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The Kernel Mean Matching (KMM) is an elegant algorithm that produces density ratios between training and test data by minimizing their maximum mean discrepancy in a kernel space. The applicability of KMM to large-scale problems is however hindered by the quadratic complexity of calculating and storing the kernel matrices over training and test data. To address this problem, this paper proposes a novel...
Volterra series is a powerful tool for black-box macro-modeling of nonlinear devices. However, the exponential complexity growth in storing and evaluating higher order Volterra kernels has limited so far its employment on complex practical applications. On the other hand, tensors are a higher order generalization of matrices that can naturally and efficiently capture multi-dimensional data. Significant...
Graph-based methods are known to be successful for pattern description and comparison. Their general principle consists in using graphs to model local features as well as their structural relationships and achieving pattern comparison with graph matching. Among these methods, subgraph isomorphism is particularly effective but intractable for general and unconstrained graph structures. In this paper,...
Construction of polar code via puncturing and shortening is studied. We propose a reduced full search for finding optimal puncturing pattern of short polar codes.
While high-end heterogeneous systems are increasingly supporting heterogeneous uniform memory access (hUMA) as envisioned by the Heterogeneous System Architecture (HSA) foundation, their low-power counterparts targeting the embedded domain still lack basic features like virtual memory support for accelerators. As opposed to simply passing virtual address pointers, explicit data management involving...
In network coding theory, when wiretapping attacks occur, secure network coding is introduced to prevent information from being leaked to adversaries. In practical network communications, secure constraints vary with time. How to effectively deal with information transmission and information security simultaneously under different security-levels is introduced in this paper as variable-security-level...
This work presents a study of the properties of a non-linear vector quantization (VQ) method based on Kernel Principal Component Analysis (KPCA), focused on the complexity and viability of implementing this method in image processing. The theory supporting this method is described and then the method is compared to traditional quantization methods, as scalar quantization and entropy-constrained vector...
The Endeavor dimension of the Essence Kernel comprehends the essential elements of Software Engineering directly related to human factors: Work, Way of Working, and Team. This paper is about the Endeavor Kernel dimension and presents an approach to extend the Essence Kernel in order to deal with ethical issues in the software system development processes.
The computation of the reliability inferences among the variables of a single-parity-check (SPC) code is a common challenge to the implementation of channel decoders. Applicable to a variety of computational mechanisms using disparate kernels, a parallel-routing network has been developed, which is, compared to the state-of-art structure, exploring the best of its parallel nature for an improved timing...
To deal with inference and reasoning problems, Gaussian process has been considered as a promising tool due to the robustness and flexibility features. Especially, solving the regression and classification, Gaussian process coupling with Bayesian learning is one of the most appropriate supervised learning approaches in terms of accuracy and tractability. Unfortunately, this combination tolerates high...
Support Vector Machines (SVM) is a supervised Machine Learning and Data Mining (MLDM) algorithm, which has become ubiquitous largely due to its high accuracy and obliviousness to dimensionality. The objective of SVM is to find an optimal boundary -- also known as hyperplane -- which separates the samples (examples in a dataset) of different classes by a maximum margin. Usually, very few samples contribute...
This paper presents an approach to build a data classifier based on a simple and inexpensive evaluation function aimed to reduce the computational costs when processing new incoming instances. The classifier agent employs concepts of Self-Organized Maps and Multiple Instance Learning. The motivation for this proposal was the need of a classifier for the processing of signals from partial discharges...
Epileptic seizures reflect runaway excitation that severely hinders normal brain functions. With EEG recordings reflecting real-time brain activity, it is essential to both predict seizures and improve the classification of seizures in EEG signs. Towards this aim, nonlinear tools are strongly recommended to select the seizure-sensitive features prior to classification. However, the choice of the feature...
Reconstruction problem for signals generated by discrete nonlinear dynamic system is considered via unified approach to recurrent kernel-based dynamic systems. In order to prevent the model complexity increasing under on-line identification, the reduced order model kernel method is proposed and proper recurrent Least-Square identification algorithms are designed along with conventional regularization...
To solve the problem that global encoding kernels for the edges have great changes for variable-rate linear broadcast network coding, the concept of universal global encoding kernel is put forward in this paper. And the construction algorithm of such universal global encoding kernel is proposed based on the algorithm of variable-rate linear broadcast network coding. In this algorithm, the same local...
The profusion of spectral bands generated by the acquisition process of hyperspectral images generally leads to high computational costs. Such difficulties arise in particular with nonlinear unmixing methods, which are naturally more complex than linear ones. This complexity, associated with the high redundancy of information within the complete set of bands, make the search of band selection algorithms...
Constructing an events and causal factors chart can assist investigators in conducting an in-depth investigation and identifying the root causes of incidents. We regard kernel traces as one of the potential evidence sources for forensic readiness, and propose a systematic approach to construct an events and causal factors chart from kernel traces by employing layers of abstraction. Through employing...
This paper presents a comparison of different in- stances of advanced iterative receivers for the non linear satellite channel. A comparison of the performance and complexity of each of the selected receivers is drawn. It is shown that the frequency domain implementation of the linear equalizer achieves good performance complexity trade-off. The cost to pay for the frequency domain processing is the...
Execution traces are frequently used to study system run-time behavior and to detect problems. However, the huge amount of data in an execution trace may complexify its analysis. Moreover, users are not usually interested in all events of a trace, hence the need for a proper filtering approach. Filtering is used to generate an enhanced trace, with a reduced size and complexity, that is easier to analyse...
The ability to automatically detect faults or fault patterns to enhance system reliability is important for system administrators in reducing system failures. To achieve this objective, the message logs from cluster system are augmented with failure information, i.e., The raw log data is labelled. However, tagging or labelling of raw log data is very costly. In this paper, our objective is to detect...
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