INFORMS International Conference
Introduction to Monte Carlo and Discrete-Event Simulation
San Jose, CA
Essential Practice Skills for High-Impact Analytics Projects
CAP NewsVideo Learning Center features analytics conference highlights
If you couldn’t attend the 2016 INFORMS Conference on Analytics & Operations Research or if you were at the Orlando event but missed some presentations, the INFORMS Video Learning Center has you covered. Feel free to use the videos as teaching material or to share with others to let them know how OR/MS and analytics are helping to make a difference in the World.Read More
Special ArticlesSeven best practices for an effective project management office
Gartner, Inc. has identified seven best practices that project management offices (PMO) leaders should employ to improve the effectiveness of project, portfolio and program management and demonstrate they can support the wider organization and its strategic goals.Read More
Special ArticlesIBM’s Watson learning nuances of security research
Watson for Cyber Security, a cloud-based version of IBM’s cognitive technology, trained on the language of security as part of a year-long research project with eight universities to greatly expand the collection of security data IBM has trained the cognitive system with. Watson is learning the nuances of security research findings and discovering patterns and evidence of hidden cyber attacks and threats that could otherwise be missed.Read More
Statistical model unlocks barriers to fingerprint evidence
Potentially key fingerprint evidence is currently not being considered due to shortcomings in the way it is reported, according to a report published in the February issue of Significance, the magazine of the Royal Statistical Society and the American Statistical Association. Researchers involved in the study have devised a statistical model to enable the weight of fingerprint evidence to be quantified, paving the way for its full inclusion in the criminal identification process.
Fingerprints have been used for over a century as a way of identifying criminals. However, fingerprint evidence is not currently permitted to be reported in court unless examiners claim absolute certainty that a mark has been left by a particular suspect. This courtroom certainty is based purely on categorical personal opinion, formed through years of training and experience, but not on logic or scientific data. Less than certain fingerprint evidence is not reported at all, irrespective of the potential weight and relevance of this evidence in a case.
The paper highlights this subjectivity in current processes, calling for changes in the way such key evidence is allowed to be presented. According to Professor of Statistics Cedric Neumann, “It is unthinkable that such valuable evidence should not be reported, effectively hidden from courts on a regular basis. Such is the importance of this wealth of data, we have devised a reliable statistical model to enable the courts to evaluate fingerprint evidence within a framework similar to that which underpins DNA evidence.”
Neumann, from Pennsylvania State University, and his team devised and successfully tested a model for establishing the probability of a print belonging to a particular suspect. After mapping the finer points of detail on a “control print” and “crime scene print,” two hypotheses were then tested. The first test, to establish the probability that the crime scene print was made by the owner of the control print (the suspect), compared the control print with a range of other prints made by the suspect. The second test, to establish the probability that the crime scene print was made by someone other than the suspect, compared the crime scene print with a set of prints in a reference database. A likelihood ratio between the two probabilities was calculated; the higher the ratio indicating stronger evidence that the suspect was the source of the crime scene print.
“Current practice allows a state of certainty to be presented which is not justified scientifically, or supported by logical process or data,” says Neumann. “We believe that the examiner should not decide what evidence should or should not be presented. Our method allows all evidence to be supported by data, and reported according to a continuous scale.”