公开(公告)号：EP2705458A2

公开(公告)日：2014-03-12

申请号：EP12782525.5

申请日：2012-05-04

申请人： Duquesne University of the Holy Spirit ,  Juola, Patrick ,  Overly, James Orlo ,  Noecker, John Isaac ,  Ryan, Michael ,  Gray, Christine

发明人： JUOLA, Patrick ,  OVERLY, James, Orlo ,  NOECKER, John, Isaac ,  RYAN, Michael ,  GRAY, Christine

IPC分类号： G06F21/00 ,  G06F17/00

CPC分类号： G06Q10/10 ,  G06F17/274 ,  G06F21/31 ,  G06F2221/2131 ,  G06Q50/184

摘要： Novel distractorless authorship verification technology optionally combines with novel algorithms to solve authorship attribution as to an open set of candidates—such as without limitation by analyzing the voting of "mixture of experts" and outputting the result to a user using the following: if z (z = p
i - p
j √ p
i + p
j - (p
i - p
j )
2 /n) is larger than a first predetermined threshold then author j cannot be the correct author; or if z (z = p
i - p
j √ p
i + p
j - (p
i - p
j )
2 /n) is smaller than a second predetermined threshold then author i cannot be the correct author; or if no author garners significantly more votes than all other contenders then none of the named authors is the author of a document in question— in a number of novel applications. Personality profiling and authorship attribution may also be used to verify user identity to a computer.

摘要翻译： 新颖无牵强的作者身份验证技术可选地与新颖的算法相结合，以解决关于一组开放的候选人的作者身份归属问题 - 例如但不限于通过分析“专家混合物”的投票并使用以下方式将结果输出给用户：if z（ z = pi-pj + pi + pj-（pi-pj）2 / n）大于第一预定阈值，则作者j不能是正确的作者; 或者如果z（z = p i -p j≠p i + p j-（p i -p j）2 / n）小于第二预定阈值，则作者i不能是正确的作者; 或者如果没有作者获得比所有其他竞争者多得多的投票，那么所有提名的作者都不是所讨论文档的作者 - 在许多新颖的应用中。 个性分析和作者身份归因也可用于验证计算机的用户身份。

公开(公告)号：EP2705458B1

公开(公告)日：2018-04-04

申请号：EP12782525.5

申请日：2012-05-04

申请人： Duquesne University of the Holy Spirit ,  Juola, Patrick ,  Overly, James Orlo ,  Noecker, John Isaac ,  Ryan, Michael ,  Gray, Christine

发明人： JUOLA, Patrick ,  OVERLY, James, Orlo ,  NOECKER, John, Isaac ,  RYAN, Michael ,  GRAY, Christine

IPC分类号： G06F21/31

CPC分类号： G06Q10/10 ,  G06F17/274 ,  G06F21/31 ,  G06F2221/2131 ,  G06Q50/184

摘要： Novel distractorless authorship verification technology optionally combines with novel algorithms to solve authorship attribution as to an open set of candidates—such as without limitation by analyzing the voting of “mixture of experts” and outputting the result to a user using the following: if z (z=pi−pj√pi+pj−(pi−pj)2/n) is larger than a first predetermined threshold then author j cannot be the correct author; or if z (z=pi−pj√pi+pj−(pi−pj)2/n) is smaller than a second predetermined threshold then author i cannot be the correct author; or if no author garners significantly more votes than all other contenders then none of the named authors is the author of a document in question—in a number of novel applications. Personality profiling and authorship attribution may also be used to verify user identity to a computer.