58,850 research outputs found
Sequence Generation via Subsequence Similarity: Theory and Application to UAV Identification
The ability to generate synthetic sequences is crucial for a wide range of
applications, and recent advances in deep learning architectures and generative
frameworks have greatly facilitated this process. Particularly, unconditional
one-shot generative models constitute an attractive line of research that
focuses on capturing the internal information of a single image, video, etc. to
generate samples with similar contents. Since many of those one-shot models are
shifting toward efficient non-deep and non-adversarial approaches, we examine
the versatility of a one-shot generative model for augmenting whole datasets.
In this work, we focus on how similarity at the subsequence level affects
similarity at the sequence level, and derive bounds on the optimal transport of
real and generated sequences based on that of corresponding subsequences. We
use a one-shot generative model to sample from the vicinity of individual
sequences and generate subsequence-similar ones and demonstrate the improvement
of this approach by applying it to the problem of Unmanned Aerial Vehicle (UAV)
identification using limited radio-frequency (RF) signals. In the context of
UAV identification, RF fingerprinting is an effective method for distinguishing
legitimate devices from malicious ones, but heterogenous environments and
channel impairments can impose data scarcity and affect the performance of
classification models. By using subsequence similarity to augment sequences of
RF data with a low ratio (5\%-20\%) of training dataset, we achieve significant
improvements in performance metrics such as accuracy, precision, recall, and F1
score.Comment: 12 pages, 5 figures, 2 table
Machine learning methods for multimedia information retrieval
In this thesis we examined several multimodal feature extraction and learning
methods for retrieval and classification purposes. We reread briefly some
theoretical results of learning in Section 2 and reviewed several generative
and discriminative models in Section 3 while we described the similarity kernel
in Section 4. We examined different aspects of the multimodal image retrieval
and classification in Section 5 and suggested methods for identifying quality
assessments of Web documents in Section 6. In our last problem we proposed
similarity kernel for time-series based classification. The experiments were
carried over publicly available datasets and source codes for the most
essential parts are either open source or released. Since the used similarity
graphs (Section 4.2) are greatly constrained for computational purposes, we
would like to continue work with more complex, evolving and capable graphs and
apply for different problems such as capturing the rapid change in the
distribution (e.g. session based recommendation) or complex graphs of the
literature work. The similarity kernel with the proper metrics reaches and in
many cases improves over the state-of-the-art. Hence we may conclude generative
models based on instance similarities with multiple modes is a generally
applicable model for classification and regression tasks ranging over various
domains, including but not limited to the ones presented in this thesis. More
generally, the Fisher kernel is not only unique in many ways but one of the
most powerful kernel functions. Therefore we may exploit the Fisher kernel in
the future over widely used generative models, such as Boltzmann Machines
[Hinton et al., 1984], a particular subset, the Restricted Boltzmann Machines
and Deep Belief Networks [Hinton et al., 2006]), Latent Dirichlet Allocation
[Blei et al., 2003] or Hidden Markov Models [Baum and Petrie, 1966] to name a
few.Comment: doctoral thesis, 201
Generative Sensing: Transforming Unreliable Sensor Data for Reliable Recognition
This paper introduces a deep learning enabled generative sensing framework
which integrates low-end sensors with computational intelligence to attain a
high recognition accuracy on par with that attained with high-end sensors. The
proposed generative sensing framework aims at transforming low-end, low-quality
sensor data into higher quality sensor data in terms of achieved classification
accuracy. The low-end data can be transformed into higher quality data of the
same modality or into data of another modality. Different from existing methods
for image generation, the proposed framework is based on discriminative models
and targets to maximize the recognition accuracy rather than a similarity
measure. This is achieved through the introduction of selective feature
regeneration in a deep neural network (DNN). The proposed generative sensing
will essentially transform low-quality sensor data into high-quality
information for robust perception. Results are presented to illustrate the
performance of the proposed framework.Comment: 5 pages, Submitted to IEEE MIPR 201
Feature Extraction from Degree Distribution for Comparison and Analysis of Complex Networks
The degree distribution is an important characteristic of complex networks.
In many data analysis applications, the networks should be represented as
fixed-length feature vectors and therefore the feature extraction from the
degree distribution is a necessary step. Moreover, many applications need a
similarity function for comparison of complex networks based on their degree
distributions. Such a similarity measure has many applications including
classification and clustering of network instances, evaluation of network
sampling methods, anomaly detection, and study of epidemic dynamics. The
existing methods are unable to effectively capture the similarity of degree
distributions, particularly when the corresponding networks have different
sizes. Based on our observations about the structure of the degree
distributions in networks over time, we propose a feature extraction and a
similarity function for the degree distributions in complex networks. We
propose to calculate the feature values based on the mean and standard
deviation of the node degrees in order to decrease the effect of the network
size on the extracted features. The proposed method is evaluated using
different artificial and real network datasets, and it outperforms the state of
the art methods with respect to the accuracy of the distance function and the
effectiveness of the extracted features.Comment: arXiv admin note: substantial text overlap with arXiv:1307.362
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