362 research outputs found
Localization Recall Precision (LRP): A New Performance Metric for Object Detection
Average precision (AP), the area under the recall-precision (RP) curve, is
the standard performance measure for object detection. Despite its wide
acceptance, it has a number of shortcomings, the most important of which are
(i) the inability to distinguish very different RP curves, and (ii) the lack of
directly measuring bounding box localization accuracy. In this paper, we
propose 'Localization Recall Precision (LRP) Error', a new metric which we
specifically designed for object detection. LRP Error is composed of three
components related to localization, false negative (FN) rate and false positive
(FP) rate. Based on LRP, we introduce the 'Optimal LRP', the minimum achievable
LRP error representing the best achievable configuration of the detector in
terms of recall-precision and the tightness of the boxes. In contrast to AP,
which considers precisions over the entire recall domain, Optimal LRP
determines the 'best' confidence score threshold for a class, which balances
the trade-off between localization and recall-precision. In our experiments, we
show that, for state-of-the-art object (SOTA) detectors, Optimal LRP provides
richer and more discriminative information than AP. We also demonstrate that
the best confidence score thresholds vary significantly among classes and
detectors. Moreover, we present LRP results of a simple online video object
detector which uses a SOTA still image object detector and show that the
class-specific optimized thresholds increase the accuracy against the common
approach of using a general threshold for all classes. At
https://github.com/cancam/LRP we provide the source code that can compute LRP
for the PASCAL VOC and MSCOCO datasets. Our source code can easily be adapted
to other datasets as well.Comment: to appear in ECCV 201
Exploring Interpretability for Predictive Process Analytics
Modern predictive analytics underpinned by machine learning techniques has
become a key enabler to the automation of data-driven decision making. In the
context of business process management, predictive analytics has been applied
to making predictions about the future state of an ongoing business process
instance, for example, when will the process instance complete and what will be
the outcome upon completion. Machine learning models can be trained on event
log data recording historical process execution to build the underlying
predictive models. Multiple techniques have been proposed so far which encode
the information available in an event log and construct input features required
to train a predictive model. While accuracy has been a dominant criterion in
the choice of various techniques, they are often applied as a black-box in
building predictive models. In this paper, we derive explanations using
interpretable machine learning techniques to compare and contrast the
suitability of multiple predictive models of high accuracy. The explanations
allow us to gain an understanding of the underlying reasons for a prediction
and highlight scenarios where accuracy alone may not be sufficient in assessing
the suitability of techniques used to encode event log data to features used by
a predictive model. Findings from this study motivate the need and importance
to incorporate interpretability in predictive process analytics.Comment: 15 pages, 7 figure
IoT Device Identification Using Deep Learning
The growing use of IoT devices in organizations has increased the number of
attack vectors available to attackers due to the less secure nature of the
devices. The widely adopted bring your own device (BYOD) policy which allows an
employee to bring any IoT device into the workplace and attach it to an
organization's network also increases the risk of attacks. In order to address
this threat, organizations often implement security policies in which only the
connection of white-listed IoT devices is permitted. To monitor adherence to
such policies and protect their networks, organizations must be able to
identify the IoT devices connected to their networks and, more specifically, to
identify connected IoT devices that are not on the white-list (unknown
devices). In this study, we applied deep learning on network traffic to
automatically identify IoT devices connected to the network. In contrast to
previous work, our approach does not require that complex feature engineering
be applied on the network traffic, since we represent the communication
behavior of IoT devices using small images built from the IoT devices network
traffic payloads. In our experiments, we trained a multiclass classifier on a
publicly available dataset, successfully identifying 10 different IoT devices
and the traffic of smartphones and computers, with over 99% accuracy. We also
trained multiclass classifiers to detect unauthorized IoT devices connected to
the network, achieving over 99% overall average detection accuracy
