combines
challenging cellular movement dynamics of real embryos with simulated
fluorescent nuclei and artificial image distortions including various
parametrizable options like cell numbers, acquisition deficiencies or multiview
simulations. We successfully applied the approach to simulate the development
of a zebrafish embryo with thousands of cells over 14 hours of its early
existence.
| no_new_dataset | 0.944944 |
1605.03498 | Micael Carvalho | Micael Carvalho, Matthieu Cord, Sandra Avila, Nicolas Thome, Eduardo
Valle | Deep Neural Networks Under Stress | This article corresponds to the accepted version at IEEE ICIP 2016.
We will link the DOI as soon as it is available | null | 10.1109/ICIP.2016.7533200 | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years, deep architectures have been used for transfer learning with
state-of-the-art performance in many datasets. The properties of their features
remain, however, largely unstudied under the transfer perspective. In this
work, we present an extensive analysis of the resiliency of feature vectors
extracted from deep models, with special focus on the trade-off between
performance and compression rate. By introducing perturbations to image
descriptions extracted from a deep convolutional neural network, we change
their precision and number of dimensions, measuring how it affects the final
score. We show that deep features are more robust to these disturbances when
compared to classical approaches, achieving a compression rate of 98.4%, while
losing only 0.88% of their original score for Pascal VOC 2007.
| [
{
"version": "v1",
"created": "Wed, 11 May 2016 16:22:23 GMT"
},
{
"version": "v2",
"created": "Mon, 23 May 2016 08:34:50 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Carvalho",
"Micael",
""
],
[
"Cord",
"Matthieu",
""
],
[
"Avila",
"Sandra",
""
],
[
"Thome",
"Nicolas",
""
],
[
"Valle",
"Eduardo",
""
]
] | TITLE: Deep Neural Networks Under Stress
ABSTRACT: In recent years, deep architectures have been used for transfer learning with
state-of-the-art performance in many datasets. The properties of their features
remain, however, largely unstudied under the transfer perspective. In this
work, we present an extensive analysis of the resiliency of feature vectors
extracted from deep models, with special focus on the trade-off between
performance and compression rate. By introducing perturbations to image
descriptions extracted from a deep convolutional neural network, we change
their precision and number of dimensions, measuring how it affects the final
score. We show that deep features are more robust to these disturbances when
compared to classical approaches, achieving a compression rate of 98.4%, while
losing only 0.88% of their original score for Pascal VOC 2007.
| no_new_dataset | 0.944638 |
1605.04227 | Madhav Nimishakavi Mr | Madhav Nimishakavi, Uday Singh Saini and Partha Talukdar | Relation Schema Induction using Tensor Factorization with Side
Information | Proceedings of the 2016 Conference on Empirical Methods in Natural
Language Processing, November 2016. Austin, TX | null | null | null | cs.IR cs.CL cs.DB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Given a set of documents from a specific domain (e.g., medical research
journals), how do we automatically build a Knowledge Graph (KG) for that
domain? Automatic identification of relations and their schemas, i.e., type
signature of arguments of relations (e.g., undergo(Patient, Surgery)), is an
important first step towards this goal. We refer to this problem as Relation
Schema Induction (RSI). In this paper, we propose Schema Induction using
Coupled Tensor Factorization (SICTF), a novel tensor factorization method for
relation schema induction. SICTF factorizes Open Information Extraction
(OpenIE) triples extracted from a domain corpus along with additional side
information in a principled way to induce relation schemas. To the best of our
knowledge, this is the first application of tensor factorization for the RSI
problem. Through extensive experiments on multiple real-world datasets, we find
that SICTF is not only more accurate than state-of-the-art baselines, but also
significantly faster (about 14x faster).
| [
{
"version": "v1",
"created": "Thu, 12 May 2016 19:44:04 GMT"
},
{
"version": "v2",
"created": "Tue, 17 May 2016 04:57:09 GMT"
},
{
"version": "v3",
"created": "Wed, 16 Nov 2016 04:53:42 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Nimishakavi",
"Madhav",
""
],
[
"Saini",
"Uday Singh",
""
],
[
"Talukdar",
"Partha",
""
]
] | TITLE: Relation Schema Induction using Tensor Factorization with Side
Information
ABSTRACT: Given a set of documents from a specific domain (e.g., medical research
journals), how do we automatically build a Knowledge Graph (KG) for that
domain? Automatic identification of relations and their schemas, i.e., type
signature of arguments of relations (e.g., undergo(Patient, Surgery)), is an
important first step towards this goal. We refer to this problem as Relation
Schema Induction (RSI). In this paper, we propose Schema Induction using
Coupled Tensor Factorization (SICTF), a novel tensor factorization method for
relation schema induction. SICTF factorizes Open Information Extraction
(OpenIE) triples extracted from a domain corpus along with additional side
information in a principled way to induce relation schemas. To the best of our
knowledge, this is the first application of tensor factorization for the RSI
problem. Through extensive experiments on multiple real-world datasets, we find
that SICTF is not only more accurate than state-of-the-art baselines, but also
significantly faster (about 14x faster).
| no_new_dataset | 0.950824 |
1606.06900 | Panupong Pasupat | Panupong Pasupat and Percy Liang | Inferring Logical Forms From Denotations | Published at the Association for Computational Linguistics (ACL)
conference, 2016 | null | null | null | cs.CL cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A core problem in learning semantic parsers from denotations is picking out
consistent logical forms--those that yield the correct denotation--from a
combinatorially large space. To control the search space, previous work relied
on restricted set of rules, which limits expressivity. In this paper, we
consider a much more expressive class of logical forms, and show how to use
dynamic programming to efficiently represent the complete set of consistent
logical forms. Expressivity also introduces many more spurious logical forms
which are consistent with the correct denotation but do not represent the
meaning of the utterance. To address this, we generate fictitious worlds and
use crowdsourced denotations on these worlds to filter out spurious logical
forms. On the WikiTableQuestions dataset, we increase the coverage of
answerable questions from 53.5% to 76%, and the additional crowdsourced
supervision lets us rule out 92.1% of spurious logical forms.
| [
{
"version": "v1",
"created": "Wed, 22 Jun 2016 11:07:43 GMT"
},
{
"version": "v2",
"created": "Tue, 15 Nov 2016 21:24:08 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Pasupat",
"Panupong",
""
],
[
"Liang",
"Percy",
""
]
] | TITLE: Inferring Logical Forms From Denotations
ABSTRACT: A core problem in learning semantic parsers from denotations is picking out
consistent logical forms--those that yield the correct denotation--from a
combinatorially large space. To control the search space, previous work relied
on restricted set of rules, which limits expressivity. In this paper, we
consider a much more expressive class of logical forms, and show how to use
dynamic programming to efficiently represent the complete set of consistent
logical forms. Expressivity also introduces many more spurious logical forms
which are consistent with the correct denotation but do not represent the
meaning of the utterance. To address this, we generate fictitious worlds and
use crowdsourced denotations on these worlds to filter out spurious logical
forms. On the WikiTableQuestions dataset, we increase the coverage of
answerable questions from 53.5% to 76%, and the additional crowdsourced
supervision lets us rule out 92.1% of spurious logical forms.
| no_new_dataset | 0.947478 |
1607.06190 | Uwe Aickelin | Christopher Roadknight, Durga Suryanarayanan, Uwe Aickelin, John
Scholefield, Lindy Durrant | An ensemble of machine learning and anti-learning methods for predicting
tumour patient survival rates | IEEE International Conference on Data Science and Advanced Analytics
(IEEE DSAA'2015), pp. 1-8, 2015. arXiv admin note: text overlap with
arXiv:1307.1599, arXiv:1409.0788 | null | 10.1109/DSAA.2015.7344863 | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper primarily addresses a dataset relating to cellular, chemical and
physical conditions of patients gathered at the time they are operated upon to
remove colorectal tumours. This data provides a unique insight into the
biochemical and immunological status of patients at the point of tumour removal
along with information about tumour classification and post-operative survival.
The relationship between severity of tumour, based on TNM staging, and survival
is still unclear for patients with TNM stage 2 and 3 tumours. We ask whether it
is possible to predict survival rate more accurately using a selection of
machine learning techniques applied to subsets of data to gain a deeper
understanding of the relationships between a patient's biochemical markers and
survival. We use a range of feature selection and single classification
techniques to predict the 5 year survival rate of TNM stage 2 and 3 patients
which initially produces less than ideal results. The performance of each model
individually is then compared with subsets of the data where agreement is
reached for multiple models. This novel method of selective ensembling
demonstrates that significant improvements in model accuracy on an unseen test
set can be achieved for patients where agreement between models is achieved.
Finally we point at a possible method to identify whether a patients prognosis
can be accurately predicted or not.
| [
{
"version": "v1",
"created": "Thu, 21 Jul 2016 04:57:16 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Roadknight",
"Christopher",
""
],
[
"Suryanarayanan",
"Durga",
""
],
[
"Aickelin",
"Uwe",
""
],
[
"Scholefield",
"John",
""
],
[
"Durrant",
"Lindy",
""
]
] | TITLE: An ensemble of machine learning and anti-learning methods for predicting
tumour patient survival rates
ABSTRACT: This paper primarily addresses a dataset relating to cellular, chemical and
physical conditions of patients gathered at the time they are operated upon to
remove colorectal tumours. This data provides a unique insight into the
biochemical and immunological status of patients at the point of tumour removal
along with information about tumour classification and post-operative survival.
The relationship between severity of tumour, based on TNM staging, and survival
is still unclear for patients with TNM stage 2 and 3 tumours. We ask whether it
is possible to predict survival rate more accurately using a selection of
machine learning techniques applied to subsets of data to gain a deeper
understanding of the relationships between a patient's biochemical markers and
survival. We use a range of feature selection and single classification
techniques to predict the 5 year survival rate of TNM stage 2 and 3 patients
which initially produces less than ideal results. The performance of each model
individually is then compared with subsets of the data where agreement is
reached for multiple models. This novel method of selective ensembling
demonstrates that significant improvements in model accuracy on an unseen test
set can be achieved for patients where agreement between models is achieved.
Finally we point at a possible method to identify whether a patients prognosis
can be accurately predicted or not.
| no_new_dataset | 0.943764 |
1607.08220 | Mostofa Patwary | Md. Mostofa Ali Patwary, Nadathur Rajagopalan Satish, Narayanan
Sundaram, Jialin Liu, Peter Sadowski, Evan Racah, Suren Byna, Craig Tull,
Wahid Bhimji, Prabhat, Pradeep Dubey | PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed
Architectures | 11 pages in PANDA: Extreme Scale Parallel K-Nearest Neighbor on
Distributed Architectures, Md. Mostofa Ali Patwary et.al., IEEE International
Parallel and Distributed Processing Symposium (IPDPS), 2016 | null | 10.1109/IPDPS.2016.57 | null | cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Computing $k$-Nearest Neighbors (KNN) is one of the core kernels used in many
machine learning, data mining and scientific computing applications. Although
kd-tree based $O(\log n)$ algorithms have been proposed for computing KNN, due
to its inherent sequentiality, linear algorithms are being used in practice.
This limits the applicability of such methods to millions of data points, with
limited scalability for Big Data analytics challenges in the scientific domain.
In this paper, we present parallel and highly optimized kd-tree based KNN
algorithms (both construction and querying) suitable for distributed
architectures. Our algorithm includes novel approaches for pruning search space
and improving load balancing and partitioning among nodes and threads. Using
TB-sized datasets from three science applications: astrophysics, plasma
physics, and particle physics, we show that our implementation can construct
kd-tree of 189 billion particles in 48 seconds on utilizing $\sim$50,000 cores.
We also demonstrate computation of KNN of 19 billion queries in 12 seconds. We
demonstrate almost linear speedup both for shared and distributed memory
computers. Our algorithms outperforms earlier implementations by more than
order of magnitude; thereby radically improving the applicability of our
implementation to state-of-the-art Big Data analytics problems. In addition, we
showcase performance and scalability on the recently released Intel Xeon Phi
processor showing that our algorithm scales well even on massively parallel
architectures.
| [
{
"version": "v1",
"created": "Wed, 27 Jul 2016 19:13:07 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Patwary",
"Md. Mostofa Ali",
""
],
[
"Satish",
"Nadathur Rajagopalan",
""
],
[
"Sundaram",
"Narayanan",
""
],
[
"Liu",
"Jialin",
""
],
[
"Sadowski",
"Peter",
""
],
[
"Racah",
"Evan",
""
],
[
"Byna",
"Suren",
""
],
[
"Tull",
"Craig",
""
],
[
"Bhimji",
"Wahid",
""
],
[
"Prabhat",
"",
""
],
[
"Dubey",
"Pradeep",
""
]
] | TITLE: PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed
Architectures
ABSTRACT: Computing $k$-Nearest Neighbors (KNN) is one of the core kernels used in many
machine learning, data mining and scientific computing applications. Although
kd-tree based $O(\log n)$ algorithms have been proposed for computing KNN, due
to its inherent sequentiality, linear algorithms are being used in practice.
This limits the applicability of such methods to millions of data points, with
limited scalability for Big Data analytics challenges in the scientific domain.
In this paper, we present parallel and highly optimized kd-tree based KNN
algorithms (both construction and querying) suitable for distributed
architectures. Our algorithm includes novel approaches for pruning search space
and improving load balancing and partitioning among nodes and threads. Using
TB-sized datasets from three science applications: astrophysics, plasma
physics, and particle physics, we show that our implementation can construct
kd-tree of 189 billion particles in 48 seconds on utilizing $\sim$50,000 cores.
We also demonstrate computation of KNN of 19 billion queries in 12 seconds. We
demonstrate almost linear speedup both for shared and distributed memory
computers. Our algorithms outperforms earlier implementations by more than
order of magnitude; thereby radically improving the applicability of our
implementation to state-of-the-art Big Data analytics problems. In addition, we
showcase performance and scalability on the recently released Intel Xeon Phi
processor showing that our algorithm scales well even on massively parallel
architectures.
| no_new_dataset | 0.945349 |
1608.00990 | Sebastian Liem | Sebastian Liem | Barrett: out-of-core processing of MultiNest output | 5 pages, 1 figure | null | null | null | stat.CO physics.data-an | http://creativecommons.org/licenses/by/4.0/ | Barrett is a Python package for processing and visualising statistical
inferences made using the nested sampling algorithm MultiNest. The main
differential feature from competitors are full out-of-core processing allowing
barrett to handle arbitrarily large datasets. This is achieved by using the
HDF5 data format.
| [
{
"version": "v1",
"created": "Tue, 2 Aug 2016 20:22:25 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Liem",
"Sebastian",
""
]
] | TITLE: Barrett: out-of-core processing of MultiNest output
ABSTRACT: Barrett is a Python package for processing and visualising statistical
inferences made using the nested sampling algorithm MultiNest. The main
differential feature from competitors are full out-of-core processing allowing
barrett to handle arbitrarily large datasets. This is achieved by using the
HDF5 data format.
| no_new_dataset | 0.940188 |
1608.02755 | Jordi Pont-Tuset | Kevis-Kokitsi Maninis and Jordi Pont-Tuset and Pablo Arbel\'aez and
Luc Van Gool | Convolutional Oriented Boundaries | ECCV 2016 Camera Ready | null | 10.1007/978-3-319-46448-0_35 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present Convolutional Oriented Boundaries (COB), which produces multiscale
oriented contours and region hierarchies starting from generic image
classification Convolutional Neural Networks (CNNs). COB is computationally
efficient, because it requires a single CNN forward pass for contour detection
and it uses a novel sparse boundary representation for hierarchical
segmentation; it gives a significant leap in performance over the
state-of-the-art, and it generalizes very well to unseen categories and
datasets. Particularly, we show that learning to estimate not only contour
strength but also orientation provides more accurate results. We perform
extensive experiments on BSDS, PASCAL Context, PASCAL Segmentation, and
MS-COCO, showing that COB provides state-of-the-art contours, region
hierarchies, and object proposals in all datasets.
| [
{
"version": "v1",
"created": "Tue, 9 Aug 2016 10:37:52 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Maninis",
"Kevis-Kokitsi",
""
],
[
"Pont-Tuset",
"Jordi",
""
],
[
"Arbeláez",
"Pablo",
""
],
[
"Van Gool",
"Luc",
""
]
] | TITLE: Convolutional Oriented Boundaries
ABSTRACT: We present Convolutional Oriented Boundaries (COB), which produces multiscale
oriented contours and region hierarchies starting from generic image
classification Convolutional Neural Networks (CNNs). COB is computationally
efficient, because it requires a single CNN forward pass for contour detection
and it uses a novel sparse boundary representation for hierarchical
segmentation; it gives a significant leap in performance over the
state-of-the-art, and it generalizes very well to unseen categories and
datasets. Particularly, we show that learning to estimate not only contour
strength but also orientation provides more accurate results. We perform
extensive experiments on BSDS, PASCAL Context, PASCAL Segmentation, and
MS-COCO, showing that COB provides state-of-the-art contours, region
hierarchies, and object proposals in all datasets.
| no_new_dataset | 0.954052 |
1609.01103 | Kevis-Kokitsi Maninis | Kevis-Kokitsi Maninis and Jordi Pont-Tuset and Pablo Arbel\'aez and
Luc Van Gool | Deep Retinal Image Understanding | MICCAI 2016 Camera Ready | null | 10.1007/978-3-319-46723-8_17 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents Deep Retinal Image Understanding (DRIU), a unified
framework of retinal image analysis that provides both retinal vessel and optic
disc segmentation. We make use of deep Convolutional Neural Networks (CNNs),
which have proven revolutionary in other fields of computer vision such as
object detection and image classification, and we bring their power to the
study of eye fundus images. DRIU uses a base network architecture on which two
set of specialized layers are trained to solve both the retinal vessel and
optic disc segmentation. We present experimental validation, both qualitative
and quantitative, in four public datasets for these tasks. In all of them, DRIU
presents super-human performance, that is, it shows results more consistent
with a gold standard than a second human annotator used as control.
| [
{
"version": "v1",
"created": "Mon, 5 Sep 2016 11:20:30 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Maninis",
"Kevis-Kokitsi",
""
],
[
"Pont-Tuset",
"Jordi",
""
],
[
"Arbeláez",
"Pablo",
""
],
[
"Van Gool",
"Luc",
""
]
] | TITLE: Deep Retinal Image Understanding
ABSTRACT: This paper presents Deep Retinal Image Understanding (DRIU), a unified
framework of retinal image analysis that provides both retinal vessel and optic
disc segmentation. We make use of deep Convolutional Neural Networks (CNNs),
which have proven revolutionary in other fields of computer vision such as
object detection and image classification, and we bring their power to the
study of eye fundus images. DRIU uses a base network architecture on which two
set of specialized layers are trained to solve both the retinal vessel and
optic disc segmentation. We present experimental validation, both qualitative
and quantitative, in four public datasets for these tasks. In all of them, DRIU
presents super-human performance, that is, it shows results more consistent
with a gold standard than a second human annotator used as control.
| no_new_dataset | 0.952926 |
1609.05871 | Avisek Lahiri | Avisek Lahiri, Abhijit Guha Roy, Debdoot Sheet, Prabir Kumar Biswas | Deep Neural Ensemble for Retinal Vessel Segmentation in Fundus Images
towards Achieving Label-free Angiography | Accepted as a conference paper at IEEE EMBC, 2016 | null | 10.1109/EMBC.2016.7590955 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Automated segmentation of retinal blood vessels in label-free fundus images
entails a pivotal role in computed aided diagnosis of ophthalmic pathologies,
viz., diabetic retinopathy, hypertensive disorders and cardiovascular diseases.
