Age Detection Through Keystroke Dynamics From User Authentication Failures

In this paper an incident response approach is proposed for handling detections of authentication failures in systems that employ dynamic biometric authentication and more specifically keystroke user recognition. The main component of the approach is a multi layer perceptron focusing on the age classification of a user. Empirical findings show that the classifier can detect the age of the subject with a probability that is far from the uniform random distribution, making the proposed method suitable for providing supporting yet circumstantial evidence during e-discovery

Direct observation of the two-stage excitation mechanism of Er in Si

In Er-doped silicon we have found direct evidence for the formation of the Er-related intermediate state that is a precursor of the final 4f-electron excited state responsible for the 0.8 eV luminescence. Time-resolved photoluminescence following band-gap illumination shows disruption of this center by a THz pulse from a free-electron laser. The decay of the intermediate state could be directly monitored in this double-beam experiment and a lifetime of approximately 100 μs has been found. In this way the most characteristic step in the excitation mechanism of the Er ion in silicon has been revealed experimentally

Optically Detected Cyclotron Resonance Studies of High Eelectron Mobility AlGaAs/GaAs Structures

Optically detected cyclotron resonance is used for the identification of recombination transitions of two-dimensional electron gas in A1GaAs/GaAs heterostructures. Two photoluminescence emissions are attributed to the recombination of the two-dimensional electron gas. These are the so-called H-band and the Fermi level singularity photoluminescence. Optical detection of cyclotron resonance is related to the change of the band bending across the GaAs active layer and the AlGaAs barrier, which is caused by impact ionization of shallow donors in the barrier region. Influence of a long range carrier scattering on ionized impurities on a mobility of the two-dimensional carriers is studied

Towards Robotic Marble Resin Application: Crack Detection on Marble Using Deep Learning

Cracks can occur on different surfaces such as buildings, roads, aircrafts, etc. The manual inspection of cracks is time-consuming and prone to human error. Machine vision has been used for decades to detect defects in materials in production lines. However, the detection or segmentation of cracks on a randomly textured surface, such as marble, has not been sufficiently investigated. This work provides an up-to-date systematic and exhaustive study on marble crack segmentation with color images based on deep learning (DL) techniques. The authors conducted a performance evaluation of 112 DL segmentation models with red–green–blue (RGB) marble slab images using five-fold cross-validation, providing consistent evaluation metrics in terms of Intersection over Union (IoU), precision, recall and F1 score to identify the segmentation challenges related to marble cracks’ physiology. Comparative results reveal the FPN model as the most efficient architecture, scoring 71.35% mean IoU, and SE-ResNet as the most effective feature extraction network family. The results indicate the importance of selecting the appropriate Loss function and backbone network, underline the challenges related to the marble crack segmentation problem, and pose an important step towards the robotic automation of crack segmentation and simultaneous resin application to heal cracks in marble-processing plants

Towards Robotic Marble Resin Application: Crack Detection on Marble Using Deep Learning

Cracks can occur on different surfaces such as buildings, roads, aircrafts, etc. The manual inspection of cracks is time-consuming and prone to human error. Machine vision has been used for decades to detect defects in materials in production lines. However, the detection or segmentation of cracks on a randomly textured surface, such as marble, has not been sufficiently investigated. This work provides an up-to-date systematic and exhaustive study on marble crack segmentation with color images based on deep learning (DL) techniques. The authors conducted a performance evaluation of 112 DL segmentation models with red–green–blue (RGB) marble slab images using five-fold cross-validation, providing consistent evaluation metrics in terms of Intersection over Union (IoU), precision, recall and F1 score to identify the segmentation challenges related to marble cracks’ physiology. Comparative results reveal the FPN model as the most efficient architecture, scoring 71.35% mean IoU, and SE-ResNet as the most effective feature extraction network family. The results indicate the importance of selecting the appropriate Loss function and backbone network, underline the challenges related to the marble crack segmentation problem, and pose an important step towards the robotic automation of crack segmentation and simultaneous resin application to heal cracks in marble-processing plants

The role of hypercoagulability in ischemic colitis

Objective. The aim of this study is to evaluate the role of thrombophilia-hypercoagulability in ischemic colitis (IC). Material and methods. Thrombophilia and fibrinogen were evaluated in 56 cases of IC and 44 controls with known predisposing factors but no evidence of IC. Thrombophilic factors tested were: protein C (PC), protein S, antithrombin (AT), resistance to activated protein C (APCR), lupus anticoagulant (LA), factor V G1691A mutation (FV Leiden), prothrombin G20210A mutation, methylenetetrahydrofolate reductase (MTHFR) gene C677T and A1298C mutations and plasminogen activator inhibitor-1 (PAI-1) gene 5G/4G and 4G/4G polymorphisms. Results. In IC group were recorded: i) low levels of PC and AT (p = 0.064 and p = 0.022, respectively); ii) low levels of APCR (normal: >2, p = 0.008); iii) high levels of fibrinogen (p = 0.0005); iv) higher number of homozygotes for MTHFR A1298C and C677T mutations (p = 0.061 and p = 0.525 (Pearson chi-square), respectively); v) greater prevalence of 5G/4G and 4G/4G polymorphisms (p = 0.031 (Pearson chi-square)) and vi) higher incidence of LA-positive individuals (p = 0.037, Fischer's exact test). Multivariate analysis was performed to determine the effects of prothrombotic factors in IC. 5G/4G polymorphism of PAI-1 gene (odds ratio (OR) 12.29; 95% confidence interval (CI) 2.26-67.00), APCR (OR 0.089; 95% CI 0.011-0.699) and fibrinogen (OR 1.013; 95% CI 1.003-1.023) were determined as predictors of IC. Conclusions. This study suggests that hypercoagulability, hereditary or acquired, plays an essential role in the manifestation of IC