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10.1371/journal.pone.0177851 http://scihub22266oqcxt.onion/10.1371/journal.pone.0177851 C5436828!5436828!28542248     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2017 ; 12 (5): ä Nephropedia Template TP {{tp|p=28542248|t=2017. The detection of faked identity using unexpected questions and mouse dynamics |pdf=|usr=}}{{28542248}} gab.com Text The detection of faked identity using unexpected questions and mouse dynamics JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=28542248 #FOAvip The detection of faked identities is a major problem in security. Current memory-detection techniques cannot be used as they require prior knowledge of the respondent?s true identity. Here, we report a novel technique for detecting faked identities based on the use of unexpected questions that may be used to check the respondent identity without any prior autobiographical information. While truth-tellers respond automatically to unexpected questions, liars have to ?build? and verify their responses. This lack of automaticity is reflected in the mouse movements used to record the responses as well as in the number of errors. Responses to unexpected questions are compared to responses to expected and control questions (i.e., questions to which a liar also must respond truthfully). Parameters that encode mouse movement were analyzed using machine learning classifiers and the results indicate that the mouse trajectories and errors on unexpected questions efficiently distinguish liars from truth-tellers. Furthermore, we showed that liars may be identified also when they are responding truthfully. Unexpected questions combined with the analysis of mouse movement may efficiently spot participants with faked identities without the need for any prior information on the examinee. Twit Text FOAVip The detection of faked identity using unexpected questions and mouse dynamics ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=28542248 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28542248 #FOAvip English Wikipedia <ref>{{Cite pmid|28542248}}</ref> The detection of faked identity using unexpected questions and mouse dynamics #MMPMID28542248 Monaro M; Gamberini L; Sartori G PLoS One 2017[]; 12 (5): ä PMID28542248show ga The detection of faked identities is a major problem in security. Current memory-detection techniques cannot be used as they require prior knowledge of the respondent?s true identity. Here, we report a novel technique for detecting faked identities based on the use of unexpected questions that may be used to check the respondent identity without any prior autobiographical information. While truth-tellers respond automatically to unexpected questions, liars have to ?build? and verify their responses. This lack of automaticity is reflected in the mouse movements used to record the responses as well as in the number of errors. Responses to unexpected questions are compared to responses to expected and control questions (i.e., questions to which a liar also must respond truthfully). Parameters that encode mouse movement were analyzed using machine learning classifiers and the results indicate that the mouse trajectories and errors on unexpected questions efficiently distinguish liars from truth-tellers. Furthermore, we showed that liars may be identified also when they are responding truthfully. Unexpected questions combined with the analysis of mouse movement may efficiently spot participants with faked identities without the need for any prior information on the examinee. äThe detection of faked identity using unexpected questions and mouse d(epmc).pdf DeepDyve Pubget Overpricing