The challenge remains active in medical image analysis research due to varied
distribution of blood vessels, which manifest variations in their dimensions of
physical appearance against a noisy background.
In this paper we formulate the segmentation challenge as a classification
task. Specifically, we employ unsupervised hierarchical feature learning using
ensemble of two level of sparsely trained denoised stacked autoencoder. First
level training with bootstrap samples ensures decoupling and second level
ensemble formed by different network architectures ensures architectural
revision. We show that ensemble training of auto-encoders fosters diversity in
learning dictionary of visual kernels for vessel segmentation. SoftMax
classifier is used for fine tuning each member auto-encoder and multiple
strategies are explored for 2-level fusion of ensemble members. On DRIVE
dataset, we achieve maximum average accuracy of 95.33\% with an impressively
low standard deviation of 0.003 and Kappa agreement coefficient of 0.708 .
Comparison with other major algorithms substantiates the high efficacy of our
model.
| [
{
"version": "v1",
"created": "Mon, 19 Sep 2016 19:11:05 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Lahiri",
"Avisek",
""
],
[
"Roy",
"Abhijit Guha",
""
],
[
"Sheet",
"Debdoot",
""
],
[
"Biswas",
"Prabir Kumar",
""
]
] | TITLE: Deep Neural Ensemble for Retinal Vessel Segmentation in Fundus Images
towards Achieving Label-free Angiography
ABSTRACT: Automated segmentation of retinal blood vessels in label-free fundus images
entails a pivotal role in computed aided diagnosis of ophthalmic pathologies,
viz., diabetic retinopathy, hypertensive disorders and cardiovascular diseases.
The challenge remains active in medical image analysis research due to varied
distribution of blood vessels, which manifest variations in their dimensions of
physical appearance against a noisy background.
In this paper we formulate the segmentation challenge as a classification
task. Specifically, we employ unsupervised hierarchical feature learning using
ensemble of two level of sparsely trained denoised stacked autoencoder. First
level training with bootstrap samples ensures decoupling and second level
ensemble formed by different network architectures ensures architectural
revision. We show that ensemble training of auto-encoders fosters diversity in
learning dictionary of visual kernels for vessel segmentation. SoftMax
classifier is used for fine tuning each member auto-encoder and multiple
strategies are explored for 2-level fusion of ensemble members. On DRIVE
dataset, we achieve maximum average accuracy of 95.33\% with an impressively
low standard deviation of 0.003 and Kappa agreement coefficient of 0.708 .
Comparison with other major algorithms substantiates the high efficacy of our
model.
| no_new_dataset | 0.949669 |
1609.09471 | Lovedeep Gondara | Lovedeep Gondara | Classifier comparison using precision | Extended version | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | New proposed models are often compared to state-of-the-art using statistical
significance testing. Literature is scarce for classifier comparison using
metrics other than accuracy. We present a survey of statistical methods that
can be used for classifier comparison using precision, accounting for
inter-precision correlation arising from use of same dataset. Comparisons are
made using per-class precision and methods presented to test global null
hypothesis of an overall model comparison. Comparisons are extended to multiple
multi-class classifiers and to models using cross validation or its variants.
Partial Bayesian update to precision is introduced when population prevalence
of a class is known. Applications to compare deep architectures are studied.
| [
{
"version": "v1",
"created": "Thu, 29 Sep 2016 19:19:29 GMT"
},
{
"version": "v2",
"created": "Wed, 16 Nov 2016 01:43:21 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Gondara",
"Lovedeep",
""
]
] | TITLE: Classifier comparison using precision
ABSTRACT: New proposed models are often compared to state-of-the-art using statistical
significance testing. Literature is scarce for classifier comparison using
metrics other than accuracy. We present a survey of statistical methods that
can be used for classifier comparison using precision, accounting for
inter-precision correlation arising from use of same dataset. Comparisons are
made using per-class precision and methods presented to test global null
hypothesis of an overall model comparison. Comparisons are extended to multiple
multi-class classifiers and to models using cross validation or its variants.
Partial Bayesian update to precision is introduced when population prevalence
of a class is known. Applications to compare deep architectures are studied.
| no_new_dataset | 0.943191 |
1610.01706 | Chunhua Shen | Yuanzhouhan Cao, Chunhua Shen, Heng Tao Shen | Exploiting Depth from Single Monocular Images for Object Detection and
Semantic Segmentation | 14 pages. Accepted to IEEE T. Image Processing | null | 10.1109/TIP.2016.2621673 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Augmenting RGB data with measured depth has been shown to improve the
performance of a range of tasks in computer vision including object detection
and semantic segmentation. Although depth sensors such as the Microsoft Kinect
have facilitated easy acquisition of such depth information, the vast majority
of images used in vision tasks do not contain depth information. In this paper,
we show that augmenting RGB images with estimated depth can also improve the
accuracy of both object detection and semantic segmentation. Specifically, we
first exploit the recent success of depth estimation from monocular images and
learn a deep depth estimation model. Then we learn deep depth features from the
estimated depth and combine with RGB features for object detection and semantic
segmentation. Additionally, we propose an RGB-D semantic segmentation method
which applies a multi-task training scheme: semantic label prediction and depth
value regression. We test our methods on several datasets and demonstrate that
incorporating information from estimated depth improves the performance of
object detection and semantic segmentation remarkably.
| [
{
"version": "v1",
"created": "Thu, 6 Oct 2016 01:30:46 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Cao",
"Yuanzhouhan",
""
],
[
"Shen",
"Chunhua",
""
],
[
"Shen",
"Heng Tao",
""
]
] | TITLE: Exploiting Depth from Single Monocular Images for Object Detection and
Semantic Segmentation
ABSTRACT: Augmenting RGB data with measured depth has been shown to improve the
performance of a range of tasks in computer vision including object detection
and semantic segmentation. Although depth sensors such as the Microsoft Kinect
have facilitated easy acquisition of such depth information, the vast majority
of images used in vision tasks do not contain depth information. In this paper,
we show that augmenting RGB images with estimated depth can also improve the
accuracy of both object detection and semantic segmentation. Specifically, we
first exploit the recent success of depth estimation from monocular images and
learn a deep depth estimation model. Then we learn deep depth features from the
estimated depth and combine with RGB features for object detection and semantic
segmentation. Additionally, we propose an RGB-D semantic segmentation method
which applies a multi-task training scheme: semantic label prediction and depth
value regression. We test our methods on several datasets and demonstrate that
incorporating information from estimated depth improves the performance of
object detection and semantic segmentation remarkably.
| no_new_dataset | 0.952794 |
1611.02064 | Avijit Dasgupta | Avijit Dasgupta and Sonam Singh | A Fully Convolutional Neural Network based Structured Prediction
Approach Towards the Retinal Vessel Segmentation | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Automatic segmentation of retinal blood vessels from fundus images plays an
important role in the computer aided diagnosis of retinal diseases. The task of
blood vessel segmentation is challenging due to the extreme variations in
morphology of the vessels against noisy background. In this paper, we formulate
the segmentation task as a multi-label inference task and utilize the implicit
advantages of the combination of convolutional neural networks and structured
prediction. Our proposed convolutional neural network based model achieves
strong performance and significantly outperforms the state-of-the-art for
automatic retinal blood vessel segmentation on DRIVE dataset with 95.33%
accuracy and 0.974 AUC score.
| [
{
"version": "v1",
"created": "Mon, 7 Nov 2016 14:16:18 GMT"
},
{
"version": "v2",
"created": "Wed, 16 Nov 2016 09:21:40 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Dasgupta",
"Avijit",
""
],
[
"Singh",
"Sonam",
""
]
] | TITLE: A Fully Convolutional Neural Network based Structured Prediction
Approach Towards the Retinal Vessel Segmentation
ABSTRACT: Automatic segmentation of retinal blood vessels from fundus images plays an
important role in the computer aided diagnosis of retinal diseases. The task of
blood vessel segmentation is challenging due to the extreme variations in
morphology of the vessels against noisy background. In this paper, we formulate
the segmentation task as a multi-label inference task and utilize the implicit
advantages of the combination of convolutional neural networks and structured
prediction. Our proposed convolutional neural network based model achieves
strong performance and significantly outperforms the state-of-the-art for
automatic retinal blood vessel segmentation on DRIVE dataset with 95.33%
accuracy and 0.974 AUC score.
| no_new_dataset | 0.951863 |
1611.05132 | Cheng Tang | Cheng Tang, Claire Monteleoni | Convergence rate of stochastic k-means | arXiv admin note: substantial text overlap with arXiv:1610.04900 | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We analyze online \cite{BottouBengio} and mini-batch \cite{Sculley} $k$-means
variants. Both scale up the widely used $k$-means algorithm via stochastic
approximation, and have become popular for large-scale clustering and
unsupervised feature learning. We show, for the first time, that starting with
any initial solution, they converge to a "local optimum" at rate
$O(\frac{1}{t})$ (in terms of the $k$-means objective) under general
conditions. In addition, we show if the dataset is clusterable, when
initialized with a simple and scalable seeding algorithm, mini-batch $k$-means
converges to an optimal $k$-means solution at rate $O(\frac{1}{t})$ with high
probability. The $k$-means objective is non-convex and non-differentiable: we
exploit ideas from recent work on stochastic gradient descent for non-convex
problems \cite{ge:sgd_tensor, balsubramani13} by providing a novel
characterization of the trajectory of $k$-means algorithm on its solution
space, and circumvent the non-differentiability problem via geometric insights
about $k$-means update.
| [
{
"version": "v1",
"created": "Wed, 16 Nov 2016 03:28:08 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Tang",
"Cheng",
""
],
[
"Monteleoni",
"Claire",
""
]
] | TITLE: Convergence rate of stochastic k-means
ABSTRACT: We analyze online \cite{BottouBengio} and mini-batch \cite{Sculley} $k$-means
variants. Both scale up the widely used $k$-means algorithm via stochastic
approximation, and have become popular for large-scale clustering and
unsupervised feature learning. We show, for the first time, that starting with
any initial solution, they converge to a "local optimum" at rate
$O(\frac{1}{t})$ (in terms of the $k$-means objective) under general
conditions. In addition, we show if the dataset is clusterable, when
initialized with a simple and scalable seeding algorithm, mini-batch $k$-means
converges to an optimal $k$-means solution at rate $O(\frac{1}{t})$ with high
probability. The $k$-means objective is non-convex and non-differentiable: we
exploit ideas from recent work on stochastic gradient descent for non-convex
problems \cite{ge:sgd_tensor, balsubramani13} by providing a novel
characterization of the trajectory of $k$-means algorithm on its solution
space, and circumvent the non-differentiability problem via geometric insights
about $k$-means update.
| no_new_dataset | 0.941547 |
1611.05138 | Shuangfei Zhai | Shuangfei Zhai, Hui Wu, Abhishek Kumar, Yu Cheng, Yongxi Lu, Zhongfei
Zhang, Rogerio Feris | S3Pool: Pooling with Stochastic Spatial Sampling | null | null | null | null | cs.LG cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Feature pooling layers (e.g., max pooling) in convolutional neural networks
(CNNs) serve the dual purpose of providing increasingly abstract
representations as well as yielding computational savings in subsequent
convolutional layers. We view the pooling operation in CNNs as a two-step
procedure: first, a pooling window (e.g., $2\times 2$) slides over the feature
map with stride one which leaves the spatial resolution intact, and second,
downsampling is performed by selecting one pixel from each non-overlapping
pooling window in an often uniform and deterministic (e.g., top-left) manner.
Our starting point in this work is the observation that this regularly spaced
downsampling arising from non-overlapping windows, although intuitive from a
signal processing perspective (which has the goal of signal reconstruction), is
not necessarily optimal for \emph{learning} (where the goal is to generalize).
We study this aspect and propose a novel pooling strategy with stochastic
spatial sampling (S3Pool), where the regular downsampling is replaced by a more
general stochastic version. We observe that this general stochasticity acts as
a strong regularizer, and can also be seen as doing implicit data augmentation
by introducing distortions in the feature maps. We further introduce a
mechanism to control the amount of distortion to suit different datasets and
architectures. To demonstrate the effectiveness of the proposed approach, we
perform extensive experiments on several popular image classification
benchmarks, observing excellent improvements over baseline models. Experimental
code is available at https://github.com/Shuangfei/s3pool.
| [
{
"version": "v1",
"created": "Wed, 16 Nov 2016 04:17:52 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Zhai",
"Shuangfei",
""
],
[
"Wu",
"Hui",
""
],
[
"Kumar",
"Abhishek",
""
],
[
"Cheng",
"Yu",
""
],
[
"Lu",
"Yongxi",
""
],
[
"Zhang",
"Zhongfei",
""
],
[
"Feris",
"Rogerio",
""
]
] | TITLE: S3Pool: Pooling with Stochastic Spatial Sampling
ABSTRACT: Feature pooling layers (e.g., max pooling) in convolutional neural networks
(CNNs) serve the dual purpose of providing increasingly abstract
representations as well as yielding computational savings in subsequent
convolutional layers. We view the pooling operation in CNNs as a two-step
procedure: first, a pooling window (e.g., $2\times 2$) slides over the feature
map with stride one which leaves the spatial resolution intact, and second,
downsampling is performed by selecting one pixel from each non-overlapping
pooling window in an often uniform and deterministic (e.g., top-left) manner.
Our starting point in this work is the observation that this regularly spaced
downsampling arising from non-overlapping windows, although intuitive from a
signal processing perspective (which has the goal of signal reconstruction), is
not necessarily optimal for \emph{learning} (where the goal is to generalize).
We study this aspect and propose a novel pooling strategy with stochastic
spatial sampling (S3Pool), where the regular downsampling is replaced by a more
general stochastic version. We observe that this general stochasticity acts as
a strong regularizer, and can also be seen as doing implicit data augmentation
by introducing distortions in the feature maps. We further introduce a
mechanism to control the amount of distortion to suit different datasets and
architectures. To demonstrate the effectiveness of the proposed approach, we
perform extensive experiments on several popular image classification
benchmarks, observing excellent improvements over baseline models. Experimental
code is available at https://github.com/Shuangfei/s3pool.
| no_new_dataset | 0.951549 |
1611.05141 | Eric Hunsberger | Eric Hunsberger, Chris Eliasmith | Training Spiking Deep Networks for Neuromorphic Hardware | 10 pages, 3 figures, 4 tables; the "methods" section of this article
draws heavily on arXiv:1510.08829 | null | 10.13140/RG.2.2.10967.06566 | null | cs.NE cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We describe a method to train spiking deep networks that can be run using
leaky integrate-and-fire (LIF) neurons, achieving state-of-the-art results for
spiking LIF networks on five datasets, including the large ImageNet ILSVRC-2012
benchmark. Our method for transforming deep artificial neural networks into
spiking networks is scalable and works with a wide range of neural
nonlinearities. We achieve these results by softening the neural response
function, such that its derivative remains bounded, and by training the network
with noise to provide robustness against the variability introduced by spikes.
Our analysis shows that implementations of these networks on neuromorphic
hardware will be many times more power-efficient than the equivalent
non-spiking networks on traditional hardware.
| [
{
"version": "v1",
"created": "Wed, 16 Nov 2016 04:32:22 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Hunsberger",
"Eric",
""
],
[
"Eliasmith",
"Chris",
""
]
] | TITLE: Training Spiking Deep Networks for Neuromorphic Hardware
ABSTRACT: We describe a method to train spiking deep networks that can be run using
leaky integrate-and-fire (LIF) neurons, achieving state-of-the-art results for
spiking LIF networks on five datasets, including the large ImageNet ILSVRC-2012
benchmark. Our method for transforming deep artificial neural networks into
spiking networks is scalable and works with a wide range of neural
nonlinearities. We achieve these results by softening the neural response
function, such that its derivative remains bounded, and by training the network
with noise to provide robustness against the variability introduced by spikes.
Our analysis shows that implementations of these networks on neuromorphic
hardware will be many times more power-efficient than the equivalent
non-spiking networks on traditional hardware.
| no_new_dataset | 0.949012 |
1611.05215 | Yemin Shi Shi | Yemin Shi and Yonghong Tian and Yaowei Wang and Tiejun Huang | Joint Network based Attention for Action Recognition | 8 pages, 5 figures, JNA | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | By extracting spatial and temporal characteristics in one network, the
two-stream ConvNets can achieve the state-of-the-art performance in action
recognition. However, such a framework typically suffers from the separately
processing of spatial and temporal information between the two standalone
streams and is hard to capture long-term temporal dependence of an action. More
importantly, it is incapable of finding the salient portions of an action, say,
the frames that are the most discriminative to identify the action. To address
these problems, a \textbf{j}oint \textbf{n}etwork based \textbf{a}ttention
(JNA) is proposed in this study. We find that the fully-connected fusion,
branch selection and spatial attention mechanism are totally infeasible for
action recognition. Thus in our joint network, the spatial and temporal
branches share some information during the training stage. We also introduce an
attention mechanism on the temporal domain to capture the long-term dependence
meanwhile finding the salient portions. Extensive experiments are conducted on
two benchmark datasets, UCF101 and HMDB51. Experimental results show that our
method can improve the action recognition performance significantly and
achieves the state-of-the-art results on both datasets.
| [
{
"version": "v1",
"created": "Wed, 16 Nov 2016 10:40:30 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Shi",
"Yemin",
""
],
[
"Tian",
"Yonghong",
""
],
[
"Wang",
"Yaowei",
""
],
[
"Huang",
"Tiejun",
""
]
] | TITLE: Joint Network based Attention for Action Recognition
ABSTRACT: By extracting spatial and temporal characteristics in one network, the
two-stream ConvNets can achieve the state-of-the-art performance in action
recognition. However, such a framework typically suffers from the separately
processing of spatial and temporal information between the two standalone
streams and is hard to capture long-term temporal dependence of an action. More
importantly, it is incapable of finding the salient portions of an action, say,
the frames that are the most discriminative to identify the action. To address
these problems, a \textbf{j}oint \textbf{n}etwork based \textbf{a}ttention
(JNA) is proposed in this study. We find that the fully-connected fusion,
branch selection and spatial attention mechanism are totally infeasible for
action recognition. Thus in our joint network, the spatial and temporal
branches share some information during the training stage. We also introduce an
attention mechanism on the temporal domain to capture the long-term dependence
meanwhile finding the salient portions. Extensive experiments are conducted on
two benchmark datasets, UCF101 and HMDB51. Experimental results show that our
method can improve the action recognition performance significantly and
achieves the state-of-the-art results on both datasets.
| no_new_dataset | 0.946794 |
1611.05267 | Colin Lea | Colin Lea, Michael D. Flynn, Rene Vidal, Austin Reiter, Gregory D.
