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  • 1
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    German Medical Science GMS Publishing House; Düsseldorf
    In:  GMS German Medical Science; VOL: 8; DOC33 /20101129/
    Publication Date: 2010-11-30
    Description: Urethral duplication is a rare congenital anomaly of the lower urinary system and has varied presentation. According to the Effmann classification, type IIA2-Y urethral duplication is charcterized by the duplicated urethra originating from the bladder neck and opening into either the rectum or the perineum. The accessory urethra is normal and functional and the normally positioned dorsal urethra is hypoplastic and stenotic in unusual form of Y-type duplication. We present a new case with unusual form of Y-type duplication and discuss its treatment.
    Description: Die Doppelung des Harnleiters ist eine seltene angeborene Störung der ableitenden Harnwege und hat unterschiedliche Einordnungen erfahren. Beim Typ II A-Y (Klassifikation von Effman) beginnt die Doppelung der Urethra am Blasenhals und mündet entweder im Rektum oder am Perineum. Der zusätzliche Harnleiter ist normal ausgebildet und funktional. Der normal positionierte dorsale Harnleiter ist hypoplastisch und in einer ungewöhnlichen Y-Form stenosiert. Hier wird ein neuer Fall von ungewöhnlicher Harnleiterdoppelung vom Y-Typ vorgestellt und die zugehörige Therapie diskutiert.
    Keywords: congenital abnormalities ; fistula ; duplication ; urethra ; perineum ; angeborene Fehlbildungen ; Fistel ; Doppelung ; Harnleiter ; ddc: 610
    Language: English
    Type: article
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  • 2
    Publication Date: 2018-02-06
    Description: Future Internet, Vol. 10, Pages 17: Increasing Trustworthiness of Face Authentication in Mobile Devices by Modeling Gesture Behavior and Location Using Neural Networks Future Internet doi: 10.3390/fi10020017 Authors: Blerim Rexha Gresa Shala Valon Xhafa Personal mobile devices currently have access to a significant portion of their user’s private sensitive data and are increasingly used for processing mobile payments. Consequently, securing access to these mobile devices is a requirement for securing access to the sensitive data and potentially costly services. Face authentication is one of the promising biometrics-based user authentication mechanisms that has been widely available in this era of mobile computing. With a built-in camera capability on smartphones, tablets, and laptops, face authentication provides an attractive alternative of legacy passwords for its memory-less authentication process, which is so sophisticated that it can unlock the device faster than a fingerprint. Nevertheless, face authentication in the context of smartphones has proven to be vulnerable to attacks. In most current implementations, a sufficiently high-resolution face image displayed on another mobile device will be enough to circumvent security measures and bypass the authentication process. In order to prevent such bypass attacks, gesture recognition together with location is proposed to be additionally modeled. Gestures provide a faster and more convenient method of authentication compared to a complex password. The focus of this paper is to build a secure authentication system with face, location and gesture recognition as components. User gestures and location data are a sequence of time series; therefore, in this paper we propose to use unsupervised learning in the long short-term memory recurrent neural network to actively learn to recognize, group and discriminate user gestures and location. Moreover, a clustering-based technique is also implemented for recognizing gestures and location.
    Electronic ISSN: 1999-5903
    Topics: Computer Science
    Published by MDPI Publishing
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