Towards a Bayesian evaluation of features in questioned handwritten signatures

In this work, we propose the construction of a evaluative framework for supporting experts in questioned signature examinations. Through the use of Bayesian networks, we envision to quantify the probative value of well defined measurements performed on questioned signatures, in a way that is both formalised and part of a coherent approach to evaluation. At the current stage, our project is explorative, focusing on the broad range of aspects that relate to comparative signature examinations. The goal is to identify writing features which are both highly discriminant, and easy for forensic examiners to detect. We also seek for a balance between case-specific features and characteristics which can be measured in the vast majority of signatures. Care is also taken at preserving the interpretability at every step of the reasoning process. This paves the way for future work, which will aim at merging the different contributions to a single probabilistic measure of strength of evidence using Bayesian networks

The athletic characteristics of Olympic sports to assist anti-doping strategies.

The determinants of success in Olympic Games competition are specific to the athletic demands of the sporting event. A global evaluation to quantify the athletic demands across the spectrum of the Olympic Games sport events has not previously been conducted. Thus far, the interpretation and the comparison of sport physiological characteristics within anti-doping organisations (ADOs) risk assessments remains subjective without a standardised framework. Despite its subjective assessment, this information is a key component of any anti-doping programme. Sport characteristics inevitably influence the type of substances and/or methods used for doping purposes and should be captured through a comprehensive analysis. Seven applied sport scientists independently conducted an assessment to quantify the athletic demands across six preselected athletic variables. A principal component analysis was performed on the results of the panel's quantitative assessment for 160 Olympic sport events. Sport events were clustered using the Hierarchical Density Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm. The HDBSCAN identified 19 independent cluster groups; 36 sport events remained statistically unassigned to a cluster group representing unique and event-specific athletic demands. This investigation provides guidance to the anti-doping community to assist in the development of the sport specific physiology component of the risk assessment for Olympic Games disciplines. The dominant athletic characteristics to excel in each of these individual events will highlight areas of how athletes may strive to gain a competitive advantage through doping strategies, and inform the development of an effective and proportionate allocation of testing resources