2,329 research outputs found
Forensic inference and statistics for the evaluation and interpretation of evidence
No full textEvidence in a criminal investigation and trial should be evaluated and interpreted in the best manner possible. An excellent, and widely supported, approach to evaluation is one - by nature probabilistic - based on the likelihood ratio, the ratio of the probability of the evidence if a certain (set of) proposition(s) (e.g., prosecution) is assumed true to the probability of the evidence if a contrasting (set of) proposition(s) (e.g., defence) is assumed true. The justification for this approach is given together with a note of the benefits arising from the use of this ratio. There is a discussion about the meaning of probability as a measure of belief, the use of numerical assignments and verbal expressions and the use of the likelihood ratio for interpretation. It is explained how beliefs can be updated in the light of new evidence, how multiple pieces of evidence may be evaluated with the use of graphical structures and how uncertainty associated with the numerical evaluation, which is probabilistic, may be handled. A procedure for judgement of the quality of the mathematical formulae used in the calculation of the likelihood ratio is outlined. A conclusion gives three important principles that an expert, and a forensic scientist in particular, should follow when trying to understand the importance of evidence
A new strong-lensing model for the massive galaxy cluster PLCKG287.0+32.9
No full textWe present a new high-precision strong lensing model of PLCK G287.0+32.9, a massive lens galaxy cluster at z = 0.383, based on 153 spectroscopically and photometrically selected member galaxies and 47 secure multiple images of 12 background multiply-lensed sources. The total mass distribution derived from our model shows this cluster to be a very prominent gravitational lens with the third biggest Einstein radius of θE = 43.4'' ± 0.1'' known to date
Data analysis in forensic science: A Bayesian decision perspective.
No full textThe use of formal statistical methods to analyse quantitative data in forensic science has increased considerably over the last few years. Students, researchers and practitioners in forensic science regularly ask questions concerning the relative merits of differing approaches, in particular the frequentist and Bayesian approaches, to statistical inference in the forensic context. The ideas of the Bayesian approach in forensic science are now being extended to include decision theory and the associated concept of utility.
Data Analysis in Forensic Science: A Bayesian Decision Perspective sets forth procedures for data analysis that rely on the decision-theoretic approach to inference. Emphasis is made on foundational philosophical tenets as well as the implications of the decision-theoretic approach in practice. This book discusses a range of statistical decision-theoretic methods that are useful in the analysis of forensic scientific data. Forensic scientific examples include point estimation, the comparison of means and proportions in populations, the choice of sample size and the classification of items of evidence of unknown origin into predefined populations.
Key Features:
- Comprehensive coverage of the analysis of forensic data from a Bayesian perspective, featuring numerous real-world examples and applications.
- Explanation and definition of key concepts and methods from historical, philosophical, and theoretical points of view.
- An incremental approach for consideration of examples inspired and motivated by issues that may arise in routine forensic practice.
- Consideration of the arguments and methods, including those of decision theory, used at each stage of the analyses.
- Inclusion of code written in R to offer an opportunity for enhanced exploration of the ideas.
- The use of graphical models (e.g. Bayesian networks) to illustrate selected applications of Bayesian methodology
Reframing the debate: a question of probability, not of likelihood ratio
No full textEvidential value is measured by a likelihood ratio. This ratio has two components, the probability, or probability density, of the evidence if the prosecution proposition is true and the probability (density) of the evidence if the defence proposition is true. It takes the form of a single value, even if these probabilities are subjective measures of belief of the reporting forensic scientist
Automatic microscope systems in the CHORUS experiment
No full textCHORUS searches for tau neutrino appearance in interactions of an originally pure nu submu beam with an active target built with nuclear emulsions. The sample of events located and studied up to now (about 170 000)(Phys. Lett. B, to be published) could not have been collected without automatic microscope systems. The principles and the features of the technique are outlined; further details are then given about various implementations. The status and future developments of the various systems are described
Stylometry and forensic science: A literature review
No full textThe article focuses on a careful description of literature on stylometry and on its potential use in forensic science. The state of the art of stylometry is summarized to illustrate the history and the scientific foundation of this discipline. However, the study conducted reveals that there are still some key unresolved aspects that require a response from the academic world. The paper introduces the readers to those issues that need to be tackled for stylometry to be accepted as a forensic discipline. In particular, a coherent probabilistic procedure to assess the probative value of the results obtained through this methodology is largely absent. This gap should be filled properly by applying criteria recommended by international organizations such as the European Network of Forensic Science Institutes. Solutions do exist and will allow a better integration of stylometry in forensic science, favouring the acceptance of this scientific technical method in judicial proceedings
A model-independent redundancy measure for human versus ChatGPT authorship discrimination using a Bayesian probabilistic approach
No full textThe academic and scientific world in general is increasingly concerned about their inability to determine and ascertain the identity of the writer of a text. More and more often the question arises as to whether a scientific article or work handed in by a student was actually produced by the alleged author of the questioned text. The role of artificial intelligence (AI) is increasingly debated due to its dangers of undeclared use. A current example is undoubtedly the undeclared use of ChatGPT to write a scientific text. The article promotes an AI model-independent redundancy measure to support discrimination between hypotheses on authorship of various multilingual texts written by humans or produced by intelligence media such as ChatGPT. The syntax of texts written by humans tends to differ from that of texts produced by AIs. This difference can be grasped and quantified even with short texts (i.e. 1800 characters). This aspect of length is extremely important, because short texts imply a greater difficulty of analysis to characterize authorship. To meet the efficiency criteria required for the evaluation of forensic evidence, a probabilistic approach is implemented. In particular, to assess the value of the redundancy measure and to offer a consistent classification criterion, a metric called Bayes factor is implemented. The proposed Bayesian probabilistic method represents an original approach in stylometry. Analyses performed over multilingual texts (English and French) covering different scientific and human areas of interest (forensic science and socio-psycho-artistic topics) reveal the feasibility of a successful authorship discrimination with limited misclassification rates. Model performance is satisfactory even with small sample sizes
KM3NeT Acquisition Control: advanced techniques and best practices in data acquisition software development
No full textThe Control Unit of the KM3NeT Data Acquisition is the software suite that is respon-
sible for operating all the components of the KM3NeT telescopes in a coordinated and scientifically
proficient way. It controls a wide span of parameters and procedures, from the power supplies, to
the operating voltages of more than 64000 photomultipliers in each detector block, to the setup of
the various trigger algorithms that are applied online. The same software suite is also designed to be
used in all test and qualification benches, from single Digital Optical Modules to full Detection Units.
As the KM3NeT detectors are being incrementally built, the Control Unit is employed in a variety
of setups and configurations, and is a dynamic software project, still adapting to shifting needs.
The conflicting requirements of flexibility and stability are reconciled by proper code develop-
ment policies. The Control Unit is able to cope with dynamically changing scenarios of multiple
firmware generations coexisting in the same detector, for various reasons including hardware com-
patibility as well as testing purposes. The code also allows for static verification and extensive unit
tests.
A Central Logic Board Simulator software was also developed to help testing the whole
architecture. Such a simulator provides properly faked slow control parameters, features a fully
specification-compliant state machine and can generate fake data with specific profiles to feed the
Trigger and Data Acquisition System. In this way, offline integration tests can be executed at each
new software release, ensuring their smooth deployment to production sites and minimising chances
of mistakes by operators
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