Hager | Temporal Convolutional Networks for Action Segmentation and Detection | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The ability to identify and temporally segment fine-grained human actions
throughout a video is crucial for robotics, surveillance, education, and
beyond. Typical approaches decouple this problem by first extracting local
spatiotemporal features from video frames and then feeding them into a temporal
classifier that captures high-level temporal patterns. We introduce a new class
of temporal models, which we call Temporal Convolutional Networks (TCNs), that
use a hierarchy of temporal convolutions to perform fine-grained action
segmentation or detection. Our Encoder-Decoder TCN uses pooling and upsampling
to efficiently capture long-range temporal patterns whereas our Dilated TCN
uses dilated convolutions. We show that TCNs are capable of capturing action
compositions, segment durations, and long-range dependencies, and are over a
magnitude faster to train than competing LSTM-based Recurrent Neural Networks.
We apply these models to three challenging fine-grained datasets and show large
improvements over the state of the art.
| [
{
"version": "v1",
"created": "Wed, 16 Nov 2016 13:19:19 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Lea",
"Colin",
""
],
[
"Flynn",
"Michael D.",
""
],
[
"Vidal",
"Rene",
""
],
[
"Reiter",
"Austin",
""
],
[
"Hager",
"Gregory D.",
""
]
] | TITLE: Temporal Convolutional Networks for Action Segmentation and Detection
ABSTRACT: The ability to identify and temporally segment fine-grained human actions
throughout a video is crucial for robotics, surveillance, education, and
beyond. Typical approaches decouple this problem by first extracting local
spatiotemporal features from video frames and then feeding them into a temporal
classifier that captures high-level temporal patterns. We introduce a new class
of temporal models, which we call Temporal Convolutional Networks (TCNs), that
use a hierarchy of temporal convolutions to perform fine-grained action
segmentation or detection. Our Encoder-Decoder TCN uses pooling and upsampling
to efficiently capture long-range temporal patterns whereas our Dilated TCN
uses dilated convolutions. We show that TCNs are capable of capturing action
compositions, segment durations, and long-range dependencies, and are over a
magnitude faster to train than competing LSTM-based Recurrent Neural Networks.
We apply these models to three challenging fine-grained datasets and show large
improvements over the state of the art.
| no_new_dataset | 0.947769 |
1611.05271 | Shu Zhang | Shu Zhang, Ran He, and Tieniu Tan | DeMeshNet: Blind Face Inpainting for Deep MeshFace Verification | 10pages, submitted to CVPR 17 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | MeshFace photos have been widely used in many Chinese business organizations
to protect ID face photos from being misused. The occlusions incurred by random
meshes severely degenerate the performance of face verification systems, which
raises the MeshFace verification problem between MeshFace and daily photos.
Previous methods cast this problem as a typical low-level vision problem, i.e.
blind inpainting. They recover perceptually pleasing clear ID photos from
MeshFaces by enforcing pixel level similarity between the recovered ID images
and the ground-truth clear ID images and then perform face verification on
them. Essentially, face verification is conducted on a compact feature space
rather than the image pixel space. Therefore, this paper argues that pixel
level similarity and feature level similarity jointly offer the key to improve
the verification performance. Based on this insight, we offer a novel feature
oriented blind face inpainting framework. Specifically, we implement this by
establishing a novel DeMeshNet, which consists of three parts. The first part
addresses blind inpainting of the MeshFaces by implicitly exploiting extra
supervision from the occlusion position to enforce pixel level similarity. The
second part explicitly enforces a feature level similarity in the compact
feature space, which can explore informative supervision from the feature space
to produce better inpainting results for verification. The last part copes with
face alignment within the net via a customized spatial transformer module when
extracting deep facial features. All the three parts are implemented within an
end-to-end network that facilitates efficient optimization. Extensive
experiments on two MeshFace datasets demonstrate the effectiveness of the
proposed DeMeshNet as well as the insight of this paper.
| [
{
"version": "v1",
"created": "Wed, 16 Nov 2016 13:36:45 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Zhang",
"Shu",
""
],
[
"He",
"Ran",
""
],
[
"Tan",
"Tieniu",
""
]
] | TITLE: DeMeshNet: Blind Face Inpainting for Deep MeshFace Verification
ABSTRACT: MeshFace photos have been widely used in many Chinese business organizations
to protect ID face photos from being misused. The occlusions incurred by random
meshes severely degenerate the performance of face verification systems, which
raises the MeshFace verification problem between MeshFace and daily photos.
Previous methods cast this problem as a typical low-level vision problem, i.e.
blind inpainting. They recover perceptually pleasing clear ID photos from
MeshFaces by enforcing pixel level similarity between the recovered ID images
and the ground-truth clear ID images and then perform face verification on
them. Essentially, face verification is conducted on a compact feature space
rather than the image pixel space. Therefore, this paper argues that pixel
level similarity and feature level similarity jointly offer the key to improve
the verification performance. Based on this insight, we offer a novel feature
oriented blind face inpainting framework. Specifically, we implement this by
establishing a novel DeMeshNet, which consists of three parts. The first part
addresses blind inpainting of the MeshFaces by implicitly exploiting extra
supervision from the occlusion position to enforce pixel level similarity. The
second part explicitly enforces a feature level similarity in the compact
feature space, which can explore informative supervision from the feature space
to produce better inpainting results for verification. The last part copes with
face alignment within the net via a customized spatial transformer module when
extracting deep facial features. All the three parts are implemented within an
end-to-end network that facilitates efficient optimization. Extensive
experiments on two MeshFace datasets demonstrate the effectiveness of the
proposed DeMeshNet as well as the insight of this paper.
| no_new_dataset | 0.951729 |
1611.05328 | Zhiwei Jin | Zhiwei Jin, Juan Cao, Jiebo Luo, and Yongdong Zhang | Image Credibility Analysis with Effective Domain Transferred Deep
Networks | null | null | null | null | cs.MM cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Numerous fake images spread on social media today and can severely jeopardize
the credibility of online content to public. In this paper, we employ deep
networks to learn distinct fake image related features. In contrast to
authentic images, fake images tend to be eye-catching and visually striking.
Compared with traditional visual recognition tasks, it is extremely challenging
to understand these psychologically triggered visual patterns in fake images.
Traditional general image classification datasets, such as ImageNet set, are
designed for feature learning at the object level but are not suitable for
learning the hyper-features that would be required by image credibility
analysis. In order to overcome the scarcity of training samples of fake images,
we first construct a large-scale auxiliary dataset indirectly related to this
task. This auxiliary dataset contains 0.6 million weakly-labeled fake and real
images collected automatically from social media. Through an AdaBoost-like
transfer learning algorithm, we train a CNN model with a few instances in the
target training set and 0.6 million images in the collected auxiliary set. This
learning algorithm is able to leverage knowledge from the auxiliary set and
gradually transfer it to the target task. Experiments on a real-world testing
set show that our proposed domain transferred CNN model outperforms several
competing baselines. It obtains superiror results over transfer learning
methods based on the general ImageNet set. Moreover, case studies show that our
proposed method reveals some interesting patterns for distinguishing fake and
authentic images.
| [
{
"version": "v1",
"created": "Wed, 16 Nov 2016 15:45:19 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Jin",
"Zhiwei",
""
],
[
"Cao",
"Juan",
""
],
[
"Luo",
"Jiebo",
""
],
[
"Zhang",
"Yongdong",
""
]
] | TITLE: Image Credibility Analysis with Effective Domain Transferred Deep
Networks
ABSTRACT: Numerous fake images spread on social media today and can severely jeopardize
the credibility of online content to public. In this paper, we employ deep
networks to learn distinct fake image related features. In contrast to
authentic images, fake images tend to be eye-catching and visually striking.
Compared with traditional visual recognition tasks, it is extremely challenging
to understand these psychologically triggered visual patterns in fake images.
Traditional general image classification datasets, such as ImageNet set, are
designed for feature learning at the object level but are not suitable for
learning the hyper-features that would be required by image credibility
analysis. In order to overcome the scarcity of training samples of fake images,
we first construct a large-scale auxiliary dataset indirectly related to this
task. This auxiliary dataset contains 0.6 million weakly-labeled fake and real
images collected automatically from social media. Through an AdaBoost-like
transfer learning algorithm, we train a CNN model with a few instances in the
target training set and 0.6 million images in the collected auxiliary set. This
learning algorithm is able to leverage knowledge from the auxiliary set and
gradually transfer it to the target task. Experiments on a real-world testing
set show that our proposed domain transferred CNN model outperforms several
competing baselines. It obtains superiror results over transfer learning
methods based on the general ImageNet set. Moreover, case studies show that our
proposed method reveals some interesting patterns for distinguishing fake and
authentic images.
| new_dataset | 0.971266 |
1611.05369 | Melanie Mitchell | Anthony D. Rhodes, Max H. Quinn, and Melanie Mitchell | Fast On-Line Kernel Density Estimation for Active Object Localization | arXiv admin note: text overlap with arXiv:1607.00548 | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A major goal of computer vision is to enable computers to interpret visual
situations---abstract concepts (e.g., "a person walking a dog," "a crowd
waiting for a bus," "a picnic") whose image instantiations are linked more by
their common spatial and semantic structure than by low-level visual
similarity. In this paper, we propose a novel method for prior learning and
active object localization for this kind of knowledge-driven search in static
images. In our system, prior situation knowledge is captured by a set of
flexible, kernel-based density estimations---a situation model---that represent
the expected spatial structure of the given situation. These estimations are
efficiently updated by information gained as the system searches for relevant
objects, allowing the system to use context as it is discovered to narrow the
search.
More specifically, at any given time in a run on a test image, our system
uses image features plus contextual information it has discovered to identify a
small subset of training images---an importance cluster---that is deemed most
similar to the given test image, given the context. This subset is used to
generate an updated situation model in an on-line fashion, using an efficient
multipole expansion technique.
As a proof of concept, we apply our algorithm to a highly varied and
challenging dataset consisting of instances of a "dog-walking" situation. Our
results support the hypothesis that dynamically-rendered, context-based
probability models can support efficient object localization in visual
situations. Moreover, our approach is general enough to be applied to diverse
machine learning paradigms requiring interpretable, probabilistic
representations generated from partially observed data.
| [
{
"version": "v1",
"created": "Wed, 16 Nov 2016 17:04:35 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Rhodes",
"Anthony D.",
""
],
[
"Quinn",
"Max H.",
""
],
[
"Mitchell",
"Melanie",
""
]
] | TITLE: Fast On-Line Kernel Density Estimation for Active Object Localization
ABSTRACT: A major goal of computer vision is to enable computers to interpret visual
situations---abstract concepts (e.g., "a person walking a dog," "a crowd
waiting for a bus," "a picnic") whose image instantiations are linked more by
their common spatial and semantic structure than by low-level visual
similarity. In this paper, we propose a novel method for prior learning and
active object localization for this kind of knowledge-driven search in static
images. In our system, prior situation knowledge is captured by a set of
flexible, kernel-based density estimations---a situation model---that represent
the expected spatial structure of the given situation. These estimations are
efficiently updated by information gained as the system searches for relevant
objects, allowing the system to use context as it is discovered to narrow the
search.
More specifically, at any given time in a run on a test image, our system
uses image features plus contextual information it has discovered to identify a
small subset of training images---an importance cluster---that is deemed most
similar to the given test image, given the context. This subset is used to
generate an updated situation model in an on-line fashion, using an efficient
multipole expansion technique.
As a proof of concept, we apply our algorithm to a highly varied and
challenging dataset consisting of instances of a "dog-walking" situation. Our
results support the hypothesis that dynamically-rendered, context-based
probability models can support efficient object localization in visual
situations. Moreover, our approach is general enough to be applied to diverse
machine learning paradigms requiring interpretable, probabilistic
representations generated from partially observed data.
| no_new_dataset | 0.508758 |
1611.05425 | Tim Weninger PhD | Baoxu Shi and Tim Weninger | ProjE: Embedding Projection for Knowledge Graph Completion | 14 pages, Accepted to AAAI 2017 | null | null | null | cs.AI stat.ML | http://creativecommons.org/licenses/by/4.0/ | With the large volume of new information created every day, determining the
validity of information in a knowledge graph and filling in its missing parts
are crucial tasks for many researchers and practitioners. To address this
challenge, a number of knowledge graph completion methods have been developed
using low-dimensional graph embeddings. Although researchers continue to
improve these models using an increasingly complex feature space, we show that
simple changes in the architecture of the underlying model can outperform
state-of-the-art models without the need for complex feature engineering. In
this work, we present a shared variable neural network model called ProjE that
fills-in missing information in a knowledge graph by learning joint embeddings
of the knowledge graph's entities and edges, and through subtle, but important,
changes to the standard loss function. In doing so, ProjE has a parameter size
that is smaller than 11 out of 15 existing methods while performing $37\%$
better than the current-best method on standard datasets. We also show, via a
new fact checking task, that ProjE is capable of accurately determining the
veracity of many declarative statements.
| [
{
"version": "v1",
"created": "Wed, 16 Nov 2016 20:09:08 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Shi",
"Baoxu",
""
],
[
"Weninger",
"Tim",
""
]
] | TITLE: ProjE: Embedding Projection for Knowledge Graph Completion
ABSTRACT: With the large volume of new information created every day, determining the
validity of information in a knowledge graph and filling in its missing parts
are crucial tasks for many researchers and practitioners. To address this
challenge, a number of knowledge graph completion methods have been developed
using low-dimensional graph embeddings. Although researchers continue to
improve these models using an increasingly complex feature space, we show that
simple changes in the architecture of the underlying model can outperform
state-of-the-art models without the need for complex feature engineering. In
this work, we present a shared variable neural network model called ProjE that
fills-in missing information in a knowledge graph by learning joint embeddings
of the knowledge graph's entities and edges, and through subtle, but important,
changes to the standard loss function. In doing so, ProjE has a parameter size
that is smaller than 11 out of 15 existing methods while performing $37\%$
better than the current-best method on standard datasets. We also show, via a
new fact checking task, that ProjE is capable of accurately determining the
veracity of many declarative statements.
| no_new_dataset | 0.941922 |
cs/0703125 | Vladimir Pestov | Vladimir Pestov | Intrinsic dimension of a dataset: what properties does one expect? | 6 pages, 6 figures, 1 table, latex with IEEE macros, final submission
to Proceedings of the 22nd IJCNN (Orlando, FL, August 12-17, 2007) | Proceedings of the 20th International Joint Conference on Neural
Networks (IJCNN'2007), Orlando, Florida (Aug. 12--17, 2007), pp. 1775--1780. | 10.1109/IJCNN.2007.4371431 | null | cs.LG | null | We propose an axiomatic approach to the concept of an intrinsic dimension of
a dataset, based on a viewpoint of geometry of high-dimensional structures. Our
first axiom postulates that high values of dimension be indicative of the
presence of the curse of dimensionality (in a certain precise mathematical
sense). The second axiom requires the dimension to depend smoothly on a
distance between datasets (so that the dimension of a dataset and that of an
approximating principal manifold would be close to each other). The third axiom
is a normalization condition: the dimension of the Euclidean $n$-sphere $\s^n$
is $\Theta(n)$. We give an example of a dimension function satisfying our
axioms, even though it is in general computationally unfeasible, and discuss a
computationally cheap function satisfying most but not all of our axioms (the
``intrinsic dimensionality'' of Ch\'avez et al.)
| [
{
"version": "v1",
"created": "Sun, 25 Mar 2007 01:19:14 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Pestov",
"Vladimir",
""
]
] | TITLE: Intrinsic dimension of a dataset: what properties does one expect?
