In the paper the existing information on the optical excitation of the erbium ion in crystalline silicon is critically reviewed. The proposed excitation mechanism is compared to the one which is believed to be responsible for the luminescence of ytterbium in indium phosphide. To this end the influence of constant and microwave electric field on the photoluminescence of both systems is inspected. It is shown that, although both systems show some similarities, their analogy is limited.The particular role of excitons and electrons in both the excitation as well as the de-excitation mechanism is investigated for the Si:Er system. The results of photoluminescence decay studies (T=4.2 K) are presented. It is argued that a nonradiative energy transfer to conduction electrons is responsible for the limitation of the energy transfer to the Er core and for its nonradiative recombination. Also, a prominent role of excitons in the energy transfer mechanism is confirmed. Finally, the origin of the 873 meV photoluminescence band recently reported in Er-implanted Si is discussed in relation to a possible defect-mediated activation of Er.
Over the past decade keystroke-based pattern recognition techniques as a forensic tool for behavioural biometrics have gained increasing attention. Although a number of machine learning based approaches have been proposed, they are limited in terms of their capability to recognise and profile a set of individual's characteristics. In addition, up to today their focus was primarily gender and age, which seem to be more appropriate for commercial applications (such as developing commercial software), leaving out from research other characteristics, such as the educational level. Educational level is an acquired user characteristic, which can improve targeted advertising, as well as provide valuable information in a digital forensic investigation, when it is known. In this context, this paper proposes a novel machine learning model, the Randomised Radial Basis function Network (R 2 BN), which recognises and profiles the educational level of an individual who stands behind the keyboard. The performance of the proposed model is evaluated by using empirical data obtained by recording volunteers' keystrokes during their daily usage of a computer. Its performance is also compared with other wellreferenced machine learning models using our keystroke dynamic datasets. Although the proposed model achieves high accuracy in educational level prediction of an unknown user, it suffers from high computational cost. For this reason, we examine ways to reduce the time that is needed to build our model, including the use of a novel data condensation method, and discuss the trade off between an accurate and a fast prediction. To the best of our knowledge, this is the first model in the literature that predicts the educational level of an individual based on keystroke dynamics information only.
Keystroke dynamics are used to authenticate users, to reveal some of their inherent or acquired characteristics and to assess their mental and physical states. The most common features utilized are the time intervals that the keys remain pressed and the time intervals that are required to use two consecutive keys. This paper examines which of these features are the most important and how utilization of these features can lead to better classification results. To achieve this, an existing dataset consisting of 387 logfiles is used, five classifiers are exploited and users are classified by gender and age. The results, while demonstrating the application of these two characteristics jointly on classifiers with high accuracy, answer the question of which keystroke dynamics features are more appropriate for classification with common classifiers.
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