58,850 research outputs found

    Sequence Generation via Subsequence Similarity: Theory and Application to UAV Identification

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    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

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    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

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    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

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    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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