ABSTRACT: We propose an axiomatic approach to the concept of an intrinsic dimension of
a dataset, based on a viewpoint of geometry of high-dimensional structures. Our
first axiom postulates that high values of dimension be indicative of the
presence of the curse of dimensionality (in a certain precise mathematical
sense). The second axiom requires the dimension to depend smoothly on a
distance between datasets (so that the dimension of a dataset and that of an
approximating principal manifold would be close to each other). The third axiom
is a normalization condition: the dimension of the Euclidean $n$-sphere $\s^n$
is $\Theta(n)$. We give an example of a dimension function satisfying our
axioms, even though it is in general computationally unfeasible, and discuss a
computationally cheap function satisfying most but not all of our axioms (the
``intrinsic dimensionality'' of Ch\'avez et al.)
| no_new_dataset | 0.944638 |
cs/9904002 | Vladimir Pestov | Vladimir Pestov | A geometric framework for modelling similarity search | 11 pages, LaTeX 2.e | Proc. 10-th Int. Workshop on Database and Expert Systems
Applications (DEXA'99), Sept. 1-3, 1999, Florence, Italy, IEEE Comp. Soc.,
pp. 150-154. | 10.1109/DEXA.1999.795158 | RP-99-12, School of Math and Comp Sci, Victoria University of
Wellington, New Zealand | cs.IR cs.DB cs.DS | null | The aim of this paper is to propose a geometric framework for modelling
similarity search in large and multidimensional data spaces of general nature,
which seems to be flexible enough to address such issues as analysis of
complexity, indexability, and the `curse of dimensionality.' Such a framework
is provided by the concept of the so-called similarity workload, which is a
probability metric space $\Omega$ (query domain) with a distinguished finite
subspace $X$ (dataset), together with an assembly of concepts, techniques, and
results from metric geometry. They include such notions as metric transform,
$\e$-entropy, and the phenomenon of concentration of measure on
high-dimensional structures. In particular, we discuss the relevance of the
latter to understanding the curse of dimensionality. As some of those concepts
and techniques are being currently reinvented by the database community, it
seems desirable to try and bridge the gap between database research and the
relevant work already done in geometry and analysis.
| [
{
"version": "v1",
"created": "Wed, 7 Apr 1999 04:16:02 GMT"
},
{
"version": "v2",
"created": "Mon, 21 Jun 1999 03:45:13 GMT"
}
] | 2016-11-17T00:00:00 | [
[
"Pestov",
"Vladimir",
""
]
] | TITLE: A geometric framework for modelling similarity search
ABSTRACT: The aim of this paper is to propose a geometric framework for modelling
similarity search in large and multidimensional data spaces of general nature,
which seems to be flexible enough to address such issues as analysis of
complexity, indexability, and the `curse of dimensionality.' Such a framework
is provided by the concept of the so-called similarity workload, which is a
probability metric space $\Omega$ (query domain) with a distinguished finite
subspace $X$ (dataset), together with an assembly of concepts, techniques, and
results from metric geometry. They include such notions as metric transform,
$\e$-entropy, and the phenomenon of concentration of measure on
high-dimensional structures. In particular, we discuss the relevance of the
latter to understanding the curse of dimensionality. As some of those concepts
and techniques are being currently reinvented by the database community, it
seems desirable to try and bridge the gap between database research and the
relevant work already done in geometry and analysis.
| no_new_dataset | 0.943452 |
1607.02810 | Mahnoosh Kholghi | Mahnoosh Kholghi, Lance De Vine, Laurianne Sitbon, Guido Zuccon,
Anthony Nguyen | The Benefits of Word Embeddings Features for Active Learning in Clinical
Information Extraction | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This study investigates the use of unsupervised word embeddings and sequence
features for sample representation in an active learning framework built to
extract clinical concepts from clinical free text. The objective is to further
reduce the manual annotation effort while achieving higher effectiveness
compared to a set of baseline features. Unsupervised features are derived from
skip-gram word embeddings and a sequence representation approach. The
comparative performance of unsupervised features and baseline hand-crafted
features in an active learning framework are investigated using a wide range of
selection criteria including least confidence, information diversity,
information density and diversity, and domain knowledge informativeness. Two
clinical datasets are used for evaluation: the i2b2/VA 2010 NLP challenge and
the ShARe/CLEF 2013 eHealth Evaluation Lab. Our results demonstrate significant
improvements in terms of effectiveness as well as annotation effort savings
across both datasets. Using unsupervised features along with baseline features
for sample representation lead to further savings of up to 9% and 10% of the
token and concept annotation rates, respectively.
| [
{
"version": "v1",
"created": "Mon, 11 Jul 2016 02:46:48 GMT"
},
{
"version": "v2",
"created": "Mon, 18 Jul 2016 00:25:18 GMT"
},
{
"version": "v3",
"created": "Wed, 9 Nov 2016 00:16:30 GMT"
},
{
"version": "v4",
"created": "Tue, 15 Nov 2016 05:06:01 GMT"
}
] | 2016-11-16T00:00:00 | [
[
"Kholghi",
"Mahnoosh",
""
],
[
"De Vine",
"Lance",
""
],
[
"Sitbon",
"Laurianne",
""
],
[
"Zuccon",
"Guido",
""
],
[
"Nguyen",
"Anthony",
""
]
] | TITLE: The Benefits of Word Embeddings Features for Active Learning in Clinical
Information Extraction
ABSTRACT: This study investigates the use of unsupervised word embeddings and sequence
features for sample representation in an active learning framework built to
extract clinical concepts from clinical free text. The objective is to further
reduce the manual annotation effort while achieving higher effectiveness
compared to a set of baseline features. Unsupervised features are derived from
skip-gram word embeddings and a sequence representation approach. The
comparative performance of unsupervised features and baseline hand-crafted
features in an active learning framework are investigated using a wide range of
selection criteria including least confidence, information diversity,
information density and diversity, and domain knowledge informativeness. Two
clinical datasets are used for evaluation: the i2b2/VA 2010 NLP challenge and
the ShARe/CLEF 2013 eHealth Evaluation Lab. Our results demonstrate significant
improvements in terms of effectiveness as well as annotation effort savings
across both datasets. Using unsupervised features along with baseline features
for sample representation lead to further savings of up to 9% and 10% of the
token and concept annotation rates, respectively.
| no_new_dataset | 0.949389 |
1607.03547 | Ron Appel | Ron Appel, Xavier Burgos-Artizzu, Pietro Perona | Improved Multi-Class Cost-Sensitive Boosting via Estimation of the
Minimum-Risk Class | Project website: http://www.vision.caltech.edu/~appel/projects/REBEL/ | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a simple unified framework for multi-class cost-sensitive
boosting. The minimum-risk class is estimated directly, rather than via an
approximation of the posterior distribution. Our method jointly optimizes
binary weak learners and their corresponding output vectors, requiring classes
to share features at each iteration. By training in a cost-sensitive manner,
weak learners are invested in separating classes whose discrimination is
important, at the expense of less relevant classification boundaries.
Additional contributions are a family of loss functions along with proof that
our algorithm is Boostable in the theoretical sense, as well as an efficient
procedure for growing decision trees for use as weak learners. We evaluate our
method on a variety of datasets: a collection of synthetic planar data, common
UCI datasets, MNIST digits, SUN scenes, and CUB-200 birds. Results show
state-of-the-art performance across all datasets against several strong
baselines, including non-boosting multi-class approaches.
| [
{
"version": "v1",
"created": "Tue, 12 Jul 2016 23:56:33 GMT"
},
{
"version": "v2",
"created": "Tue, 15 Nov 2016 19:29:30 GMT"
}
] | 2016-11-16T00:00:00 | [
[
"Appel",
"Ron",
""
],
[
"Burgos-Artizzu",
"Xavier",
""
],
[
"Perona",
"Pietro",
""
]
] | TITLE: Improved Multi-Class Cost-Sensitive Boosting via Estimation of the
Minimum-Risk Class
ABSTRACT: We present a simple unified framework for multi-class cost-sensitive
boosting. The minimum-risk class is estimated directly, rather than via an
approximation of the posterior distribution. Our method jointly optimizes
binary weak learners and their corresponding output vectors, requiring classes
to share features at each iteration. By training in a cost-sensitive manner,
weak learners are invested in separating classes whose discrimination is
important, at the expense of less relevant classification boundaries.
Additional contributions are a family of loss functions along with proof that
our algorithm is Boostable in the theoretical sense, as well as an efficient
procedure for growing decision trees for use as weak learners. We evaluate our
method on a variety of datasets: a collection of synthetic planar data, common
UCI datasets, MNIST digits, SUN scenes, and CUB-200 birds. Results show
state-of-the-art performance across all datasets against several strong
baselines, including non-boosting multi-class approaches.
| no_new_dataset | 0.945751 |
1611.00456 | Bo Wang | Bo Wang, Yanshu Yu, Yuan Wang | Measuring Asymmetric Opinions on Online Social Interrelationship with
Language and Network Features | null | null | null | null | cs.SI cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Instead of studying the properties of social relationship from an objective
view, in this paper, we focus on individuals' subjective and asymmetric
opinions on their interrelationships. Inspired by the theories from
sociolinguistics, we investigate two individuals' opinions on their
interrelationship with their interactive language features. Eliminating the
difference of personal language style, we clarify that the asymmetry of
interactive language feature values can indicate individuals' asymmetric
opinions on their interrelationship. We also discuss how the degree of
opinions' asymmetry is related to the individuals' personality traits.
Furthermore, to measure the individuals' asymmetric opinions on
interrelationship concretely, we develop a novel model synthetizing interactive
language and social network features. The experimental results with Enron email
dataset provide multiple evidences of the asymmetric opinions on
interrelationship, and also verify the effectiveness of the proposed model in
measuring the degree of opinions' asymmetry.
| [
{
"version": "v1",
"created": "Wed, 2 Nov 2016 03:04:42 GMT"
},
{
"version": "v2",
"created": "Tue, 15 Nov 2016 04:18:51 GMT"
}
] | 2016-11-16T00:00:00 | [
[
"Wang",
"Bo",
""
],
[
"Yu",
"Yanshu",
""
],
[
"Wang",
"Yuan",
""
]
] | TITLE: Measuring Asymmetric Opinions on Online Social Interrelationship with
Language and Network Features
ABSTRACT: Instead of studying the properties of social relationship from an objective
view, in this paper, we focus on individuals' subjective and asymmetric
opinions on their interrelationships. Inspired by the theories from
sociolinguistics, we investigate two individuals' opinions on their
interrelationship with their interactive language features. Eliminating the
difference of personal language style, we clarify that the asymmetry of
interactive language feature values can indicate individuals' asymmetric
opinions on their interrelationship. We also discuss how the degree of
opinions' asymmetry is related to the individuals' personality traits.
Furthermore, to measure the individuals' asymmetric opinions on
interrelationship concretely, we develop a novel model synthetizing interactive
language and social network features. The experimental results with Enron email
dataset provide multiple evidences of the asymmetric opinions on
interrelationship, and also verify the effectiveness of the proposed model in
measuring the degree of opinions' asymmetry.
| no_new_dataset | 0.948537 |
1611.02639 | Ankur Taly | Mukund Sundararajan, Ankur Taly, Qiqi Yan | Gradients of Counterfactuals | null | null | null | null | cs.LG cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gradients have been used to quantify feature importance in machine learning
models. Unfortunately, in nonlinear deep networks, not only individual neurons
but also the whole network can saturate, and as a result an important input
feature can have a tiny gradient. We study various networks, and observe that
this phenomena is indeed widespread, across many inputs.
We propose to examine interior gradients, which are gradients of
counterfactual inputs constructed by scaling down the original input. We apply
our method to the GoogleNet architecture for object recognition in images, as
well as a ligand-based virtual screening network with categorical features and
an LSTM based language model for the Penn Treebank dataset. We visualize how
interior gradients better capture feature importance. Furthermore, interior
gradients are applicable to a wide variety of deep networks, and have the
attribution property that the feature importance scores sum to the the
prediction score.
Best of all, interior gradients can be computed just as easily as gradients.
In contrast, previous methods are complex to implement, which hinders practical
adoption.
| [
{
"version": "v1",
"created": "Tue, 8 Nov 2016 18:10:44 GMT"
},
{
"version": "v2",
"created": "Tue, 15 Nov 2016 19:55:26 GMT"
}
] | 2016-11-16T00:00:00 | [
[
"Sundararajan",
"Mukund",
""
],
[
"Taly",
"Ankur",
""
],
[
"Yan",
"Qiqi",
""
]
] | TITLE: Gradients of Counterfactuals
ABSTRACT: Gradients have been used to quantify feature importance in machine learning
models. Unfortunately, in nonlinear deep networks, not only individual neurons
but also the whole network can saturate, and as a result an important input
feature can have a tiny gradient. We study various networks, and observe that
this phenomena is indeed widespread, across many inputs.
We propose to examine interior gradients, which are gradients of
counterfactual inputs constructed by scaling down the original input. We apply
our method to the GoogleNet architecture for object recognition in images, as
well as a ligand-based virtual screening network with categorical features and
an LSTM based language model for the Penn Treebank dataset. We visualize how
interior gradients better capture feature importance. Furthermore, interior
gradients are applicable to a wide variety of deep networks, and have the
attribution property that the feature importance scores sum to the the
prediction score.
Best of all, interior gradients can be computed just as easily as gradients.
In contrast, previous methods are complex to implement, which hinders practical
adoption.
| no_new_dataset | 0.947235 |
1611.04035 | Murat Kocaoglu | Murat Kocaoglu, Alexandros G. Dimakis, Sriram Vishwanath, Babak
Hassibi | Entropic Causal Inference | To appear in AAAI 2017 | null | null | null | cs.AI cs.IT math.IT stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the problem of identifying the causal direction between two
discrete random variables using observational data. Unlike previous work, we
keep the most general functional model but make an assumption on the unobserved
exogenous variable: Inspired by Occam's razor, we assume that the exogenous
variable is simple in the true causal direction. We quantify simplicity using
R\'enyi entropy. Our main result is that, under natural assumptions, if the
exogenous variable has low $H_0$ entropy (cardinality) in the true direction,
it must have high $H_0$ entropy in the wrong direction. We establish several
algorithmic hardness results about estimating the minimum entropy exogenous
variable. We show that the problem of finding the exogenous variable with
minimum entropy is equivalent to the problem of finding minimum joint entropy
given $n$ marginal distributions, also known as minimum entropy coupling
problem. We propose an efficient greedy algorithm for the minimum entropy
coupling problem, that for $n=2$ provably finds a local optimum. This gives a
greedy algorithm for finding the exogenous variable with minimum $H_1$ (Shannon
Entropy). Our greedy entropy-based causal inference algorithm has similar
performance to the state of the art additive noise models in real datasets. One
advantage of our approach is that we make no use of the values of random
variables but only their distributions. Our method can therefore be used for
causal inference for both ordinal and also categorical data, unlike additive
noise models.
| [
{
"version": "v1",
"created": "Sat, 12 Nov 2016 18:56:34 GMT"
},
{
"version": "v2",
"created": "Tue, 15 Nov 2016 03:09:53 GMT"
}
] | 2016-11-16T00:00:00 | [
[
"Kocaoglu",
"Murat",
""
],
[
"Dimakis",
"Alexandros G.",
""
],
[
"Vishwanath",
"Sriram",
""
],
[
"Hassibi",
"Babak",
""
]
] | TITLE: Entropic Causal Inference
ABSTRACT: We consider the problem of identifying the causal direction between two
discrete random variables using observational data. Unlike previous work, we
keep the most general functional model but make an assumption on the unobserved
exogenous variable: Inspired by Occam's razor, we assume that the exogenous
variable is simple in the true causal direction. We quantify simplicity using
R\'enyi entropy. Our main result is that, under natural assumptions, if the
exogenous variable has low $H_0$ entropy (cardinality) in the true direction,
it must have high $H_0$ entropy in the wrong direction. We establish several
algorithmic hardness results about estimating the minimum entropy exogenous
variable. We show that the problem of finding the exogenous variable with
minimum entropy is equivalent to the problem of finding minimum joint entropy
given $n$ marginal distributions, also known as minimum entropy coupling
problem. We propose an efficient greedy algorithm for the minimum entropy
coupling problem, that for $n=2$ provably finds a local optimum. This gives a
greedy algorithm for finding the exogenous variable with minimum $H_1$ (Shannon
Entropy). Our greedy entropy-based causal inference algorithm has similar
performance to the state of the art additive noise models in real datasets. One
advantage of our approach is that we make no use of the values of random
variables but only their distributions. Our method can therefore be used for
causal inference for both ordinal and also categorical data, unlike additive
noise models.
| no_new_dataset | 0.947624 |
1611.04149 | Zebang Shen | Zebang Shen, Hui Qian, Chao Zhang, and Tengfei Zhou | Accelerated Variance Reduced Block Coordinate Descent | null | null | null | null | stat.ML cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Algorithms with fast convergence, small number of data access, and low
per-iteration complexity are particularly favorable in the big data era, due to
the demand for obtaining \emph{highly accurate solutions} to problems with
\emph{a large number of samples} in \emph{ultra-high} dimensional space.
Existing algorithms lack at least one of these qualities, and thus are
inefficient in handling such big data challenge. In this paper, we propose a
method enjoying all these merits with an accelerated convergence rate
$O(\frac{1}{k^2})$. Empirical studies on large scale datasets with more than
one million features are conducted to show the effectiveness of our methods in
practice.
| [
{
"version": "v1",
"created": "Sun, 13 Nov 2016 16:01:10 GMT"
}
] | 2016-11-16T00:00:00 | [
[
"Shen",
"Zebang",
""
],
[
"Qian",
"Hui",
""
],
[
"Zhang",
"Chao",
""
],
[
"Zhou",
"Tengfei",
""
]
] | TITLE: Accelerated Variance Reduced Block Coordinate Descent
ABSTRACT: Algorithms with fast convergence, small number of data access, and low
per-iteration complexity are particularly favorable in the big data era, due to
the demand for obtaining \emph{highly accurate solutions} to problems with
\emph{a large number of samples} in \emph{ultra-high} dimensional space.
Existing algorithms lack at least one of these qualities, and thus are
inefficient in handling such big data challenge. In this paper, we propose a
method enjoying all these merits with an accelerated convergence rate
$O(\frac{1}{k^2})$. Empirical studies on large scale datasets with more than
one million features are conducted to show the effectiveness of our methods in
practice.
| no_new_dataset | 0.94625 |
1611.04358 | Weijie Huang | Weijie Huang, Jun Wang | Character-level Convolutional Network for Text Classification Applied to
Chinese Corpus | MSc Thesis, 44 pages | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This article provides an interesting exploration of character-level
convolutional neural network solving Chinese corpus text classification
problem. We constructed a large-scale Chinese language dataset, and the result
shows that character-level convolutional neural network works better on Chinese
corpus than its corresponding pinyin format dataset. This is the first time
that character-level convolutional neural network applied to text
classification problem.
| [
{
"version": "v1",
"created": "Mon, 14 Nov 2016 12:24:27 GMT"
},
{
"version": "v2",
"created": "Tue, 15 Nov 2016 14:41:23 GMT"
}
] | 2016-11-16T00:00:00 | [
[
"Huang",
"Weijie",
""
],
[
"Wang",
"Jun",
""
]
] | TITLE: Character-level Convolutional Network for Text Classification Applied to
Chinese Corpus
ABSTRACT: This article provides an interesting exploration of character-level
convolutional neural network solving Chinese corpus text classification
problem. We constructed a large-scale Chinese language dataset, and the result
shows that character-level convolutional neural network works better on Chinese
corpus than its corresponding pinyin format dataset. This is the first time
that character-level convolutional neural network applied to text
classification problem.
| new_dataset | 0.954984 |
1611.04374 | Pascale Bayle-Guillemaud | Maxime Boniface (MEM), Lucille Quazuguel (IMN), Julien Danet (MEM),
Dominique Guyomard (IMN), Philippe Moreau (IMN), Pascale Bayle-Guillemaud
(MEM) | Nanoscale Chemical Evolution of Silicon Negative Electrodes
Characterized by Low-Loss STEM-EELS | Nano Letters, American Chemical Society, 2016 | null | 10.1021/acs.nanolett.6b02883 | null | physics.chem-ph cond-mat.mes-hall | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Continuous solid electrolyte interface (SEI) formation remains the limiting
factor of the lifetime of silicon nanoparticles (SiNPs) based negative
electrodes. Methods that could provide clear diagnosis of the electrode
degradation are of utmost necessity to streamline further developments. We
demonstrate that electron energy-loss spectroscopy (EELS) in a scanning
transmission electron microscope (STEM) can be used to quickly map SEI
components and quantify LixSi alloys from single experiments, with resolutions
down to 5 nm. Exploiting the low-loss part of the EEL spectrum allowed us to
circumvent the degradation phenomena that have so far crippled the application
of this technique on such beam-sensitive compounds. Our results provide
unprecedented insight into silicon aging mechanisms in full cell configuration.
