Vol 1, No 3 ( In Publishing)


Table of Contents


by Martina Benvenuti, Elvis Mazzoni
399 Views, 121 PDF Downloads
This research analyzes what predictors determine either a problematic internet use (PIU) or a functional Internet use (FIU) in 574 adolescents (303 females and 271 males). A cross-sectional study was proposed based on the compilation of an online questionnaire. It was hypothesized that Online Social-Support positively predicts PIU only when Offline Social-Support is low and Online Social-Support positively predicts FIU only when Offline Social-Support is high. Results show that Online Social-Support doesn't predict PIU, while, Offline Social-Support negatively affects It. FIU isn't affected by Offline Social-Support, while Online Social-Support predicts it. Gender differences occurs in PIU, Offline Social-Support and number of acquaintances in favor of males.


by Gustavo Botelho de Souza, Joao Paulo Papa, Aparecido Nilceu Marana
269 Views, 145 PDF Downloads
Biometrics emerged as a robust solution for security systems. Despite, nowadays criminals are developing techniques to accurately simulate biometric traits of valid users, process known as spoofing attack, in order to circumvent the biometric applications. Face is among the main biometric characteristics, being extremely convenient for users given its non-intrusive capture by means of digital cameras. However, face recognition systems are the ones that most suffer with spoofing attacks since such cameras, in general, can be easily fooled with common printed photographs. In this sense, countermeasure techniques should be developed and integrated to the traditional face recognition systems in order to prevent such frauds. Among the main neural networks for face spoofing detection is the discriminative Restricted Boltzmann Machine (RBM) which, besides of efficiency, achieves great results in attack detection by learning the distributions of real and fake facial images. However, it is known that deeper neural networks present better accuracy results in many tasks. In this context, we propose a novel model called Deep Discriminative Restricted Boltzmann Machine (DDRBM) applied to face spoofing detection. Results on the NUAA dataset show a significative improvement in performance when compared to the accuracy rates of a traditional discriminative RBM on attack detection.