690,438 research outputs found
Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification
Recently, substantial research effort has focused on how to apply CNNs or
RNNs to better extract temporal patterns from videos, so as to improve the
accuracy of video classification. In this paper, however, we show that temporal
information, especially longer-term patterns, may not be necessary to achieve
competitive results on common video classification datasets. We investigate the
potential of a purely attention based local feature integration. Accounting for
the characteristics of such features in video classification, we propose a
local feature integration framework based on attention clusters, and introduce
a shifting operation to capture more diverse signals. We carefully analyze and
compare the effect of different attention mechanisms, cluster sizes, and the
use of the shifting operation, and also investigate the combination of
attention clusters for multimodal integration. We demonstrate the effectiveness
of our framework on three real-world video classification datasets. Our model
achieves competitive results across all of these. In particular, on the
large-scale Kinetics dataset, our framework obtains an excellent single model
accuracy of 79.4% in terms of the top-1 and 94.0% in terms of the top-5
accuracy on the validation set. The attention clusters are the backbone of our
winner solution at ActivityNet Kinetics Challenge 2017. Code and models will be
released soon.Comment: The backbone of the winner solution at ActivityNet Kinetics Challenge
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Optimization of RBF-SVM hyperparameters using genetic algorithm for face recognit
Manual grid-search tuning of machine learning hyperparameters is very time-consuming. Hence, to curb this problem, we propose the use of a genetic algorithm (GA) for the selection of optimal radial-basis-function based support vector machine (RBF-SVM) hyperparameters; regularization parameter C and cost-factor γ. The resulting optimal parameters were used during the training of face recognition models. To train the models, we independently extracted features from the ORL face image dataset using local binary patterns (handcrafted) and deep learning architectures (pretrained variants of VGGNet). The resulting features were passed as input to either linear-SVM or optimized RBF-SVM. The results show that the models from optimized RBFSVM combined with deep learning or hand-crafted features yielded performances that surpass models obtained from Linear-SVM combined with the aforementioned features in most of the data splits. The study demonstrated that it is profitable to optimize the hyperparameters of an SVM to obtain the best classification performance.
Keywords: Face Recognition, Feature Extraction, Local Binary Patterns, Transfer Learning, Genetic Algorithm and Support Vector Machines
A new approach for discovering business process models from event logs.
Process mining is the automated acquisition of process models from the event logs of information systems. Although process mining has many useful applications, not all inherent difficulties have been sufficiently solved. A first difficulty is that process mining is often limited to a setting of non-supervised learnings since negative information is often not available. Moreover, state transitions in processes are often dependent on the traversed path, which limits the appropriateness of search techniques based on local information in the event log. Another difficulty is that case data and resource properties that can also influence state transitions are time-varying properties, such that they cannot be considered ascross-sectional.This article investigates the use of first-order, ILP classification learners for process mining and describes techniques for dealing with each of the above mentioned difficulties. To make process mining a supervised learning task, we propose to include negative events in the event log. When event logs contain no negative information, a technique is described to add artificial negative examples to a process log. To capture history-dependent behavior the article proposes to take advantage of the multi-relational nature of ILP classification learners. Multi-relational process mining allows to search for patterns among multiple event rows in the event log, effectively basing its search on global information. To deal with time-varying case data and resource properties, a closed-world version of the Event Calculus has to be added as background knowledge, transforming the event log effectively in a temporal database. First experiments on synthetic event logs show that first-order classification learners are capable of predicting the behavior with high accuracy, even under conditions of noise.Credit; Credit scoring; Models; Model; Applications; Performance; Space; Decision; Yield; Real life; Risk; Evaluation; Rules; Neural networks; Networks; Classification; Research; Business; Processes; Event; Information; Information systems; Systems; Learning; Data; Behavior; Patterns; IT; Event calculus; Knowledge; Database; Noise;
Gender differences in the temporal voice areas