We observe the morphology of the SEI to be extremely heterogeneous at the
particle scale but with clear chemical evolutions with extended cycling coming
from both SEI accumulation and a transition from lithium-rich carbonate-like
compounds to lithium-poor ones. Thanks to the retrieval of several results from
a single dataset, we were able to correlate local discrepancies in lithiation
to the initial crystallinity of silicon as well as to the local SEI chemistry
and morphology. This study emphasizes how initial heterogeneities in the
percolating electronic network and the porosity affect SiNPs aggregates along
cycling. These findings pinpoint the crucial role of an optimized formulation
in silicon-based thick electrodes.
| [
{
"version": "v1",
"created": "Mon, 14 Nov 2016 13:07:22 GMT"
}
] | 2016-11-16T00:00:00 | [
[
"Boniface",
"Maxime",
"",
"MEM"
],
[
"Quazuguel",
"Lucille",
"",
"IMN"
],
[
"Danet",
"Julien",
"",
"MEM"
],
[
"Guyomard",
"Dominique",
"",
"IMN"
],
[
"Moreau",
"Philippe",
"",
"IMN"
],
[
"Bayle-Guillemaud",
"Pascale",
"",
"MEM"
]
] | TITLE: Nanoscale Chemical Evolution of Silicon Negative Electrodes
Characterized by Low-Loss STEM-EELS
ABSTRACT: Continuous solid electrolyte interface (SEI) formation remains the limiting
factor of the lifetime of silicon nanoparticles (SiNPs) based negative
electrodes. Methods that could provide clear diagnosis of the electrode
degradation are of utmost necessity to streamline further developments. We
demonstrate that electron energy-loss spectroscopy (EELS) in a scanning
transmission electron microscope (STEM) can be used to quickly map SEI
components and quantify LixSi alloys from single experiments, with resolutions
down to 5 nm. Exploiting the low-loss part of the EEL spectrum allowed us to
circumvent the degradation phenomena that have so far crippled the application
of this technique on such beam-sensitive compounds. Our results provide
unprecedented insight into silicon aging mechanisms in full cell configuration.
We observe the morphology of the SEI to be extremely heterogeneous at the
particle scale but with clear chemical evolutions with extended cycling coming
from both SEI accumulation and a transition from lithium-rich carbonate-like
compounds to lithium-poor ones. Thanks to the retrieval of several results from
a single dataset, we were able to correlate local discrepancies in lithiation
to the initial crystallinity of silicon as well as to the local SEI chemistry
and morphology. This study emphasizes how initial heterogeneities in the
percolating electronic network and the porosity affect SiNPs aggregates along
cycling. These findings pinpoint the crucial role of an optimized formulation
in silicon-based thick electrodes.
| no_new_dataset | 0.946051 |
1611.04636 | Honglin Zheng | Honglin Zheng, Tianlang Chen, Jiebo Luo | When Saliency Meets Sentiment: Understanding How Image Content Invokes
Emotion and Sentiment | 7 pages, 5 figures, submitted to AAAI-17 | null | null | null | cs.AI cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sentiment analysis is crucial for extracting social signals from social media
content. Due to the prevalence of images in social media, image sentiment
analysis is receiving increasing attention in recent years. However, most
existing systems are black-boxes that do not provide insight on how image
content invokes sentiment and emotion in the viewers. Psychological studies
have confirmed that salient objects in an image often invoke emotions. In this
work, we investigate more fine-grained and more comprehensive interaction
between visual saliency and visual sentiment. In particular, we partition
images in several primary scene-type dimensions, including: open-closed,
natural-manmade, indoor-outdoor, and face-noface. Using state of the art
saliency detection algorithm and sentiment classification algorithm, we examine
how the sentiment of the salient region(s) in an image relates to the overall
sentiment of the image. The experiments on a representative image emotion
dataset have shown interesting correlation between saliency and sentiment in
different scene types and in turn shed light on the mechanism of visual
sentiment evocation.
| [
{
"version": "v1",
"created": "Mon, 14 Nov 2016 22:02:09 GMT"
}
] | 2016-11-16T00:00:00 | [
[
"Zheng",
"Honglin",
""
],
[
"Chen",
"Tianlang",
""
],
[
"Luo",
"Jiebo",
""
]
] | TITLE: When Saliency Meets Sentiment: Understanding How Image Content Invokes
Emotion and Sentiment
ABSTRACT: Sentiment analysis is crucial for extracting social signals from social media
content. Due to the prevalence of images in social media, image sentiment
analysis is receiving increasing attention in recent years. However, most
existing systems are black-boxes that do not provide insight on how image
content invokes sentiment and emotion in the viewers. Psychological studies
have confirmed that salient objects in an image often invoke emotions. In this
work, we investigate more fine-grained and more comprehensive interaction
between visual saliency and visual sentiment. In particular, we partition
images in several primary scene-type dimensions, including: open-closed,
natural-manmade, indoor-outdoor, and face-noface. Using state of the art
saliency detection algorithm and sentiment classification algorithm, we examine
how the sentiment of the salient region(s) in an image relates to the overall
sentiment of the image. The experiments on a representative image emotion
dataset have shown interesting correlation between saliency and sentiment in
different scene types and in turn shed light on the mechanism of visual
sentiment evocation.
| no_new_dataset | 0.948775 |
1611.04686 | Hang Zhang | Hang Zhang, Fengyuan Zhu and Shixin Li | Robust Matrix Regression | 8 pages, 4 tables | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Modern technologies are producing datasets with complex intrinsic structures,
and they can be naturally represented as matrices instead of vectors. To
preserve the latent data structures during processing, modern regression
approaches incorporate the low-rank property to the model and achieve
satisfactory performance for certain applications. These approaches all assume
that both predictors and labels for each pair of data within the training set
are accurate. However, in real-world applications, it is common to see the
training data contaminated by noises, which can affect the robustness of these
matrix regression methods. In this paper, we address this issue by introducing
a novel robust matrix regression method. We also derive efficient proximal
algorithms for model training. To evaluate the performance of our methods, we
apply it to real world applications with comparative studies. Our method
achieves the state-of-the-art performance, which shows the effectiveness and
the practical value of our method.
| [
{
"version": "v1",
"created": "Tue, 15 Nov 2016 03:15:46 GMT"
}
] | 2016-11-16T00:00:00 | [
[
"Zhang",
"Hang",
""
],
[
"Zhu",
"Fengyuan",
""
],
[
"Li",
"Shixin",
""
]
] | TITLE: Robust Matrix Regression
ABSTRACT: Modern technologies are producing datasets with complex intrinsic structures,
and they can be naturally represented as matrices instead of vectors. To
preserve the latent data structures during processing, modern regression
approaches incorporate the low-rank property to the model and achieve
satisfactory performance for certain applications. These approaches all assume
that both predictors and labels for each pair of data within the training set
are accurate. However, in real-world applications, it is common to see the
training data contaminated by noises, which can affect the robustness of these
matrix regression methods. In this paper, we address this issue by introducing
a novel robust matrix regression method. We also derive efficient proximal
algorithms for model training. To evaluate the performance of our methods, we
apply it to real world applications with comparative studies. Our method
achieves the state-of-the-art performance, which shows the effectiveness and
the practical value of our method.
| no_new_dataset | 0.944485 |
1611.04782 | Salvatore Rampone | Gianni D'Angelo, Salvatore Rampone | Feature Extraction and Soft Computing Methods for Aerospace Structure
Defect Classification | null | Measurement Volume 85, May 2016, Pages 192-209 | 10.1016/j.measurement.2016.02.027 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This study concerns the effectiveness of several techniques and methods of
signals processing and data interpretation for the diagnosis of aerospace
structure defects. This is done by applying different known feature extraction
methods, in addition to a new CBIR-based one; and some soft computing
techniques including a recent HPC parallel implementation of the U-BRAIN
learning algorithm on Non Destructive Testing data. The performance of the
resulting detection systems are measured in terms of Accuracy, Sensitivity,
Specificity, and Precision. Their effectiveness is evaluated by the Matthews
correlation, the Area Under Curve (AUC), and the F-Measure. Several experiments
are performed on a standard dataset of eddy current signal samples for aircraft
structures. Our experimental results evidence that the key to a successful
defect classifier is the feature extraction method - namely the novel
CBIR-based one outperforms all the competitors - and they illustrate the
greater effectiveness of the U-BRAIN algorithm and the MLP neural network among
the soft computing methods in this kind of application.
Keywords- Non-destructive testing (NDT); Soft Computing; Feature Extraction;
Classification Algorithms; Content-Based Image Retrieval (CBIR); Eddy Currents
(EC).
| [
{
"version": "v1",
"created": "Tue, 15 Nov 2016 10:47:12 GMT"
}
] | 2016-11-16T00:00:00 | [
[
"D'Angelo",
"Gianni",
""
],
[
"Rampone",
"Salvatore",
""
]
] | TITLE: Feature Extraction and Soft Computing Methods for Aerospace Structure
Defect Classification
ABSTRACT: This study concerns the effectiveness of several techniques and methods of
signals processing and data interpretation for the diagnosis of aerospace
structure defects. This is done by applying different known feature extraction
methods, in addition to a new CBIR-based one; and some soft computing
techniques including a recent HPC parallel implementation of the U-BRAIN
learning algorithm on Non Destructive Testing data. The performance of the
resulting detection systems are measured in terms of Accuracy, Sensitivity,
Specificity, and Precision. Their effectiveness is evaluated by the Matthews
correlation, the Area Under Curve (AUC), and the F-Measure. Several experiments
are performed on a standard dataset of eddy current signal samples for aircraft
structures. Our experimental results evidence that the key to a successful
defect classifier is the feature extraction method - namely the novel
CBIR-based one outperforms all the competitors - and they illustrate the
greater effectiveness of the U-BRAIN algorithm and the MLP neural network among
the soft computing methods in this kind of application.
Keywords- Non-destructive testing (NDT); Soft Computing; Feature Extraction;
Classification Algorithms; Content-Based Image Retrieval (CBIR); Eddy Currents
(EC).
| no_new_dataset | 0.945601 |
1611.04835 | Nauman Shahid | Nauman Shahid, Francesco Grassi, Pierre Vandergheynst | Multilinear Low-Rank Tensors on Graphs & Applications | null | null | null | null | cs.CV cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a new framework for the analysis of low-rank tensors which lies at
the intersection of spectral graph theory and signal processing. As a first
step, we present a new graph based low-rank decomposition which approximates
the classical low-rank SVD for matrices and multi-linear SVD for tensors. Then,
building on this novel decomposition we construct a general class of convex
optimization problems for approximately solving low-rank tensor inverse
problems, such as tensor Robust PCA. The whole framework is named as
'Multilinear Low-rank tensors on Graphs (MLRTG)'. Our theoretical analysis
shows: 1) MLRTG stands on the notion of approximate stationarity of
multi-dimensional signals on graphs and 2) the approximation error depends on
the eigen gaps of the graphs. We demonstrate applications for a wide variety of
4 artificial and 12 real tensor datasets, such as EEG, FMRI, BCI, surveillance
videos and hyperspectral images. Generalization of the tensor concepts to
non-euclidean domain, orders of magnitude speed-up, low-memory requirement and
significantly enhanced performance at low SNR are the key aspects of our
framework.
| [
{
"version": "v1",
"created": "Tue, 15 Nov 2016 14:05:43 GMT"
}
] | 2016-11-16T00:00:00 | [
[
"Shahid",
"Nauman",
""
],
[
"Grassi",
"Francesco",
""
],
[
"Vandergheynst",
"Pierre",
""
]
] | TITLE: Multilinear Low-Rank Tensors on Graphs & Applications
ABSTRACT: We propose a new framework for the analysis of low-rank tensors which lies at
the intersection of spectral graph theory and signal processing. As a first
step, we present a new graph based low-rank decomposition which approximates
the classical low-rank SVD for matrices and multi-linear SVD for tensors. Then,
building on this novel decomposition we construct a general class of convex
optimization problems for approximately solving low-rank tensor inverse
problems, such as tensor Robust PCA. The whole framework is named as
'Multilinear Low-rank tensors on Graphs (MLRTG)'. Our theoretical analysis
shows: 1) MLRTG stands on the notion of approximate stationarity of
multi-dimensional signals on graphs and 2) the approximation error depends on
the eigen gaps of the graphs. We demonstrate applications for a wide variety of
4 artificial and 12 real tensor datasets, such as EEG, FMRI, BCI, surveillance
videos and hyperspectral images. Generalization of the tensor concepts to
non-euclidean domain, orders of magnitude speed-up, low-memory requirement and
significantly enhanced performance at low SNR are the key aspects of our
framework.
| no_new_dataset | 0.944995 |
1611.04905 | Yehya Abouelnaga | Yehya Abouelnaga, Ola S. Ali, Hager Rady, and Mohamed Moustafa | CIFAR-10: KNN-based Ensemble of Classifiers | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we study the performance of different classifiers on the
CIFAR-10 dataset, and build an ensemble of classifiers to reach a better
performance. We show that, on CIFAR-10, K-Nearest Neighbors (KNN) and
Convolutional Neural Network (CNN), on some classes, are mutually exclusive,
thus yield in higher accuracy when combined. We reduce KNN overfitting using
Principal Component Analysis (PCA), and ensemble it with a CNN to increase its
accuracy. Our approach improves our best CNN model from 93.33% to 94.03%.
| [
{
"version": "v1",
"created": "Tue, 15 Nov 2016 16:02:58 GMT"
}
] | 2016-11-16T00:00:00 | [
[
"Abouelnaga",
"Yehya",
""
],
[
"Ali",
"Ola S.",
""
],
[
"Rady",
"Hager",
""
],
[
"Moustafa",
"Mohamed",
""
]
] | TITLE: CIFAR-10: KNN-based Ensemble of Classifiers
ABSTRACT: In this paper, we study the performance of different classifiers on the
CIFAR-10 dataset, and build an ensemble of classifiers to reach a better
performance. We show that, on CIFAR-10, K-Nearest Neighbors (KNN) and
Convolutional Neural Network (CNN), on some classes, are mutually exclusive,
thus yield in higher accuracy when combined. We reduce KNN overfitting using
Principal Component Analysis (PCA), and ensemble it with a CNN to increase its
accuracy. Our approach improves our best CNN model from 93.33% to 94.03%.
| no_new_dataset | 0.954265 |
1611.04999 | Cyrus Rashtchian | Paul Beame and Cyrus Rashtchian | Massively-Parallel Similarity Join, Edge-Isoperimetry, and Distance
Correlations on the Hypercube | 23 pages, plus references and appendix. To appear in SODA 2017 | null | null | null | cs.DS cs.CC cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study distributed protocols for finding all pairs of similar vectors in a
large dataset. Our results pertain to a variety of discrete metrics, and we
give concrete instantiations for Hamming distance. In particular, we give
improved upper bounds on the overhead required for similarity defined by
Hamming distance $r>1$ and prove a lower bound showing qualitative optimality
of the overhead required for similarity over any Hamming distance $r$. Our main
conceptual contribution is a connection between similarity search algorithms
and certain graph-theoretic quantities. For our upper bounds, we exhibit a
general method for designing one-round protocols using edge-isoperimetric
shapes in similarity graphs. For our lower bounds, we define a new
combinatorial optimization problem, which can be stated in purely
graph-theoretic terms yet also captures the core of the analysis in previous
theoretical work on distributed similarity joins. As one of our main technical
results, we prove new bounds on distance correlations in subsets of the Hamming
cube.
| [
{
"version": "v1",
"created": "Tue, 15 Nov 2016 19:36:28 GMT"
}
] | 2016-11-16T00:00:00 | [
[
"Beame",
"Paul",
""
],
[
"Rashtchian",
"Cyrus",
""
]
] | TITLE: Massively-Parallel Similarity Join, Edge-Isoperimetry, and Distance
Correlations on the Hypercube
ABSTRACT: We study distributed protocols for finding all pairs of similar vectors in a
large dataset. Our results pertain to a variety of discrete metrics, and we
give concrete instantiations for Hamming distance. In particular, we give
improved upper bounds on the overhead required for similarity defined by
Hamming distance $r>1$ and prove a lower bound showing qualitative optimality
of the overhead required for similarity over any Hamming distance $r$. Our main
conceptual contribution is a connection between similarity search algorithms
and certain graph-theoretic quantities. For our upper bounds, we exhibit a
general method for designing one-round protocols using edge-isoperimetric
shapes in similarity graphs. For our lower bounds, we define a new
combinatorial optimization problem, which can be stated in purely
graph-theoretic terms yet also captures the core of the analysis in previous
theoretical work on distributed similarity joins. As one of our main technical
results, we prove new bounds on distance correlations in subsets of the Hamming
cube.
| no_new_dataset | 0.946349 |
1611.05013 | Ishaan Gulrajani | Ishaan Gulrajani, Kundan Kumar, Faruk Ahmed, Adrien Ali Taiga,
Francesco Visin, David Vazquez, Aaron Courville | PixelVAE: A Latent Variable Model for Natural Images | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Natural image modeling is a landmark challenge of unsupervised learning.
