1,721,238 research outputs found
The management of primary schools in twenty years of the «scuola italiana moderna» journal (1946-1966)
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Perturbation Method to Calculate the Interaction Potentials and Electronic Excitation Spectra of Atoms in He Nanodroplets
No full textA method is proposed for the calculation of potential energy curves and related electronic excitation spectra of dopant atoms captured in/on He nanodroplets and is applied to alkali metal atoms. The method requires knowledge of the droplet density distribution at equilibrium (here calculated within a bosonic-He density functional approach) and of a set of valence electron orbitals of the bare dopant atom (here calculated by numeric solution of the Schrodinger equation in a suitably parametrized model potential). The electron helium interaction is added as a perturbation, and potential energy curves are obtained by numeric diagonalization of the resulting Hamiltonian as a function of an effective coordinate z(A) (here the distance between the dopant atom and center of mass of the droplet, resulting in a pseudodiatomic potential). Excitation spectra are calculated for Na in the companion paper as the Franck-Condon factors between the nu = 0 vibrational state in the ground electronic state and excited states of the pseudodiatomic molecule. They agree well with available experimental data, even for highly excited states where a more traditional approach fails
On the proper choice of datasets and traffic features for realtime anomaly detection
No full textThanks to its ability to face unknown attacks, Anomaly-based Intrusion Detection is a key research topic in network security and different statistical methods, fed by suitable traffic features, have been proposed in the literature. The choice of a proper dataset is a critical element not only for performance comparison, but also for the correct identification of the normal traffic behaviour. In this paper we address the general problem of selecting traffic features from recent real traffic traces (MAWI data set) and verify how the real-time constraint impacts on the general performance. Although a state-of-the-art IDS (Intrusion Detection System) based on deep neural networks is considered, our conclusions can be extended to any anomaly detection algorithm and advocate for a fair comparison of IDSs using representative datasets and traffic features that can be extracted on-line (and do not depend on the entire dataset)
A Real Time Deep Learning Based Approach for Detecting Network Attacks
No full textAnomaly-based Intrusion Detection is a key research topic in network security due to its ability to face unknown attacks and new security threats. For this reason, many works on the topic have been proposed in the last decade. Nonetheless, an ultimate solution, able to provide a high detection rate with an acceptable false alarm rate, has still to be identified. In the last years big research efforts have focused on the application of Deep Learning techniques to the field, but no work has been able, so far, to propose a system achieving good detection performance, while processing raw network traffic in real time. For this reason in the paper we propose an Intrusion Detection System that, leveraging on probabilistic data structures and Deep Learning techniques, is able to process in real time the traffic collected in a backbone network, offering excellent detection performance and low false alarm rate. Indeed, the extensive experimental tests, run to validate our system and compare different Deep Learning techniques, confirm that, with a proper parameter setting, we can achieve about 92% of detection rate, with an accuracy of 0.899. Finally, with minimal changes, the proposed system can provide some information about the kind of anomaly, although in the multi-class scenario the detection rate is slightly lower (around 86%)
Prediction of mobile networks traffic: enhancement of the NMLS technique
No full textThe today networks evolution requires the prediction of traffic demand in order to efficiently use the available resources. The traffic load prediction can be exploited to dynamically allocate the network resources among the different users that, in the 5G world, can be the different verticals. In this scenario, we analyse the application of classical time series predictors to the mobile network traffic in order to evaluate the performance of the considered approaches in terms of complexity and prediction accuracy. Furthermore, we propose an enhancement to the classical Normalized Least Mean Square (NMLS) in order to increase its prediction accuracy, with a negligible complexity increase. The enhancement is based on the application of the Chebyshev's inequality to estimate the prediction error bound. This statistical bound is used to correct the prediction error. The simulation analysis shows the performance improvements given by the proposed scheme
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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