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Traditional network anomaly detection involves developing models that rely on packet inspection. Increasing network speeds and use of encrypted protocols make per-packet inspection unsuited for today's networks. One method of overcoming this obstacle is flow based analysis. Many existing approaches are special purpose, i.e., limited to detecting specific behavior. Also, the data reduction inherent...
Common conceptualizations of trust concern expectations about the future behavior of an entity. We focus on expectations about fair future behavior: this aspect was insufficiently formalized to date, prohibiting formal reasoning about fairness. In this paper, we extend our previous binary fairness model to a gradual one. We demonstrate the capabilities of gradual fairness in two scenarios: "distributing...
Day to day variability and non-stationarity caused by changes in subject motivation, learning and behavior pose a challenge in using local field potentials (LFP) for practical Brain Computer Interfaces. Pattern recognition algorithms require that the features possess little to no variation from the training to test data. As such models developed on one day fail to represent the characteristics on...
The maximum likelihood linear regression (MLLR) technique is a well-known approach to parameter adaptation in hidden Markov model (HMM)-based systems. In this paper, we propose the maximum penalized likelihood kernel regression (MPLKR) approach as a novel adaptation technique for HMM-based speech synthesis. The proposed algorithm performs a nonlinear regression between the mean vector of the base...
In this paper, a new control scheme named model-free adaptive control with contractive constraints (MFAC-CC) is proposed based on compact form dynamic linearization (CFDL) for a class of nonlinear systems. In this strategy, contractive constraints are added into the model-free adaptive control (MFAC) algorithm, which lead to the tracking error decaying gradually. A numerical comparison experiment...
Domain adaptation addresses the problem of accuracy drop that a classifier may suffer when the training data (source domain) and the testing data (target domain) are drawn from different distributions. In this work, we focus on domain adaptation for structured SVM (SSVM). We propose a cost-sensitive domain adaptation method for SSVM, namely COSS-SSVM. In particular, during the re-training of an adapted...
We propose a novel multiple model fitting method based on outlier insensitive evolutionary dynamics, fulfilling several important requirements. Our method automatically identifies a unspecified number of models and is robust to noise and outliers in the data. Furthermore, we are able to handle overlapping models, by allowing that data points are assigned to more than one model. This is implicitly...
In this paper, we describe a framework for surface registration. The framework consists of a combination of rigid registration, elasticity modulated registration and the use of a shape model prior. The main goal in this paper is to minimize the geometric surface registration error while maintaining correspondences. Experiments show improved geometric fit, correspondence, and timing compared to the...
This paper proposes the use of the type-2 fuzzy GMM (T2FGMM) framework in order to improve the verification rates of the standard GMM-UBM text-independent speaker verification system in noisy environments. Based on type-2 fuzzy sets, the T2FGMM framework describes GMMs with uncertain parameters and provides likelihood intervals for them. The proposed method (T2F-GMM-UBM) estimates the parameter intervals...
We present a benchmark of several existing multi-source adaptive methods on the largest publicly available database of surface electromyography signals for polyarticulated self-powered hand prostheses. By exploiting the information collected over numerous subjects, these methods allow to reduce significantly the training time needed by any new prosthesis user. Our findings provide the bio robotics...
We address a particular scenario within the area of domain adaptation, where a predictive model obtained from a source domain can be applied directly to a target domain. Both source and target domains share the same input or feature space, but we do not impose any restrictions on the marginal and class posterior distributions (both distributions can differ). Our main assumption is that the difference...
We discuss the properties of a class of latent variable models that assumes each labeled sample is associated with a set of different features, with no prior knowledge of which feature is the most relevant feature to be used. Deformable-Part Models (DPM) can be seen as good examples of such models. These models are usually considered to be expensive to train and very sensitive to the initialization...
This paper investigates the problem of cross document image retrieval, i.e. use of query images from one style (say font) to perform retrieval from a collection which is in a different style (say a different set of books). We present two approaches to tackle this problem. We propose an effective style independent retrieval scheme using a nonlinear style-content separation model. We also propose a...
Attitude determination using vector observations and the related Adaptive Optimal-REQUEST algorithm are studied. Analysis of Optimal-REQUEST's characteristic is listed. In the condition that the observe noise changes frequently, improved adaptive method is imported. The model noise is estimated real-timely and it is used in the updating of states. The simulation indicated that the algorithm could...
This paper describes the design of parameter dependent robust model predictive controller (PD-RMPC) for a class of flexible air-breathing hypersonic vehicles. The strong system uncertainties, high nonlinearity, strong coupling, input saturation and flight state constraints are challenging problems in the design of control system. Therefore, a control method that can handle model uncertainties and...
Improving the use of energy resources has been a great challenge in the last years. A new complex scenario involving a decentralized bidirectional communication between energy suppliers, distribution system and consumption is nowadays becoming reality. Sometimes cited as the largest and most complex machine ever built, Electric Grids (EG) are been transformed into Smart Grids (SG). Hence, the load...
In this paper, we propose an observer based model reference adaptive iterative learning control (MRAILC) using model reference adaptive control strategy for more general class of uncertain nonlinear systems with non-canonical form and iteration-varying reference trajectories. Due to the system state vector is assumed to be unmeasurable, a state tracking error observer is applied for state tracking...
In this paper, we propose using distributed diffusion adaptive networks for acoustic signature identification, as a time-varying autoregressive (TVAR) stochastic model. A distributed adaptive sensor network considers spatio-temporal challenges simultaneously. To analyze diffusion networks under TVAR modeling problem circumstances, we investigate and elaborate on their performance under non-stationary...
This paper is concerned with the problem of tracking target with multiple sensors in the presence of unknown dynamic bias. A suboptimal adaptive two-stage Kalman filter (ATKF) is designed with two reduce-order filters to estimate the target state and the dynamic bias in parallel when the bias model information is incomplete. Moreover, a distributed adaptive two-stage Kalman filter (DATKF) is developed...
In recent years, information systems have become more diverse and complex making them a privileged target of network and computer attacks. These attacks have increased tremendously and turned out to be more sophisticated and evolving in an unpredictable manner. This work presents an attack model called AIDD (Attacks Identification Description and Defense). It offers a generic attack modeling to classify,...
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