Variational Autoencoders (VAEs) learn a useful latent representation and model
global structure well but have difficulty capturing small details. PixelCNN
models details very well, but lacks a latent code and is difficult to scale for
capturing large structures. We present PixelVAE, a VAE model with an
autoregressive decoder based on PixelCNN. Our model requires very few expensive
autoregressive layers compared to PixelCNN and learns latent codes that are
more compressed than a standard VAE while still capturing most non-trivial
structure. Finally, we extend our model to a hierarchy of latent variables at
different scales. Our model achieves state-of-the-art performance on binarized
MNIST, competitive performance on 64x64 ImageNet, and high-quality samples on
the LSUN bedrooms dataset.
| [
{
"version": "v1",
"created": "Tue, 15 Nov 2016 20:16:27 GMT"
}
] | 2016-11-16T00:00:00 | [
[
"Gulrajani",
"Ishaan",
""
],
[
"Kumar",
"Kundan",
""
],
[
"Ahmed",
"Faruk",
""
],
[
"Taiga",
"Adrien Ali",
""
],
[
"Visin",
"Francesco",
""
],
[
"Vazquez",
"David",
""
],
[
"Courville",
"Aaron",
""
]
] | TITLE: PixelVAE: A Latent Variable Model for Natural Images
ABSTRACT: Natural image modeling is a landmark challenge of unsupervised learning.
Variational Autoencoders (VAEs) learn a useful latent representation and model
global structure well but have difficulty capturing small details. PixelCNN
models details very well, but lacks a latent code and is difficult to scale for
capturing large structures. We present PixelVAE, a VAE model with an
autoregressive decoder based on PixelCNN. Our model requires very few expensive
autoregressive layers compared to PixelCNN and learns latent codes that are
more compressed than a standard VAE while still capturing most non-trivial
structure. Finally, we extend our model to a hierarchy of latent variables at
different scales. Our model achieves state-of-the-art performance on binarized
MNIST, competitive performance on 64x64 ImageNet, and high-quality samples on
the LSUN bedrooms dataset.
| no_new_dataset | 0.949201 |
0803.3363 | Yoshiharu Maeno | Yoshiharu Maeno | Node discovery in a networked organization | null | Proceedings of the IEEE International Conference on Systems, Man
and Cybernetics, San Antonio, October 2009 | 10.1109/ICSMC.2009.5346826 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, I present a method to solve a node discovery problem in a
networked organization. Covert nodes refer to the nodes which are not
observable directly. They affect social interactions, but do not appear in the
surveillance logs which record the participants of the social interactions.
Discovering the covert nodes is defined as identifying the suspicious logs
where the covert nodes would appear if the covert nodes became overt. A
mathematical model is developed for the maximal likelihood estimation of the
network behind the social interactions and for the identification of the
suspicious logs. Precision, recall, and F measure characteristics are
demonstrated with the dataset generated from a real organization and the
computationally synthesized datasets. The performance is close to the
theoretical limit for any covert nodes in the networks of any topologies and
sizes if the ratio of the number of observation to the number of possible
communication patterns is large.
| [
{
"version": "v1",
"created": "Mon, 24 Mar 2008 05:53:39 GMT"
},
{
"version": "v2",
"created": "Fri, 26 Jun 2009 05:13:58 GMT"
}
] | 2016-11-15T00:00:00 | [
[
"Maeno",
"Yoshiharu",
""
]
] | TITLE: Node discovery in a networked organization
ABSTRACT: In this paper, I present a method to solve a node discovery problem in a
networked organization. Covert nodes refer to the nodes which are not
observable directly. They affect social interactions, but do not appear in the
surveillance logs which record the participants of the social interactions.
Discovering the covert nodes is defined as identifying the suspicious logs
where the covert nodes would appear if the covert nodes became overt. A
mathematical model is developed for the maximal likelihood estimation of the
network behind the social interactions and for the identification of the
suspicious logs. Precision, recall, and F measure characteristics are
demonstrated with the dataset generated from a real organization and the
computationally synthesized datasets. The performance is close to the
theoretical limit for any covert nodes in the networks of any topologies and
sizes if the ratio of the number of observation to the number of possible
communication patterns is large.
| no_new_dataset | 0.949435 |
1007.5459 | John Whitbeck | John Whitbeck, Yoann Lopez, J\'er\'emie Leguay, Vania Conan, Marcelo
Dias de Amorim | Relieving the Wireless Infrastructure: When Opportunistic Networks Meet
Guaranteed Delays | Accepted at IEEE WoWMoM 2011 conference | null | 10.1109/WoWMoM.2011.5986466 | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Major wireless operators are nowadays facing network capacity issues in
striving to meet the growing demands of mobile users. At the same time,
3G-enabled devices increasingly benefit from ad hoc radio connectivity (e.g.,
Wi-Fi). In this context of hybrid connectivity, we propose Push-and-track, a
content dissemination framework that harnesses ad hoc communication
opportunities to minimize the load on the wireless infrastructure while
guaranteeing tight delivery delays. It achieves this through a control loop
that collects user-sent acknowledgements to determine if new copies need to be
reinjected into the network through the 3G interface. Push-and-Track includes
multiple strategies to determine how many copies of the content should be
injected, when, and to whom. The short delay-tolerance of common content, such
as news or road traffic updates, make them suitable for such a system. Based on
a realistic large-scale vehicular dataset from the city of Bologna composed of
more than 10,000 vehicles, we demonstrate that Push-and-Track consistently
meets its delivery objectives while reducing the use of the 3G network by over
90%.
| [
{
"version": "v1",
"created": "Fri, 30 Jul 2010 14:26:53 GMT"
},
{
"version": "v2",
"created": "Sat, 4 Dec 2010 21:44:42 GMT"
},
{
"version": "v3",
"created": "Mon, 30 May 2011 20:17:57 GMT"
}
] | 2016-11-15T00:00:00 | [
[
"Whitbeck",
"John",
""
],
[
"Lopez",
"Yoann",
""
],
[
"Leguay",
"Jérémie",
""
],
[
"Conan",
"Vania",
""
],
[
"de Amorim",
"Marcelo Dias",
""
]
] | TITLE: Relieving the Wireless Infrastructure: When Opportunistic Networks Meet
Guaranteed Delays
ABSTRACT: Major wireless operators are nowadays facing network capacity issues in
striving to meet the growing demands of mobile users. At the same time,
3G-enabled devices increasingly benefit from ad hoc radio connectivity (e.g.,
Wi-Fi). In this context of hybrid connectivity, we propose Push-and-track, a
content dissemination framework that harnesses ad hoc communication
opportunities to minimize the load on the wireless infrastructure while
guaranteeing tight delivery delays. It achieves this through a control loop
that collects user-sent acknowledgements to determine if new copies need to be
reinjected into the network through the 3G interface. Push-and-Track includes
multiple strategies to determine how many copies of the content should be
injected, when, and to whom. The short delay-tolerance of common content, such
as news or road traffic updates, make them suitable for such a system. Based on
a realistic large-scale vehicular dataset from the city of Bologna composed of
more than 10,000 vehicles, we demonstrate that Push-and-Track consistently
meets its delivery objectives while reducing the use of the 3G network by over
90%.
| no_new_dataset | 0.784443 |
1010.5669 | Pascal Pernot | Pascal Pernot and Fabien Cailliez | Semi-empirical correction of ab initio harmonic properties by scaling
factors: a validated uncertainty model for calibration and prediction | null | null | null | null | physics.chem-ph physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bayesian Model Calibration is used to revisit the problem of scaling factor
calibration for semi-empirical correction of ab initio harmonic properties
(e.g. vibrational frequencies and zero-point energies). A particular attention
is devoted to the evaluation of scaling factor uncertainty, and to its effect
on the accuracy of scaled properties. We argue that in most cases of interest
the standard calibration model is not statistically valid, in the sense that it
is not able to fit experimental calibration data within their uncertainty
limits. This impairs any attempt to use the results of the standard model for
uncertainty analysis and/or uncertainty propagation. We propose to include a
stochastic term in the calibration model to account for model inadequacy. This
new model is validated in the Bayesian Model Calibration framework. We provide
explicit formulae for prediction uncertainty in typical limit cases: large and
small calibration sets of data with negligible measurement uncertainty, and
datasets with large measurement uncertainties.
| [
{
"version": "v1",
"created": "Wed, 27 Oct 2010 12:27:03 GMT"
}
] | 2016-11-15T00:00:00 | [
[
"Pernot",
"Pascal",
""
],
[
"Cailliez",
"Fabien",
""
]
] | TITLE: Semi-empirical correction of ab initio harmonic properties by scaling
factors: a validated uncertainty model for calibration and prediction
ABSTRACT: Bayesian Model Calibration is used to revisit the problem of scaling factor
calibration for semi-empirical correction of ab initio harmonic properties
(e.g. vibrational frequencies and zero-point energies). A particular attention
is devoted to the evaluation of scaling factor uncertainty, and to its effect
on the accuracy of scaled properties. We argue that in most cases of interest
the standard calibration model is not statistically valid, in the sense that it
is not able to fit experimental calibration data within their uncertainty
limits. This impairs any attempt to use the results of the standard model for
uncertainty analysis and/or uncertainty propagation. We propose to include a
stochastic term in the calibration model to account for model inadequacy. This
new model is validated in the Bayesian Model Calibration framework. We provide
explicit formulae for prediction uncertainty in typical limit cases: large and
small calibration sets of data with negligible measurement uncertainty, and
datasets with large measurement uncertainties.
| no_new_dataset | 0.951414 |
1012.0726 | John Tang | John Tang, Cecilia Mascolo, Mirco Musolesi, Vito Latora | Exploiting Temporal Complex Network Metrics in Mobile Malware
Containment | 9 Pages, 13 Figures, In Proceedings of IEEE 12th International
Symposium on a World of Wireless, Mobile and Multimedia Networks (WOWMOM '11) | null | 10.1109/WoWMoM.2011.5986463 | null | cs.NI physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Malicious mobile phone worms spread between devices via short-range Bluetooth
contacts, similar to the propagation of human and other biological viruses.
Recent work has employed models from epidemiology and complex networks to
analyse the spread of malware and the effect of patching specific nodes. These
approaches have adopted a static view of the mobile networks, i.e., by
aggregating all the edges that appear over time, which leads to an approximate
representation of the real interactions: instead, these networks are inherently
dynamic and the edge appearance and disappearance is highly influenced by the
ordering of the human contacts, something which is not captured at all by
existing complex network measures. In this paper we first study how the
blocking of malware propagation through immunisation of key nodes (even if
carefully chosen through static or temporal betweenness centrality metrics) is
ineffective: this is due to the richness of alternative paths in these
networks. Then we introduce a time-aware containment strategy that spreads a
patch message starting from nodes with high temporal closeness centrality and
show its effectiveness using three real-world datasets. Temporal closeness
allows the identification of nodes able to reach most nodes quickly: we show
that this scheme can reduce the cellular network resource consumption and
associated costs, achieving, at the same time, a complete containment of the
malware in a limited amount of time.
| [
{
"version": "v1",
"created": "Fri, 3 Dec 2010 13:03:58 GMT"
},
{
"version": "v2",
"created": "Tue, 10 May 2011 19:32:14 GMT"
}
] | 2016-11-15T00:00:00 | [
[
"Tang",
"John",
""
],
[
"Mascolo",
"Cecilia",
""
],
[
"Musolesi",
"Mirco",
""
],
[
"Latora",
"Vito",
""
]
] | TITLE: Exploiting Temporal Complex Network Metrics in Mobile Malware
Containment
ABSTRACT: Malicious mobile phone worms spread between devices via short-range Bluetooth
contacts, similar to the propagation of human and other biological viruses.
Recent work has employed models from epidemiology and complex networks to
analyse the spread of malware and the effect of patching specific nodes. These
approaches have adopted a static view of the mobile networks, i.e., by
aggregating all the edges that appear over time, which leads to an approximate
representation of the real interactions: instead, these networks are inherently
dynamic and the edge appearance and disappearance is highly influenced by the
ordering of the human contacts, something which is not captured at all by
existing complex network measures. In this paper we first study how the
blocking of malware propagation through immunisation of key nodes (even if
carefully chosen through static or temporal betweenness centrality metrics) is
ineffective: this is due to the richness of alternative paths in these
networks. Then we introduce a time-aware containment strategy that spreads a
patch message starting from nodes with high temporal closeness centrality and
show its effectiveness using three real-world datasets. Temporal closeness
allows the identification of nodes able to reach most nodes quickly: we show
that this scheme can reduce the cellular network resource consumption and
associated costs, achieving, at the same time, a complete containment of the
malware in a limited amount of time.
| no_new_dataset | 0.949576 |
1103.2635 | Lawrence Cayton | Lawrence Cayton | Accelerating Nearest Neighbor Search on Manycore Systems | null | In Proceedings of the 2012 IEEE 26th International Parallel and
Distributed Processing Symposium (IPDPS '12). IEEE Computer Society,
Washington, DC, USA, 402-413 | 10.1109/IPDPS.2012.45 | null | cs.DB cs.CG cs.DC cs.DS cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We develop methods for accelerating metric similarity search that are
effective on modern hardware. Our algorithms factor into easily parallelizable
components, making them simple to deploy and efficient on multicore CPUs and
GPUs. Despite the simple structure of our algorithms, their search performance
is provably sublinear in the size of the database, with a factor dependent only
on its intrinsic dimensionality. We demonstrate that our methods provide
substantial speedups on a range of datasets and hardware platforms. In
particular, we present results on a 48-core server machine, on graphics
hardware, and on a multicore desktop.
| [
{
"version": "v1",
"created": "Mon, 14 Mar 2011 11:39:23 GMT"
},
{
"version": "v2",
"created": "Wed, 30 Mar 2011 18:26:44 GMT"
}
] | 2016-11-15T00:00:00 | [
[
"Cayton",
"Lawrence",
""
]
] | TITLE: Accelerating Nearest Neighbor Search on Manycore Systems
ABSTRACT: We develop methods for accelerating metric similarity search that are
effective on modern hardware. Our algorithms factor into easily parallelizable
components, making them simple to deploy and efficient on multicore CPUs and
GPUs. Despite the simple structure of our algorithms, their search performance
is provably sublinear in the size of the database, with a factor dependent only
on its intrinsic dimensionality. We demonstrate that our methods provide
substantial speedups on a range of datasets and hardware platforms. In
particular, we present results on a 48-core server machine, on graphics
hardware, and on a multicore desktop.
| no_new_dataset | 0.952442 |
1201.1174 | Yongjun Liao | Yongjun Liao, Wei Du, Pierre Geurts and Guy Leduc | DMFSGD: A Decentralized Matrix Factorization Algorithm for Network
Distance Prediction | submitted to IEEE/ACM Transactions on Networking on Nov. 2011 | null | 10.1109/TNET.2012.2228881 | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The knowledge of end-to-end network distances is essential to many Internet
applications. As active probing of all pairwise distances is infeasible in
large-scale networks, a natural idea is to measure a few pairs and to predict
the other ones without actually measuring them. This paper formulates the
distance prediction problem as matrix completion where unknown entries of an
incomplete matrix of pairwise distances are to be predicted. The problem is
solvable because strong correlations among network distances exist and cause
the constructed distance matrix to be low rank. The new formulation circumvents
the well-known drawbacks of existing approaches based on Euclidean embedding.
A new algorithm, so-called Decentralized Matrix Factorization by Stochastic
Gradient Descent (DMFSGD), is proposed to solve the network distance prediction
problem. By letting network nodes exchange messages with each other, the
algorithm is fully decentralized and only requires each node to collect and to
process local measurements, with neither explicit matrix constructions nor
special nodes such as landmarks and central servers. In addition, we compared
comprehensively matrix factorization and Euclidean embedding to demonstrate the
suitability of the former on network distance prediction. We further studied
the incorporation of a robust loss function and of non-negativity constraints.
Extensive experiments on various publicly-available datasets of network delays
show not only the scalability and the accuracy of our approach but also its
usability in real Internet applications.
| [
{
"version": "v1",
"created": "Thu, 5 Jan 2012 14:02:16 GMT"
}
] | 2016-11-15T00:00:00 | [
[
"Liao",
"Yongjun",
""
],
[
"Du",
"Wei",
""
],
[
"Geurts",
"Pierre",
""
],
[
"Leduc",
"Guy",
""
]
] | TITLE: DMFSGD: A Decentralized Matrix Factorization Algorithm for Network
Distance Prediction
ABSTRACT: The knowledge of end-to-end network distances is essential to many Internet
applications. As active probing of all pairwise distances is infeasible in
large-scale networks, a natural idea is to measure a few pairs and to predict
the other ones without actually measuring them. This paper formulates the
distance prediction problem as matrix completion where unknown entries of an
incomplete matrix of pairwise distances are to be predicted. The problem is
solvable because strong correlations among network distances exist and cause
the constructed distance matrix to be low rank. The new formulation circumvents
the well-known drawbacks of existing approaches based on Euclidean embedding.
A new algorithm, so-called Decentralized Matrix Factorization by Stochastic
Gradient Descent (DMFSGD), is proposed to solve the network distance prediction
problem. By letting network nodes exchange messages with each other, the
algorithm is fully decentralized and only requires each node to collect and to
process local measurements, with neither explicit matrix constructions nor
special nodes such as landmarks and central servers. In addition, we compared
comprehensively matrix factorization and Euclidean embedding to demonstrate the
suitability of the former on network distance prediction. We further studied
the incorporation of a robust loss function and of non-negativity constraints.
Extensive experiments on various publicly-available datasets of network delays
show not only the scalability and the accuracy of our approach but also its
usability in real Internet applications.
| no_new_dataset | 0.948298 |
1401.0764 | Chunhua Shen | Xi Li, Weiming Hu, Chunhua Shen, Anthony Dick, Zhongfei Zhang | Context-Aware Hypergraph Construction for Robust Spectral Clustering | 10 pages. Appearing in IEEE TRANSACTIONS ON KNOWLEDGE AND DATA
ENGINEERING: http://doi.ieeecomputersociety.org/10.1109/TKDE.2013.126 | null | 10.1109/TKDE.2013.126 | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Spectral clustering is a powerful tool for unsupervised data analysis. In
this paper, we propose a context-aware hypergraph similarity measure (CAHSM),
which leads to robust spectral clustering in the case of noisy data. We
construct three types of hypergraph---the pairwise hypergraph, the
k-nearest-neighbor (kNN) hypergraph, and the high-order over-clustering
hypergraph. The pairwise hypergraph captures the pairwise similarity of data
points; the kNN hypergraph captures the neighborhood of each point; and the
clustering hypergraph encodes high-order contexts within the dataset. By
combining the affinity information from these three hypergraphs, the CAHSM
algorithm is able to explore the intrinsic topological information of the
dataset. Therefore, data clustering using CAHSM tends to be more robust.
