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Process-level races are endemic in modern systems. These races are difficult to debug because they are sensitive to execution events such as interrupts and scheduling. Unless a process interleaving that can result in the race can be found, it cannot be reproduced and cannot be corrected. In practice, however, the number of interleavings that can occur among processes in practice is large, and the...
Deep Convolutional Neural Network (CNN) is one of the most popular methods for image processing and recognition. There are many research works to improve the performance of CNNs. However, as an important part of CNNs, convolution kernel has rarely been discussed. As one Original Convolution Kernel (OCK) can only detect one type of visual feature with a fixed deformation, the networks using OCKs may...
Currently, studies on learning relationship between objects focus on the text domain. There are a few researchers who focus on relationship learning between objects in other domains. In these researches, they have tried to represent the qualitative description of structure of objects, and the symbolic relationship between them. This output provides symbolic meaning to the inter-object relationships...
Spectral clustering is a suitable technique to deal with problems involving unlabeled clusters and having a complex structure, being kernel-based approaches the most recommended ones. This work aims at demonstrating the relationship between a widely-recommended method, so-named kernel spectral clustering (KSC) and other well-known approaches, namely normalized cut clustering and kernel k-means. Such...
In the case of building large convolutional neural networks, signal propagation speed is one of priority factors. Training large neural structures requires enormous time for achieving satisfying accuracy. In addition, the networks need to be learn by very large sets of good quality training images, which is another time-consuming factor. The paper presents a fast computing framework with some methods...
Water is classified into four status of water quality, which good condition, lightly polluted, medium polluted and heavyly polluted. The classification status of water quality is very important to know the proper use and handling. Accuracy in classification of the quality status is very important, so that both of the classification algorithm K-Nearest Neighbor (KNN) and Support Vector Machine (SVM)...
Text-independent writer identification is challenging due to the huge variation of written contents and the ambiguous written styles of different writers. This paper proposes DeepWriter, a deep multi-stream CNN to learn deep powerful representation for recognizing writers. DeepWriter takes local handwritten patches as input and is trained with softmax classification loss. The main contributions are:...
With time and space partitioned architectures becoming increasingly appealing to the European space sector, the dependability of separation kernel technology is a key factor to its applicability in European Space Agency projects. This paper explores the potential of the data type fault model, which injects faults through the Application Program Interface, in separation kernel robustness testing. This...
Human iris can be used for detecting organ disorders based on iridology science. Nowadays, iridology diagnosis can be done automatically by computer using artificial intelligence approach. This research focused on cardiac diagnosis based on left iris map on clockwise direction around 2:00 to 3:00. The Principal Component Analysis (PCA) is used for feature extraction while the Support Vector Machine...
We propose a novel approach for pixel classification in hyperspectral images, leveraging on both the spatial and spectral information in the data. The introduced method relies on a recently proposed framework for learning on distributions — by representing them with mean elements in reproducing kernel Hilbert spaces (RKHS) and formulating a classification algorithm therein. In particular, we associate...
Domain adaptation (DA) algorithms address the problem of distribution shift between training and testing data. Recent approaches transform data into a shared subspace by minimizing the shift between their marginal distributions. We propose a method to learn a common subspace that will leverage the class conditional distributions of training samples along with reducing the marginal distribution shift...
The approximation of nonlinear kernels via linear feature maps has recently gained interest due to their applications in reducing the training and testing time of kernel-based learning algorithms. Current random projection methods avoid the curse of dimensionality by embedding the nonlinear feature space into a low dimensional Euclidean space to create nonlinear kernels. We introduce a Layered Random...
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Kernel adaptive filters (KAFs) are powerful tools for online nonlinear system modeling, which are direct extensions of traditional linear adaptive filters in kernel space, with growing linear-in-the-parameters (LIP) structure. However, like most other nonlinear adaptive filters, the KAFs are “black box” models where no prior information about the unknown nonlinear system is utilized. If some prior...
Reinforcement learning is an effective algorithm for brain machine interfaces (BMIs) which interprets the mapping between neural activities with plasticity and the kinematics. Exploring large state-action space is difficulty when the complicated BMIs needs to assign credits over both time and space. For BMIs attention gated reinforcement learning (AGREL) has been developed to classify multi-actions...
Clustering algorithm is often used to analyze the communication data for network intrusion detection system. However, network communication data are mixed, e.g., numerical and categorical data. So, at first, this paper put forward a method for representing the cluster center (prototype) of mixed-type data. Then respectively in combination with the continuity characteristic of the numerical attributes...
Hyperspectral remote-sensing image has high data dimensionality and a small amount of labeled pixels, which causes the curse of dimensionality phenomenon. Therefore, feature extraction is needed ahead of recognition for reducing dimensionality and improving classification accuracy. A novel multiclass feature extraction method, i.e., M-ary discriminant analysis (M-ary DA), is presented for solving...
Movement classification from electromyography (EMG) signals is a promising vector for improvement of human computer interaction and prosthetic control. Conventional work in this area typically makes use of expert knowledge to select a set of movements a priori and then design classifiers based around these movements. The disadvantage of this approach is that different individuals might have different...
This communication adapts the formalism of hierarchical (${\mathcal {H}}$ -) matrices to the direct solution process (LU decomposition) of the method of moments (MoM) in the frequency domain. A novel clustering approach based on physical constraints is presented and extended to treat dielectric bodies as well as arbitrary boundary conditions. Comparisons with other numerical methods are provided...
Fault diagnosis is an important procedure to ensure the equipment efficiency and stability. The diagnosis process is actually a pattern recognition process, and usually, the fault samples are lack of tags of fault types. In this case, the non-supervised learning method is more available, and kernel clustering is one of the most effective methods. In this paper, a novel electromagnetic particle swarm...
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