66,035 research outputs found
Validation of hardware events for successful performance pattern identification in High Performance Computing
Hardware performance monitoring (HPM) is a crucial ingredient of performance
analysis tools. While there are interfaces like LIKWID, PAPI or the kernel
interface perf\_event which provide HPM access with some additional features,
many higher level tools combine event counts with results retrieved from other
sources like function call traces to derive (semi-)automatic performance
advice. However, although HPM is available for x86 systems since the early 90s,
only a small subset of the HPM features is used in practice. Performance
patterns provide a more comprehensive approach, enabling the identification of
various performance-limiting effects. Patterns address issues like bandwidth
saturation, load imbalance, non-local data access in ccNUMA systems, or false
sharing of cache lines. This work defines HPM event sets that are best suited
to identify a selection of performance patterns on the Intel Haswell processor.
We validate the chosen event sets for accuracy in order to arrive at a reliable
pattern detection mechanism and point out shortcomings that cannot be easily
circumvented due to bugs or limitations in the hardware
Magnetic and radar sensing for multimodal remote health monitoring
With the increased life expectancy and rise in health conditions related to aging, there is a need for new technologies that can routinely monitor vulnerable people, identify their daily pattern of activities and any anomaly or critical events such as falls. This paper aims to evaluate magnetic and radar sensors as suitable technologies for remote health monitoring purpose, both individually and fusing their information. After experiments and collecting data from 20 volunteers, numerical features has been extracted in both time and frequency domains. In order to analyse and verify the validation of fusion method for different classifiers, a Support Vector Machine with a quadratic kernel, and an Artificial Neural Network with one and multiple hidden layers have been implemented. Furthermore, for both classifiers, feature selection has been performed to obtain salient features. Using this technique along with fusion, both classifiers can detect 10 different activities with an accuracy rate of approximately 96%. In cases where the user is unknown to the classifier, an accuracy of approximately 92% is maintained
Spontaneous Subtle Expression Detection and Recognition based on Facial Strain
Optical strain is an extension of optical flow that is capable of quantifying
subtle changes on faces and representing the minute facial motion intensities
at the pixel level. This is computationally essential for the relatively new
field of spontaneous micro-expression, where subtle expressions can be
technically challenging to pinpoint. In this paper, we present a novel method
for detecting and recognizing micro-expressions by utilizing facial optical
strain magnitudes to construct optical strain features and optical strain
weighted features. The two sets of features are then concatenated to form the
resultant feature histogram. Experiments were performed on the CASME II and
SMIC databases. We demonstrate on both databases, the usefulness of optical
strain information and more importantly, that our best approaches are able to
outperform the original baseline results for both detection and recognition
tasks. A comparison of the proposed method with other existing spatio-temporal
feature extraction approaches is also presented.Comment: 21 pages (including references), single column format, accepted to
Signal Processing: Image Communication journa
Automatic Detection of Malware-Generated Domains with Recurrent Neural Models
Modern malware families often rely on domain-generation algorithms (DGAs) to
determine rendezvous points to their command-and-control server. Traditional
defence strategies (such as blacklisting domains or IP addresses) are
inadequate against such techniques due to the large and continuously changing
list of domains produced by these algorithms. This paper demonstrates that a
machine learning approach based on recurrent neural networks is able to detect
domain names generated by DGAs with high precision. The neural models are
estimated on a large training set of domains generated by various malwares.
Experimental results show that this data-driven approach can detect
malware-generated domain names with a F_1 score of 0.971. To put it
differently, the model can automatically detect 93 % of malware-generated
domain names for a false positive rate of 1:100.Comment: Submitted to NISK 201
Microbial community pattern detection in human body habitats via ensemble clustering framework
The human habitat is a host where microbial species evolve, function, and
continue to evolve. Elucidating how microbial communities respond to human
habitats is a fundamental and critical task, as establishing baselines of human
microbiome is essential in understanding its role in human disease and health.
However, current studies usually overlook a complex and interconnected
landscape of human microbiome and limit the ability in particular body habitats
with learning models of specific criterion. Therefore, these methods could not
capture the real-world underlying microbial patterns effectively. To obtain a
comprehensive view, we propose a novel ensemble clustering framework to mine
the structure of microbial community pattern on large-scale metagenomic data.
Particularly, we first build a microbial similarity network via integrating
1920 metagenomic samples from three body habitats of healthy adults. Then a
novel symmetric Nonnegative Matrix Factorization (NMF) based ensemble model is
proposed and applied onto the network to detect clustering pattern. Extensive
experiments are conducted to evaluate the effectiveness of our model on
deriving microbial community with respect to body habitat and host gender. From
clustering results, we observed that body habitat exhibits a strong bound but
non-unique microbial structural patterns. Meanwhile, human microbiome reveals
different degree of structural variations over body habitat and host gender. In
summary, our ensemble clustering framework could efficiently explore integrated
clustering results to accurately identify microbial communities, and provide a
comprehensive view for a set of microbial communities. Such trends depict an
integrated biography of microbial communities, which offer a new insight
towards uncovering pathogenic model of human microbiome.Comment: BMC Systems Biology 201
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