Considering the intra-cluster compactness and the inter-cluster separability of
vertices, we further design a discriminative hypergraph partitioning criterion
(DHPC). Using both CAHSM and DHPC, a robust spectral clustering algorithm is
developed. Theoretical analysis and experimental evaluation demonstrate the
effectiveness and robustness of the proposed algorithm.
| [
{
"version": "v1",
"created": "Sat, 4 Jan 2014 02:05:35 GMT"
}
] | 2016-11-15T00:00:00 | [
[
"Li",
"Xi",
""
],
[
"Hu",
"Weiming",
""
],
[
"Shen",
"Chunhua",
""
],
[
"Dick",
"Anthony",
""
],
[
"Zhang",
"Zhongfei",
""
]
] | TITLE: Context-Aware Hypergraph Construction for Robust Spectral Clustering
ABSTRACT: Spectral clustering is a powerful tool for unsupervised data analysis. In
this paper, we propose a context-aware hypergraph similarity measure (CAHSM),
which leads to robust spectral clustering in the case of noisy data. We
construct three types of hypergraph---the pairwise hypergraph, the
k-nearest-neighbor (kNN) hypergraph, and the high-order over-clustering
hypergraph. The pairwise hypergraph captures the pairwise similarity of data
points; the kNN hypergraph captures the neighborhood of each point; and the
clustering hypergraph encodes high-order contexts within the dataset. By
combining the affinity information from these three hypergraphs, the CAHSM
algorithm is able to explore the intrinsic topological information of the
dataset. Therefore, data clustering using CAHSM tends to be more robust.
Considering the intra-cluster compactness and the inter-cluster separability of
vertices, we further design a discriminative hypergraph partitioning criterion
(DHPC). Using both CAHSM and DHPC, a robust spectral clustering algorithm is
developed. Theoretical analysis and experimental evaluation demonstrate the
effectiveness and robustness of the proposed algorithm.
| no_new_dataset | 0.951997 |
1404.2999 | Tianlin Shi | Tianlin Shi, Liang Ming, Xiaolin Hu | A Reverse Hierarchy Model for Predicting Eye Fixations | CVPR 2014, 27th IEEE Conference on Computer Vision and Pattern
Recognition (CVPR). CVPR 2014 | null | 10.1109/CVPR.2014.361 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A number of psychological and physiological evidences suggest that early
visual attention works in a coarse-to-fine way, which lays a basis for the
reverse hierarchy theory (RHT). This theory states that attention propagates
from the top level of the visual hierarchy that processes gist and abstract
information of input, to the bottom level that processes local details.
Inspired by the theory, we develop a computational model for saliency detection
in images. First, the original image is downsampled to different scales to
constitute a pyramid. Then, saliency on each layer is obtained by image
super-resolution reconstruction from the layer above, which is defined as
unpredictability from this coarse-to-fine reconstruction. Finally, saliency on
each layer of the pyramid is fused into stochastic fixations through a
probabilistic model, where attention initiates from the top layer and
propagates downward through the pyramid. Extensive experiments on two standard
eye-tracking datasets show that the proposed method can achieve competitive
results with state-of-the-art models.
| [
{
"version": "v1",
"created": "Fri, 11 Apr 2014 04:39:21 GMT"
}
] | 2016-11-15T00:00:00 | [
[
"Shi",
"Tianlin",
""
],
[
"Ming",
"Liang",
""
],
[
"Hu",
"Xiaolin",
""
]
] | TITLE: A Reverse Hierarchy Model for Predicting Eye Fixations
ABSTRACT: A number of psychological and physiological evidences suggest that early
visual attention works in a coarse-to-fine way, which lays a basis for the
reverse hierarchy theory (RHT). This theory states that attention propagates
from the top level of the visual hierarchy that processes gist and abstract
information of input, to the bottom level that processes local details.
Inspired by the theory, we develop a computational model for saliency detection
in images. First, the original image is downsampled to different scales to
constitute a pyramid. Then, saliency on each layer is obtained by image
super-resolution reconstruction from the layer above, which is defined as
unpredictability from this coarse-to-fine reconstruction. Finally, saliency on
each layer of the pyramid is fused into stochastic fixations through a
probabilistic model, where attention initiates from the top layer and
propagates downward through the pyramid. Extensive experiments on two standard
eye-tracking datasets show that the proposed method can achieve competitive
results with state-of-the-art models.
| no_new_dataset | 0.950134 |
1407.7330 | Arnold Wiliem | Arnold Wiliem, Peter Hobson, Brian C. Lovell | Discovering Discriminative Cell Attributes for HEp-2 Specimen Image
Classification | WACV 2014: IEEE Winter Conference on Applications of Computer Vision | null | 10.1109/WACV.2014.6836071 | null | cs.CV cs.CE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, there has been a growing interest in developing Computer Aided
Diagnostic (CAD) systems for improving the reliability and consistency of
pathology test results. This paper describes a novel CAD system for the
Anti-Nuclear Antibody (ANA) test via Indirect Immunofluorescence protocol on
Human Epithelial Type 2 (HEp-2) cells. While prior works have primarily focused
on classifying cell images extracted from ANA specimen images, this work takes
a further step by focussing on the specimen image classification problem
itself. Our system is able to efficiently classify specimen images as well as
producing meaningful descriptions of ANA pattern class which helps physicians
to understand the differences between various ANA patterns. We achieve this
goal by designing a specimen-level image descriptor that: (1) is highly
discriminative; (2) has small descriptor length and (3) is semantically
meaningful at the cell level. In our work, a specimen image descriptor is
represented by its overall cell attribute descriptors. As such, we propose two
max-margin based learning schemes to discover cell attributes whilst still
maintaining the discrimination of the specimen image descriptor. Our learning
schemes differ from the existing discriminative attribute learning approaches
as they primarily focus on discovering image-level attributes. Comparative
evaluations were undertaken to contrast the proposed approach to various
state-of-the-art approaches on a novel HEp-2 cell dataset which was
specifically proposed for the specimen-level classification. Finally, we
showcase the ability of the proposed approach to provide textual descriptions
to explain ANA patterns.
| [
{
"version": "v1",
"created": "Mon, 28 Jul 2014 06:03:03 GMT"
}
] | 2016-11-15T00:00:00 | [
[
"Wiliem",
"Arnold",
""
],
[
"Hobson",
"Peter",
""
],
[
"Lovell",
"Brian C.",
""
]
] | TITLE: Discovering Discriminative Cell Attributes for HEp-2 Specimen Image
Classification
ABSTRACT: Recently, there has been a growing interest in developing Computer Aided
Diagnostic (CAD) systems for improving the reliability and consistency of
pathology test results. This paper describes a novel CAD system for the
Anti-Nuclear Antibody (ANA) test via Indirect Immunofluorescence protocol on
Human Epithelial Type 2 (HEp-2) cells. While prior works have primarily focused
on classifying cell images extracted from ANA specimen images, this work takes
a further step by focussing on the specimen image classification problem
itself. Our system is able to efficiently classify specimen images as well as
producing meaningful descriptions of ANA pattern class which helps physicians
to understand the differences between various ANA patterns. We achieve this
goal by designing a specimen-level image descriptor that: (1) is highly
discriminative; (2) has small descriptor length and (3) is semantically
meaningful at the cell level. In our work, a specimen image descriptor is
represented by its overall cell attribute descriptors. As such, we propose two
max-margin based learning schemes to discover cell attributes whilst still
maintaining the discrimination of the specimen image descriptor. Our learning
schemes differ from the existing discriminative attribute learning approaches
as they primarily focus on discovering image-level attributes. Comparative
evaluations were undertaken to contrast the proposed approach to various
state-of-the-art approaches on a novel HEp-2 cell dataset which was
specifically proposed for the specimen-level classification. Finally, we
showcase the ability of the proposed approach to provide textual descriptions
to explain ANA patterns.
| no_new_dataset | 0.94256 |
1409.0788 | Uwe Aickelin | Chris Roadknight, Uwe Aickelin, John Scholefield, Lindy Durrant | Ensemble Learning of Colorectal Cancer Survival Rates | IEEE International Conference on Computational Intelligence and
Virtual Environments for Measurement Systems and Applications (CIVEMSA) 2013,
pp. 82 - 86, 2013 | null | 10.1109/CIVEMSA.2013.6617400 | null | cs.LG cs.CE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we describe a dataset relating to cellular and physical
conditions of patients who are operated upon to remove colorectal tumours. This
data provides a unique insight into immunological status at the point of tumour
removal, tumour classification and post-operative survival. We build on
existing research on clustering and machine learning facets of this data to
demonstrate a role for an ensemble approach to highlighting patients with
clearer prognosis parameters. Results for survival prediction using 3 different
approaches are shown for a subset of the data which is most difficult to model.
The performance of each model individually is compared with subsets of the data
where some agreement is reached for multiple models. Significant improvements
in model accuracy on an unseen test set can be achieved for patients where
agreement between models is achieved.
| [
{
"version": "v1",
"created": "Tue, 2 Sep 2014 16:52:16 GMT"
}
] | 2016-11-15T00:00:00 | [
[
"Roadknight",
"Chris",
""
],
[
"Aickelin",
"Uwe",
""
],
[
"Scholefield",
"John",
""
],
[
"Durrant",
"Lindy",
""
]
] | TITLE: Ensemble Learning of Colorectal Cancer Survival Rates
ABSTRACT: In this paper, we describe a dataset relating to cellular and physical
conditions of patients who are operated upon to remove colorectal tumours. This
data provides a unique insight into immunological status at the point of tumour
removal, tumour classification and post-operative survival. We build on
existing research on clustering and machine learning facets of this data to
demonstrate a role for an ensemble approach to highlighting patients with
clearer prognosis parameters. Results for survival prediction using 3 different
approaches are shown for a subset of the data which is most difficult to model.
The performance of each model individually is compared with subsets of the data
where some agreement is reached for multiple models. Significant improvements
in model accuracy on an unseen test set can be achieved for patients where
agreement between models is achieved.
| new_dataset | 0.971102 |
1409.2195 | Daniel Fried | Daniel Fried, Mihai Surdeanu, Stephen Kobourov, Melanie Hingle, Dane
Bell | Analyzing the Language of Food on Social Media | An extended abstract of this paper will appear in IEEE Big Data 2014 | null | 10.1109/BigData.2014.7004305 | null | cs.CL cs.CY cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate the predictive power behind the language of food on social
media. We collect a corpus of over three million food-related posts from
Twitter and demonstrate that many latent population characteristics can be
directly predicted from this data: overweight rate, diabetes rate, political
leaning, and home geographical location of authors. For all tasks, our
language-based models significantly outperform the majority-class baselines.
Performance is further improved with more complex natural language processing,
such as topic modeling. We analyze which textual features have most predictive
power for these datasets, providing insight into the connections between the
language of food, geographic locale, and community characteristics. Lastly, we
design and implement an online system for real-time query and visualization of
the dataset. Visualization tools, such as geo-referenced heatmaps,
semantics-preserving wordclouds and temporal histograms, allow us to discover
more complex, global patterns mirrored in the language of food.
| [
{
"version": "v1",
"created": "Mon, 8 Sep 2014 03:07:54 GMT"
},
{
"version": "v2",
"created": "Thu, 11 Sep 2014 17:35:02 GMT"
}
] | 2016-11-15T00:00:00 | [
[
"Fried",
"Daniel",
""
],
[
"Surdeanu",
"Mihai",
""
],
[
"Kobourov",
"Stephen",
""
],
[
"Hingle",
"Melanie",
""
],
[
"Bell",
"Dane",
""
]
] | TITLE: Analyzing the Language of Food on Social Media
ABSTRACT: We investigate the predictive power behind the language of food on social
media. We collect a corpus of over three million food-related posts from
Twitter and demonstrate that many latent population characteristics can be
directly predicted from this data: overweight rate, diabetes rate, political
leaning, and home geographical location of authors. For all tasks, our
language-based models significantly outperform the majority-class baselines.
Performance is further improved with more complex natural language processing,
such as topic modeling. We analyze which textual features have most predictive
power for these datasets, providing insight into the connections between the
language of food, geographic locale, and community characteristics. Lastly, we
design and implement an online system for real-time query and visualization of
the dataset. Visualization tools, such as geo-referenced heatmaps,
semantics-preserving wordclouds and temporal histograms, allow us to discover
more complex, global patterns mirrored in the language of food.
| no_new_dataset | 0.943138 |
1502.07019 | Shreyansh Daftry | Shreyansh Daftry, Christof Hoppe and Horst Bischof | Building with Drones: Accurate 3D Facade Reconstruction using MAVs | 8 Pages, 2015 IEEE International Conference on Robotics and
Automation (ICRA '15), Seattle, WA, USA | null | 10.1109/ICRA.2015.7139681 | null | cs.RO cs.AI cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Automatic reconstruction of 3D models from images using multi-view
Structure-from-Motion methods has been one of the most fruitful outcomes of
computer vision. These advances combined with the growing popularity of Micro
Aerial Vehicles as an autonomous imaging platform, have made 3D vision tools
ubiquitous for large number of Architecture, Engineering and Construction
applications among audiences, mostly unskilled in computer vision. However, to
obtain high-resolution and accurate reconstructions from a large-scale object
using SfM, there are many critical constraints on the quality of image data,
which often become sources of inaccuracy as the current 3D reconstruction
pipelines do not facilitate the users to determine the fidelity of input data
during the image acquisition. In this paper, we present and advocate a
closed-loop interactive approach that performs incremental reconstruction in
real-time and gives users an online feedback about the quality parameters like
Ground Sampling Distance (GSD), image redundancy, etc on a surface mesh. We
also propose a novel multi-scale camera network design to prevent scene drift
caused by incremental map building, and release the first multi-scale image
sequence dataset as a benchmark. Further, we evaluate our system on real
outdoor scenes, and show that our interactive pipeline combined with a
multi-scale camera network approach provides compelling accuracy in multi-view
reconstruction tasks when compared against the state-of-the-art methods.
| [
{
"version": "v1",
"created": "Wed, 25 Feb 2015 00:52:11 GMT"
}
] | 2016-11-15T00:00:00 | [
[
"Daftry",
"Shreyansh",
""
],
[
"Hoppe",
"Christof",
""
],
[
"Bischof",
"Horst",
""
]
] | TITLE: Building with Drones: Accurate 3D Facade Reconstruction using MAVs
ABSTRACT: Automatic reconstruction of 3D models from images using multi-view
Structure-from-Motion methods has been one of the most fruitful outcomes of
computer vision. These advances combined with the growing popularity of Micro
Aerial Vehicles as an autonomous imaging platform, have made 3D vision tools
ubiquitous for large number of Architecture, Engineering and Construction
applications among audiences, mostly unskilled in computer vision. However, to
obtain high-resolution and accurate reconstructions from a large-scale object
using SfM, there are many critical constraints on the quality of image data,
which often become sources of inaccuracy as the current 3D reconstruction
pipelines do not facilitate the users to determine the fidelity of input data
during the image acquisition. In this paper, we present and advocate a
closed-loop interactive approach that performs incremental reconstruction in
real-time and gives users an online feedback about the quality parameters like
Ground Sampling Distance (GSD), image redundancy, etc on a surface mesh. We
also propose a novel multi-scale camera network design to prevent scene drift
caused by incremental map building, and release the first multi-scale image
sequence dataset as a benchmark. Further, we evaluate our system on real
outdoor scenes, and show that our interactive pipeline combined with a
multi-scale camera network approach provides compelling accuracy in multi-view
reconstruction tasks when compared against the state-of-the-art methods.
| no_new_dataset | 0.937268 |
1503.02391 | Xiaodan Liang | Xiaodan Liang, Si Liu, Xiaohui Shen, Jianchao Yang, Luoqi Liu, Jian
Dong, Liang Lin, Shuicheng Yan | Deep Human Parsing with Active Template Regression | This manuscript is the accepted version for IEEE Transactions on
Pattern Analysis and Machine Intelligence (TPAMI) 2015 | null | 10.1109/TPAMI.2015.2408360 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, the human parsing task, namely decomposing a human image into
semantic fashion/body regions, is formulated as an Active Template Regression
(ATR) problem, where the normalized mask of each fashion/body item is expressed
as the linear combination of the learned mask templates, and then morphed to a
more precise mask with the active shape parameters, including position, scale
and visibility of each semantic region. The mask template coefficients and the
active shape parameters together can generate the human parsing results, and
are thus called the structure outputs for human parsing. The deep Convolutional
Neural Network (CNN) is utilized to build the end-to-end relation between the
input human image and the structure outputs for human parsing. More
specifically, the structure outputs are predicted by two separate networks. The
first CNN network is with max-pooling, and designed to predict the template
coefficients for each label mask, while the second CNN network is without
max-pooling to preserve sensitivity to label mask position and accurately
predict the active shape parameters. For a new image, the structure outputs of
the two networks are fused to generate the probability of each label for each
pixel, and super-pixel smoothing is finally used to refine the human parsing
result. Comprehensive evaluations on a large dataset well demonstrate the
significant superiority of the ATR framework over other state-of-the-arts for
human parsing. In particular, the F1-score reaches $64.38\%$ by our ATR
framework, significantly higher than $44.76\%$ based on the state-of-the-art
algorithm.
| [
{
"version": "v1",
"created": "Mon, 9 Mar 2015 08:14:12 GMT"
}
] | 2016-11-15T00:00:00 | [
[
"Liang",
"Xiaodan",
""
],
[
"Liu",
"Si",
""
],
[
"Shen",
"Xiaohui",
""
],
[
"Yang",
"Jianchao",
""
],
[
"Liu",
"Luoqi",
""
],
[
"Dong",
"Jian",
""
],
[
"Lin",
"Liang",
""
],
[
"Yan",
"Shuicheng",
""
]
] | TITLE: Deep Human Parsing with Active Template Regression
ABSTRACT: In this work, the human parsing task, namely decomposing a human image into
semantic fashion/body regions, is formulated as an Active Template Regression
(ATR) problem, where the normalized mask of each fashion/body item is expressed
as the linear combination of the learned mask templates, and then morphed to a
more precise mask with the active shape parameters, including position, scale
and visibility of each semantic region. The mask template coefficients and the
active shape parameters together can generate the human parsing results, and
are thus called the structure outputs for human parsing. The deep Convolutional
Neural Network (CNN) is utilized to build the end-to-end relation between the
input human image and the structure outputs for human parsing. More
specifically, the structure outputs are predicted by two separate networks. The
first CNN network is with max-pooling, and designed to predict the template
coefficients for each label mask, while the second CNN network is without
max-pooling to preserve sensitivity to label mask position and accurately
predict the active shape parameters. For a new image, the structure outputs of
the two networks are fused to generate the probability of each label for each
pixel, and super-pixel smoothing is finally used to refine the human parsing
result. Comprehensive evaluations on a large dataset well demonstrate the
significant superiority of the ATR framework over other state-of-the-arts for
human parsing. In particular, the F1-score reaches $64.38\%$ by our ATR
framework, significantly higher than $44.76\%$ based on the state-of-the-art
algorithm.
| no_new_dataset | 0.954942 |
1503.08479 | Lex Fridman | Lex Fridman, Steven Weber, Rachel Greenstadt, Moshe Kam | Active Authentication on Mobile Devices via Stylometry, Application
Usage, Web Browsing, and GPS Location | Accepted for Publication in the IEEE Systems Journal | null | 10.1109/JSYST.2015.2472579 | null | cs.CR stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Active authentication is the problem of continuously verifying the identity
of a person based on behavioral aspects of their interaction with a computing
device. In this study, we collect and analyze behavioral biometrics data from
200subjects, each using their personal Android mobile device for a period of at
least 30 days. This dataset is novel in the context of active authentication
due to its size, duration, number of modalities, and absence of restrictions on
tracked activity. The geographical colocation of the subjects in the study is
representative of a large closed-world environment such as an organization
where the unauthorized user of a device is likely to be an insider threat:
coming from within the organization. We consider four biometric modalities: (1)
text entered via soft keyboard, (2) applications used, (3) websites visited,
and (4) physical location of the device as determined from GPS (when outdoors)
or WiFi (when indoors). We implement and test a classifier for each modality
and organize the classifiers as a parallel binary decision fusion architecture.