Localization recall precision (LRP): A new performance metric for object detection
Average precision (AP), the area under the recall-precision (RP) curve, is the standard performance measure for object detection. Despite its wide acceptance, it has a number of shortcomings, the most important of which are (i) the inability to distinguish very different RP curves, and (ii) the lack of directly measuring bounding box localization accuracy. In this paper, we propose “Localization Recall Precision (LRP) Error”, a new metric specifically designed for object detection. LRP Error is composed of three components related to localization, false negative (FN) rate and false positive (FP) rate. Based on LRP, we introduce the “Optimal LRP” (oLRP), the minimum achievable LRP error representing the best achievable configuration of the detector in terms of recall-precision and the tightness of the boxes. In contrast to AP, which considers precisions over the entire recall domain, oLRP determines the “best” confidence score threshold for a class, which balances the trade-off between localization and recall-precision. In our experiments, we show that oLRP provides richer and more discriminative information than AP. We also demonstrate that the best confidence score thresholds vary significantly among classes and detectors. Moreover, we present LRP results of a simple online video object detector and show that the class-specific optimized thresholds increase the accuracy against the common approach of using a general threshold for all classes. Our experiments demonstrate that LRP is more competent than AP in capturing the performance of detectors. Our source code for PASCAL VOC AND MSCOCO datasets are provided at https://github.com/cancam/LRP
A Taxonomy of Explainable Bayesian Networks
Artificial Intelligence (AI), and in particular, the explainability thereof,
has gained phenomenal attention over the last few years. Whilst we usually do
not question the decision-making process of these systems in situations where
only the outcome is of interest, we do however pay close attention when these
systems are applied in areas where the decisions directly influence the lives
of humans. It is especially noisy and uncertain observations close to the
decision boundary which results in predictions which cannot necessarily be
explained that may foster mistrust among end-users. This drew attention to AI
methods for which the outcomes can be explained. Bayesian networks are
probabilistic graphical models that can be used as a tool to manage
uncertainty. The probabilistic framework of a Bayesian network allows for
explainability in the model, reasoning and evidence. The use of these methods
is mostly ad hoc and not as well organised as explainability methods in the
wider AI research field. As such, we introduce a taxonomy of explainability in
Bayesian networks. We extend the existing categorisation of explainability in
the model, reasoning or evidence to include explanation of decisions. The
explanations obtained from the explainability methods are illustrated by means
of a simple medical diagnostic scenario. The taxonomy introduced in this paper
has the potential not only to encourage end-users to efficiently communicate
outcomes obtained, but also support their understanding of how and, more
importantly, why certain predictions were made
Performance of CMS muon reconstruction in pp collision events at sqrt(s) = 7 TeV
The performance of muon reconstruction, identification, and triggering in CMS
has been studied using 40 inverse picobarns of data collected in pp collisions
at sqrt(s) = 7 TeV at the LHC in 2010. A few benchmark sets of selection
criteria covering a wide range of physics analysis needs have been examined.
For all considered selections, the efficiency to reconstruct and identify a
muon with a transverse momentum pT larger than a few GeV is above 95% over the
whole region of pseudorapidity covered by the CMS muon system, abs(eta) < 2.4,
while the probability to misidentify a hadron as a muon is well below 1%. The
efficiency to trigger on single muons with pT above a few GeV is higher than
90% over the full eta range, and typically substantially better. The overall
momentum scale is measured to a precision of 0.2% with muons from Z decays. The
transverse momentum resolution varies from 1% to 6% depending on pseudorapidity
for muons with pT below 100 GeV and, using cosmic rays, it is shown to be
better than 10% in the central region up to pT = 1 TeV. Observed distributions
of all quantities are well reproduced by the Monte Carlo simulation.Comment: Replaced with published version. Added journal reference and DO
Performance of CMS muon reconstruction in pp collision events at sqrt(s) = 7 TeV
The performance of muon reconstruction, identification, and triggering in CMS
has been studied using 40 inverse picobarns of data collected in pp collisions
at sqrt(s) = 7 TeV at the LHC in 2010. A few benchmark sets of selection
criteria covering a wide range of physics analysis needs have been examined.
