The eLearning Department of the institute for computer science and control, Hungarian Academy of sciences (MTA SZTAKI) has become a key player of the domestic eLearning market in recent years. This role is based on it...
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ISBN:
(纸本)9781479924622
The eLearning Department of the institute for computer science and control, Hungarian Academy of sciences (MTA SZTAKI) has become a key player of the domestic eLearning market in recent years. This role is based on its multimedia technology experience in different areas of eLearning, including research, expertise, product and content development. The present document summarises the guidelines of the preparation of electronic educational curricula in accordance with the practice performed at the eLearning Department of MTA SZTAKI. We describe in detail the preparation of scripts facilitating the production of training materials. All major steps comprising these procedures are based on widely accepted international standards of eLearning. We introduce some selected eLearning projects of our department and describe our experience in content development for special devices, such as PC glasses and digital television.
Intrusion detection system (IDS) can identify abnormal network traffic and attacks, which is an important means of network security defense. However, some intrusion data are often disguised as normal data for transmis...
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Intrusion detection system (IDS) can identify abnormal network traffic and attacks, which is an important means of network security defense. However, some intrusion data are often disguised as normal data for transmission, which increases the difficulty of intrusion data classification. In addition, the existing packet-based or flow-based data feature extraction methods result in low feature dimensions, causing the problem of class overlapping between different categories with the same features. To clarify, overlapping samples are those that overlap between erroneous samples and correct samples. Nonoverlapping samples are those in the test set that do not match the characteristics of the already identified overlapping samples and are therefore considered nonoverlapping samples. Therefore, the detection effect of some attacks with high concealment is poor. In order to solve the above problems, this paper proposes a multistage intrusion detection method: an existing intrusion detection model with higher classification performance (OBLR) is used to predict the data in the first stage. In the second stage, for the overlapping data in the confusing data, the method learns the distribution of each feature group according to the randomly divided "intermediary set," and realizes the prediction of overlapping samples through the prior distribution knowledge, and achieves efficient classification of overlapping samples without increasing the computational burden of the model. For nonoverlapping data in the confusing data, KPCA (kernel principal component analysis) dimension elevation is used in the third stage to capture more detailed difference information between samples, and GMM (Gaussian mixed model) is combined with the "representative samples" proposed in this paper to assist classifier classification. At the same time, all the base classifiers are integrated through LTR (learning to rank) to improve the classification effect of the model for nonoverlapping data in the
Abstract: The initial boundary value problem regarding vibrations of an annular membrane is considered. Nonsteady boundary conditions are specified, and there is no distributed load. This is a nonclassical formulation...
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Abstract: For a one-dimensional boundary problem associated with a linear parabolic equation in the presence of a nonlocal spatial condition, necessary and sufficient conditions for the existence of a solution are est...
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Abstract: The initial model boundary value problem regarding vibrations of a viscoelastic beam with damping of Voigt type is considered. A classical formulation of a mixed problem for a fifth-order linear partial diff...
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The motion of viscous electrically conducting incompressible liquid is investigated, in circumstances where the liquid rotates initially in a solid mass at constant speed together with a porous boundary wall (plate) u...
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Abstract: A stochastic model of the growth of household (family) savings is considered. Conditions are identified such that there exists a solution to the mixed parabolic problem for the distribution density of househ...
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The authors consider the problem of reaching consensus over a communication network via asynchronous interaction between pairs of agents.A well-known method is the linear gossip algorithm due to Tsitsiklis(1984).Exten...
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The authors consider the problem of reaching consensus over a communication network via asynchronous interaction between pairs of agents.A well-known method is the linear gossip algorithm due to Tsitsiklis(1984).Extension of this,allowing the selection of a strictly stationary sequence of communicating pairs,was given in Picci and Taylor(2013).Extension of the linear gossip algorithm to directed communication networks,retaining the linear dynamics,was proposed by Cai and Ishii(2012),later extended by Silvestre,et al.(2018).A definite novelty of these algorithms is that L2-convergence with exponential rate can be *** authors attend the above issues,extending the result of Picci and Taylor(2013)motivated by features of algorithms for directed *** authors present and discuss the algorithm of Silvestre,et al.(2018),together with systematic simulation results based on 5M randomly chosen parameter *** core of the proposed mathematical technology is a set of simple observations,presented with a tutorial aspect,by which the authors can conveniently establish various results on the almost sure convergence of products of strictly stationary sequences of matrices to a rank-1 matrix.
This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking pe...
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This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.
COMPUTATIONAL knowledge vision [1] is emphasized as a novel perspective or field in this paper. It first proposes the visual hierarchy and its connection to knowledge, stating that knowledge is a justified true belief...
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COMPUTATIONAL knowledge vision [1] is emphasized as a novel perspective or field in this paper. It first proposes the visual hierarchy and its connection to knowledge, stating that knowledge is a justified true belief. To further the previous research, we concisely summarize our recent works and suggest a new direction that knowledge is also a thought framework in vision.
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