We are able to characterize the performance of the system with respect to
intruder detection time and to quantify the contribution of each modality to
the overall performance.
| [
{
"version": "v1",
"created": "Sun, 29 Mar 2015 18:59:23 GMT"
}
] | 2016-11-15T00:00:00 | [
[
"Fridman",
"Lex",
""
],
[
"Weber",
"Steven",
""
],
[
"Greenstadt",
"Rachel",
""
],
[
"Kam",
"Moshe",
""
]
] | TITLE: Active Authentication on Mobile Devices via Stylometry, Application
Usage, Web Browsing, and GPS Location
ABSTRACT: Active authentication is the problem of continuously verifying the identity
of a person based on behavioral aspects of their interaction with a computing
device. In this study, we collect and analyze behavioral biometrics data from
200subjects, each using their personal Android mobile device for a period of at
least 30 days. This dataset is novel in the context of active authentication
due to its size, duration, number of modalities, and absence of restrictions on
tracked activity. The geographical colocation of the subjects in the study is
representative of a large closed-world environment such as an organization
where the unauthorized user of a device is likely to be an insider threat:
coming from within the organization. We consider four biometric modalities: (1)
text entered via soft keyboard, (2) applications used, (3) websites visited,
and (4) physical location of the device as determined from GPS (when outdoors)
or WiFi (when indoors). We implement and test a classifier for each modality
and organize the classifiers as a parallel binary decision fusion architecture.
We are able to characterize the performance of the system with respect to
intruder detection time and to quantify the contribution of each modality to
the overall performance.
| no_new_dataset | 0.765681 |
1505.05212 | Hamid Tizhoosh | Hamid R. Tizhoosh | Barcode Annotations for Medical Image Retrieval: A Preliminary
Investigation | To be published in proceedings of The IEEE International Conference
on Image Processing (ICIP 2015), September 27-30, 2015, Quebec City, Canada | null | 10.1109/ICIP.2015.7350913 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes to generate and to use barcodes to annotate medical
images and/or their regions of interest such as organs, tumors and tissue
types. A multitude of efficient feature-based image retrieval methods already
exist that can assign a query image to a certain image class. Visual
annotations may help to increase the retrieval accuracy if combined with
existing feature-based classification paradigms. Whereas with annotations we
usually mean textual descriptions, in this paper barcode annotations are
proposed. In particular, Radon barcodes (RBC) are introduced. As well, local
binary patterns (LBP) and local Radon binary patterns (LRBP) are implemented as
barcodes. The IRMA x-ray dataset with 12,677 training images and 1,733 test
images is used to verify how barcodes could facilitate image retrieval.
| [
{
"version": "v1",
"created": "Tue, 19 May 2015 23:48:24 GMT"
}
] | 2016-11-15T00:00:00 | [
[
"Tizhoosh",
"Hamid R.",
""
]
] | TITLE: Barcode Annotations for Medical Image Retrieval: A Preliminary
Investigation
ABSTRACT: This paper proposes to generate and to use barcodes to annotate medical
images and/or their regions of interest such as organs, tumors and tissue
types. A multitude of efficient feature-based image retrieval methods already
exist that can assign a query image to a certain image class. Visual
annotations may help to increase the retrieval accuracy if combined with
existing feature-based classification paradigms. Whereas with annotations we
usually mean textual descriptions, in this paper barcode annotations are
proposed. In particular, Radon barcodes (RBC) are introduced. As well, local
binary patterns (LBP) and local Radon binary patterns (LRBP) are implemented as
barcodes. The IRMA x-ray dataset with 12,677 training images and 1,733 test
images is used to verify how barcodes could facilitate image retrieval.
| new_dataset | 0.961061 |
1505.05233 | Lei Zhang | Lei Zhang and David Zhang | Visual Understanding via Multi-Feature Shared Learning with Global
Consistency | 13 pages,6 figures, this paper is accepted for publication in IEEE
Transactions on Multimedia | null | 10.1109/TMM.2015.2510509 | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Image/video data is usually represented with multiple visual features. Fusion
of multi-source information for establishing the attributes has been widely
recognized. Multi-feature visual recognition has recently received much
attention in multimedia applications. This paper studies visual understanding
via a newly proposed l_2-norm based multi-feature shared learning framework,
which can simultaneously learn a global label matrix and multiple
sub-classifiers with the labeled multi-feature data. Additionally, a group
graph manifold regularizer composed of the Laplacian and Hessian graph is
proposed for better preserving the manifold structure of each feature, such
that the label prediction power is much improved through the semi-supervised
learning with global label consistency. For convenience, we call the proposed
approach Global-Label-Consistent Classifier (GLCC). The merits of the proposed
method include: 1) the manifold structure information of each feature is
exploited in learning, resulting in a more faithful classification owing to the
global label consistency; 2) a group graph manifold regularizer based on the
Laplacian and Hessian regularization is constructed; 3) an efficient
alternative optimization method is introduced as a fast solver owing to the
convex sub-problems. Experiments on several benchmark visual datasets for
multimedia understanding, such as the 17-category Oxford Flower dataset, the
challenging 101-category Caltech dataset, the YouTube & Consumer Videos dataset
and the large-scale NUS-WIDE dataset, demonstrate that the proposed approach
compares favorably with the state-of-the-art algorithms. An extensive
experiment on the deep convolutional activation features also show the
effectiveness of the proposed approach. The code is available on
http://www.escience.cn/people/lei/index.html
| [
{
"version": "v1",
"created": "Wed, 20 May 2015 03:01:08 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Sep 2015 10:07:11 GMT"
}
] | 2016-11-15T00:00:00 | [
[
"Zhang",
"Lei",
""
],
[
"Zhang",
"David",
""
]
] | TITLE: Visual Understanding via Multi-Feature Shared Learning with Global
Consistency
ABSTRACT: Image/video data is usually represented with multiple visual features. Fusion
of multi-source information for establishing the attributes has been widely
recognized. Multi-feature visual recognition has recently received much
attention in multimedia applications. This paper studies visual understanding
via a newly proposed l_2-norm based multi-feature shared learning framework,
which can simultaneously learn a global label matrix and multiple
sub-classifiers with the labeled multi-feature data. Additionally, a group
graph manifold regularizer composed of the Laplacian and Hessian graph is
proposed for better preserving the manifold structure of each feature, such
that the label prediction power is much improved through the semi-supervised
learning with global label consistency. For convenience, we call the proposed
approach Global-Label-Consistent Classifier (GLCC). The merits of the proposed
method include: 1) the manifold structure information of each feature is
exploited in learning, resulting in a more faithful classification owing to the
global label consistency; 2) a group graph manifold regularizer based on the
Laplacian and Hessian regularization is constructed; 3) an efficient
alternative optimization method is introduced as a fast solver owing to the
convex sub-problems. Experiments on several benchmark visual datasets for
multimedia understanding, such as the 17-category Oxford Flower dataset, the
challenging 101-category Caltech dataset, the YouTube & Consumer Videos dataset
and the large-scale NUS-WIDE dataset, demonstrate that the proposed approach
compares favorably with the state-of-the-art algorithms. An extensive
experiment on the deep convolutional activation features also show the
effectiveness of the proposed approach. The code is available on
http://www.escience.cn/people/lei/index.html
| no_new_dataset | 0.950134 |
1508.00299 | Pin-Yu Chen | Pin-Yu Chen, Shin-Ming Cheng, Pai-Shun Ting, Chia-Wei Lien, Fu-Jen Chu | When Crowdsourcing Meets Mobile Sensing: A Social Network Perspective | To appear in Oct. IEEE Communications Magazine, feature topic on
"Social Networks Meet Next Generation Mobile Multimedia Internet" | null | 10.1109/MCOM.2015.7295478 | null | cs.SI stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mobile sensing is an emerging technology that utilizes agent-participatory
data for decision making or state estimation, including multimedia
applications. This article investigates the structure of mobile sensing schemes
and introduces crowdsourcing methods for mobile sensing. Inspired by social
network, one can establish trust among participatory agents to leverage the
wisdom of crowds for mobile sensing. A prototype of social network inspired
mobile multimedia and sensing application is presented for illustrative
purpose. Numerical experiments on real-world datasets show improved performance
of mobile sensing via crowdsourcing. Challenges for mobile sensing with respect
to Internet layers are discussed.
| [
{
"version": "v1",
"created": "Mon, 3 Aug 2015 02:01:06 GMT"
}
] | 2016-11-15T00:00:00 | [
[
"Chen",
"Pin-Yu",
""
],
[
"Cheng",
"Shin-Ming",
""
],
[
"Ting",
"Pai-Shun",
""
],
[
"Lien",
"Chia-Wei",
""
],
[
"Chu",
"Fu-Jen",
""
]
] | TITLE: When Crowdsourcing Meets Mobile Sensing: A Social Network Perspective
ABSTRACT: Mobile sensing is an emerging technology that utilizes agent-participatory
data for decision making or state estimation, including multimedia
applications. This article investigates the structure of mobile sensing schemes
and introduces crowdsourcing methods for mobile sensing. Inspired by social
network, one can establish trust among participatory agents to leverage the
wisdom of crowds for mobile sensing. A prototype of social network inspired
mobile multimedia and sensing application is presented for illustrative
purpose. Numerical experiments on real-world datasets show improved performance
of mobile sensing via crowdsourcing. Challenges for mobile sensing with respect
to Internet layers are discussed.
| no_new_dataset | 0.95388 |
1508.06483 | Naoki Hamada | Naoki Hamada, Katsumi Homma, Hiroyuki Higuchi and Hideyuki Kikuchi | Population Synthesis via k-Nearest Neighbor Crossover Kernel | 10 pages, 4 figures, IEEE International Conference on Data Mining
(ICDM) 2015 | null | 10.1109/ICDM.2015.65 | null | cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The recent development of multi-agent simulations brings about a need for
population synthesis. It is a task of reconstructing the entire population from
a sampling survey of limited size (1% or so), supplying the initial conditions
from which simulations begin. This paper presents a new kernel density
estimator for this task. Our method is an analogue of the classical
Breiman-Meisel-Purcell estimator, but employs novel techniques that harness the
huge degree of freedom which is required to model high-dimensional nonlinearly
correlated datasets: the crossover kernel, the k-nearest neighbor restriction
of the kernel construction set and the bagging of kernels. The performance as a
statistical estimator is examined through real and synthetic datasets. We
provide an "optimization-free" parameter selection rule for our method, a
theory of how our method works and a computational cost analysis. To
demonstrate the usefulness as a population synthesizer, our method is applied
to a household synthesis task for an urban micro-simulator.
| [
{
"version": "v1",
"created": "Wed, 26 Aug 2015 13:22:37 GMT"
}
] | 2016-11-15T00:00:00 | [
[
"Hamada",
"Naoki",
""
],
[
"Homma",
"Katsumi",
""
],
[
"Higuchi",
"Hiroyuki",
""
],
[
"Kikuchi",
"Hideyuki",
""
]
] | TITLE: Population Synthesis via k-Nearest Neighbor Crossover Kernel
ABSTRACT: The recent development of multi-agent simulations brings about a need for
population synthesis. It is a task of reconstructing the entire population from
a sampling survey of limited size (1% or so), supplying the initial conditions
from which simulations begin. This paper presents a new kernel density
estimator for this task. Our method is an analogue of the classical
Breiman-Meisel-Purcell estimator, but employs novel techniques that harness the
huge degree of freedom which is required to model high-dimensional nonlinearly
correlated datasets: the crossover kernel, the k-nearest neighbor restriction
of the kernel construction set and the bagging of kernels. The performance as a
statistical estimator is examined through real and synthetic datasets. We
provide an "optimization-free" parameter selection rule for our method, a
theory of how our method works and a computational cost analysis. To
demonstrate the usefulness as a population synthesizer, our method is applied
to a household synthesis task for an urban micro-simulator.
| no_new_dataset | 0.951369 |
1510.02071 | Vinay Bettadapura | Vinay Bettadapura, Grant Schindler, Thomaz Plotz, Irfan Essa | Augmenting Bag-of-Words: Data-Driven Discovery of Temporal and
Structural Information for Activity Recognition | 8 pages | Proceedings of the 2013 IEEE Conference on Computer Vision and
Pattern Recognition (CVPR 2013) -- Pages 2619 - 2626 | 10.1109/CVPR.2013.338 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present data-driven techniques to augment Bag of Words (BoW) models, which
allow for more robust modeling and recognition of complex long-term activities,
especially when the structure and topology of the activities are not known a
priori. Our approach specifically addresses the limitations of standard BoW
approaches, which fail to represent the underlying temporal and causal
information that is inherent in activity streams. In addition, we also propose
the use of randomly sampled regular expressions to discover and encode patterns
in activities. We demonstrate the effectiveness of our approach in experimental
evaluations where we successfully recognize activities and detect anomalies in
four complex datasets.
| [
{
"version": "v1",
"created": "Wed, 7 Oct 2015 19:37:11 GMT"
}
] | 2016-11-15T00:00:00 | [
[
"Bettadapura",
"Vinay",
""
],
[
"Schindler",
"Grant",
""
],
[
"Plotz",
"Thomaz",
""
],
[
"Essa",
"Irfan",
""
]
] | TITLE: Augmenting Bag-of-Words: Data-Driven Discovery of Temporal and
Structural Information for Activity Recognition
ABSTRACT: We present data-driven techniques to augment Bag of Words (BoW) models, which
allow for more robust modeling and recognition of complex long-term activities,
especially when the structure and topology of the activities are not known a
priori. Our approach specifically addresses the limitations of standard BoW
approaches, which fail to represent the underlying temporal and causal
information that is inherent in activity streams. In addition, we also propose
the use of randomly sampled regular expressions to discover and encode patterns
in activities. We demonstrate the effectiveness of our approach in experimental
evaluations where we successfully recognize activities and detect anomalies in
four complex datasets.
| no_new_dataset | 0.948394 |
1511.06855 | Jianyu Wang | Jianyu Wang, Zhishuai Zhang, Cihang Xie, Vittal Premachandran, Alan
Yuille | Unsupervised learning of object semantic parts from internal states of
CNNs by population encoding | null | null | null | null | cs.LG cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We address the key question of how object part representations can be found
from the internal states of CNNs that are trained for high-level tasks, such as
object classification. This work provides a new unsupervised method to learn
semantic parts and gives new understanding of the internal representations of
CNNs. Our technique is based on the hypothesis that semantic parts are
represented by populations of neurons rather than by single filters. We propose
a clustering technique to extract part representations, which we call Visual
Concepts. We show that visual concepts are semantically coherent in that they
represent semantic parts, and visually coherent in that corresponding image
patches appear very similar. Also, visual concepts provide full spatial
coverage of the parts of an object, rather than a few sparse parts as is
typically found in keypoint annotations. Furthermore, We treat single visual
concept as part detector and evaluate it for keypoint detection using the
PASCAL3D+ dataset and for part detection using our newly annotated ImageNetPart
dataset. The experiments demonstrate that visual concepts can be used to detect
parts. We also show that some visual concepts respond to several semantic
parts, provided these parts are visually similar. Thus visual concepts have the
essential properties: semantic meaning and detection capability. Note that our
ImageNetPart dataset gives rich part annotations which cover the whole object,
making it useful for other part-related applications.
| [
{
"version": "v1",
"created": "Sat, 21 Nov 2015 09:02:21 GMT"
},
{
"version": "v2",
"created": "Thu, 7 Jan 2016 22:10:52 GMT"
},
{
"version": "v3",
"created": "Sat, 12 Nov 2016 13:37:07 GMT"
}
] | 2016-11-15T00:00:00 | [
[
"Wang",
"Jianyu",
""
],
[
"Zhang",
"Zhishuai",
""
],
[
"Xie",
"Cihang",
""
],
[
"Premachandran",
"Vittal",
""
],
[
"Yuille",
"Alan",
""
]
] | TITLE: Unsupervised learning of object semantic parts from internal states of
CNNs by population encoding
ABSTRACT: We address the key question of how object part representations can be found
from the internal states of CNNs that are trained for high-level tasks, such as
object classification. This work provides a new unsupervised method to learn
semantic parts and gives new understanding of the internal representations of
CNNs. Our technique is based on the hypothesis that semantic parts are
represented by populations of neurons rather than by single filters. We propose
a clustering technique to extract part representations, which we call Visual
Concepts. We show that visual concepts are semantically coherent in that they
represent semantic parts, and visually coherent in that corresponding image
patches appear very similar. Also, visual concepts provide full spatial
coverage of the parts of an object, rather than a few sparse parts as is
typically found in keypoint annotations. Furthermore, We treat single visual
concept as part detector and evaluate it for keypoint detection using the
PASCAL3D+ dataset and for part detection using our newly annotated ImageNetPart
dataset. The experiments demonstrate that visual concepts can be used to detect
parts. We also show that some visual concepts respond to several semantic
parts, provided these parts are visually similar. Thus visual concepts have the
essential properties: semantic meaning and detection capability. Note that our
ImageNetPart dataset gives rich part annotations which cover the whole object,
making it useful for other part-related applications.
| new_dataset | 0.968797 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.