For all considered selections, the efficiency to reconstruct and identify a
muon with a transverse momentum pT larger than a few GeV is above 95% over the
whole region of pseudorapidity covered by the CMS muon system, abs(eta) < 2.4,
while the probability to misidentify a hadron as a muon is well below 1%. The
efficiency to trigger on single muons with pT above a few GeV is higher than
90% over the full eta range, and typically substantially better. The overall
momentum scale is measured to a precision of 0.2% with muons from Z decays. The
transverse momentum resolution varies from 1% to 6% depending on pseudorapidity
for muons with pT below 100 GeV and, using cosmic rays, it is shown to be
better than 10% in the central region up to pT = 1 TeV. Observed distributions
of all quantities are well reproduced by the Monte Carlo simulation.Comment: Replaced with published version. Added journal reference and DO
X-ray emission from the Sombrero galaxy: discrete sources
We present a study of discrete X-ray sources in and around the
bulge-dominated, massive Sa galaxy, Sombrero (M104), based on new and archival
Chandra observations with a total exposure of ~200 ks. With a detection limit
of L_X = 1E37 erg/s and a field of view covering a galactocentric radius of ~30
kpc (11.5 arcminute), 383 sources are detected. Cross-correlation with Spitler
et al.'s catalogue of Sombrero globular clusters (GCs) identified from HST/ACS
observations reveals 41 X-rays sources in GCs, presumably low-mass X-ray
binaries (LMXBs). We quantify the differential luminosity functions (LFs) for
both the detected GC and field LMXBs, whose power-low indices (~1.1 for the
GC-LF and ~1.6 for field-LF) are consistent with previous studies for
elliptical galaxies. With precise sky positions of the GCs without a detected
X-ray source, we further quantify, through a fluctuation analysis, the GC LF at
fainter luminosities down to 1E35 erg/s. The derived index rules out a
faint-end slope flatter than 1.1 at a 2 sigma significance, contrary to recent
findings in several elliptical galaxies and the bulge of M31. On the other
hand, the 2-6 keV unresolved emission places a tight constraint on the field
LF, implying a flattened index of ~1.0 below 1E37 erg/s. We also detect 101
sources in the halo of Sombrero. The presence of these sources cannot be
interpreted as galactic LMXBs whose spatial distribution empirically follows
the starlight. Their number is also higher than the expected number of cosmic
AGNs (52+/-11 [1 sigma]) whose surface density is constrained by deep X-ray
surveys. We suggest that either the cosmic X-ray background is unusually high
in the direction of Sombrero, or a distinct population of X-ray sources is
present in the halo of Sombrero.Comment: 11 figures, 5 tables, ApJ in pres
Azimuthal anisotropy of charged particles at high transverse momenta in PbPb collisions at sqrt(s[NN]) = 2.76 TeV
The azimuthal anisotropy of charged particles in PbPb collisions at
nucleon-nucleon center-of-mass energy of 2.76 TeV is measured with the CMS
detector at the LHC over an extended transverse momentum (pt) range up to
approximately 60 GeV. The data cover both the low-pt region associated with
hydrodynamic flow phenomena and the high-pt region where the anisotropies may
reflect the path-length dependence of parton energy loss in the created medium.
The anisotropy parameter (v2) of the particles is extracted by correlating
charged tracks with respect to the event-plane reconstructed by using the
energy deposited in forward-angle calorimeters. For the six bins of collision
centrality studied, spanning the range of 0-60% most-central events, the
observed v2 values are found to first increase with pt, reaching a maximum
around pt = 3 GeV, and then to gradually decrease to almost zero, with the
decline persisting up to at least pt = 40 GeV over the full centrality range
measured.Comment: Replaced with published version. Added journal reference and DO
Search for new physics with same-sign isolated dilepton events with jets and missing transverse energy
A search for new physics is performed in events with two same-sign isolated
leptons, hadronic jets, and missing transverse energy in the final state. The
analysis is based on a data sample corresponding to an integrated luminosity of
4.98 inverse femtobarns produced in pp collisions at a center-of-mass energy of
7 TeV collected by the CMS experiment at the LHC. This constitutes a factor of
140 increase in integrated luminosity over previously published results. The
observed yields agree with the standard model predictions and thus no evidence
for new physics is found. The observations are used to set upper limits on
possible new physics contributions and to constrain supersymmetric models. To
facilitate the interpretation of the data in a broader range of new physics
scenarios, information on the event selection, detector response, and
efficiencies is provided.Comment: Published in Physical Review Letter
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