There is not only evidence for behavioral differences in voice perception between female and male listeners, but also recent suggestions for differences in neural correlates between genders. The fMRI functional voice localizer (comprising a univariate analysis contrasting stimulation with vocal versus non-vocal sounds) is known to give robust estimates of the temporal voice areas (TVAs). However there is growing interest in employing multivariate analysis approaches to fMRI data (e.g. multivariate pattern analysis; MVPA). The aim of the current study was to localize voice-related areas in both female and male listeners and to investigate whether brain maps may differ depending on the gender of the listener. After a univariate analysis, a random effects analysis was performed on female (n = 149) and male (n = 123) listeners and contrasts between them were computed. In addition, MVPA with a whole-brain searchlight approach was implemented and classification maps were entered into a second-level permutation based random effects models using statistical non-parametric mapping (SnPM; Nichols & Holmes 2002). Gender differences were found only in the MVPA. Identified regions were located in the middle part of the middle temporal gyrus (bilateral) and the middle superior temporal gyrus (right hemisphere). Our results suggest differences in classifier performance between genders in response to the voice localizer with higher classification accuracy from local BOLD signal patterns in several temporal-lobe regions in female listeners
Event Recognition Using Signal Spectrograms in Long Pulse Experiments
As discharge duration increases, real-time complex analysis of the signal becomes more important. In this context, data acquisition and processing systems must provide models for designing experiments which use event oriented plasma control. One example of advanced data analysis is signal classification. The off-line statistical analysis of a large number of discharges provides information to develop algorithms for the determination of the plasma parameters from measurements of magnetohydrodinamic waves, for example, to detect density fluctuations induced by the Alfvén cascades using morphological patterns. The need to apply different algorithms to the signals and to address different processing algorithms using the previous results necessitates the use of an event-based experiment. The Intelligent Test and Measurement System platform is an example of architecture designed to implement distributed data acquisition and real-time processing systems. The processing algorithm sequence is modeled using an event-based paradigm. The adaptive capacity of this model is based on the logic defined by the use of state machines in SCXML. The Intelligent Test and Measurement System platform mixes a local multiprocessing model with a distributed deployment of services based on Jini
Detecting spatial patterns of land cover and methane fluxes with remote sensing in Pallastunturi, Finland
Northern peatlands are a large source of methane (CH4) to the atmosphere and can vary strongly
depending on local environmental conditions. However, few studies have mapped fine-grained CH4
fluxes at the landscape-level. The aim of this study was to predict land cover and CH4 flux patterns
in Pallastunturi, Finland, in a study area dominated by forests, peatlands, fells, and lakes. I used
random forest models to map land cover types and CH4 fluxes with multi-source remote sensing
data and upscaled CH4 fluxes based on land cover maps. The random forest classifier reliably
detected the same land cover patterns as the CORINE Land Cover maps. The main differences
between the land cover maps were forest type classification, misclassification between neighboring
peatland types, and detection of sparsely vegetated areas on fells. The upscaled CH4 fluxes of sinks
were very robust to changes in land cover classification, but shrub tundra and peatland CH4 fluxes
were sensitive to the level of detail in the land cover classification. The random forest regression
performed well (NRMSE 6.6%, R2 82%) and predicted similar CH4 flux patterns as the upscaled
CH4 flux maps, despite predicting larger areas that act as CH4 sources than the upscaled CH4 flux
maps. The random forest regressor also better predicted CH4 fluxes in peatlands due to added
information about soil moisture content from the remote sensing data. Random forests are a good
model choice to detect landscape patterns and predict CH4 patterns in northern peatlands based
on remote sensing and topographic data
Classification Tree Models for Predicting Distributions of Michigan Stream Fish from Landscape Variables
Traditionally, fish habitat requirements have been described from local‐scale environmental variables. However, recent studies have shown that studying landscape‐scale processes improves our understanding of what drives species assemblages and distribution patterns across the landscape. Our goal was to learn more about constraints on the distribution of Michigan stream fish by examining landscape‐scale habitat variables. We used classification trees and landscape‐scale habitat variables to create and validate presence‐absence models and relative abundance models for Michigan stream fishes. We developed 93 presence‐absence models that on average were 72% correct in making predictions for an independent data set, and we developed 46 relative abundance models that were 76% correct in making predictions for independent data. The models were used to create statewide predictive distribution and abundance maps that have the potential to be used for a variety of conservation and scientific purposes.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141481/1/tafs0976.pd
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