The study of human factors in forensic science informs our understanding of the interaction between humans and the systems they use. The Expert Working Group (EWG) on Human Factors in Forensic DNA Interpretation used a systems approach to conduct a scientific assessment of the effects of human factors on forensic DNA interpretation with the goal of recommending approaches to improve practice and reduce the likelihood and consequence of errors. This effort resulted in 44 recommendations. The EWG designed many of these recommendations to improve the production, interpretation, evaluation, documentation, and communication of DNA comparison results.
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Aims and objectives. The Forensic Early Warning Signs of Aggression Inventory (FESAI) was developed to assist nurses and patients in identifying early warning signs and constructing individual early detection plans (EDP) for the prevention of aggressive incidents. The aims of this research were as follows: First, to study the prevalence of early warning signs of aggression, measured with the FESAI, in a sample of forensic patients, and second, to explore whether there are any types of warning signs typical of diagnostic subgroups or offender subgroups. Background. Reconstructing patients’ changes in behaviour prior to aggressive incidents may contribute to identify early warning signs specific to the individual patient. The EDP comprises an early intervention strategy suggested by the patient and approved by the nurses. Implementation of EDP may enhance efficient risk assessment and management. Design. An explorative design was used to review existing records and to monitor frequencies of early warning signs. Methods. Early detection plans of 171 patients from two forensic hospital wards were examined. Frequency distributions were estimated by recording the early warning signs on the FESAI. Rank order correlation analyses were conducted to compare diagnostic subgroups and offender subgroups concerning types and frequencies of warning signs. Results. The FESAI categories with the highest frequency rank were the following: (1) anger, (2) social withdrawal, (3) superficial contact and (4) non-aggressive antisocial behaviour. There were no significant differences between subgroups of patients concerning the ranks of the four categories of early warning signs. Conclusion. The results suggest that the FESAI covers very well the wide variety of occurred warning signs reported in the EDPs. No group profiles of warning signs were found to be specific to diagnosis or offence type. Relevance to clinical practice. Applying the FESAI to develop individual EDPs appears to be a promising approach to enhance risk assessment and management.
Aggressive incidents occur frequently in health care facilities, such as psychiatric care and forensic psychiatric hospitals. Previous research suggests that civil psychiatric inpatients may display more aggression than forensic inpatients. However, there is a lack of research comparing these groups on the incident severity, even though both frequency and severity of aggression influence the impact on staff members. The purpose of this study is to compare the frequency and severity of inpatient aggression caused by forensic and civil psychiatric inpatients in the same Dutch forensic psychiatric hospital. Data on aggressive incidents occurring between January 1, 2014, and December 31, 2017, were gathered from hospital files and analyzed using the Modified Overt Aggression Scale, including sexual aggression (MOAS+). Multilevel random intercept models were used to analyze differences between forensic and civil psychiatric patients in severity of aggressive incidents. In all, 3,603 aggressive incidents were recorded, caused by 344 different patients. Civil psychiatric patients caused more aggressive incidents than forensic patients and female patients caused more inpatient aggression compared with male patients. Female forensic patients were found to cause the most severe incidents, followed by female civil psychiatric patients. Male forensic patients caused the least severe incidents. The findings have important clinical implications, such as corroborating the need for an intensive treatment program for aggressive and disruptive civil psychiatric patients, as well as emphasizing the importance of gender-responsive treatment
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Every year the police are confronted with an ever increasing number of complex cases involving missing persons. About 100 people are reported missing every year in the Netherlands, of which, an unknown number become victims of crime, and presumed buried in clandestine graves. Similarly, according to NWVA, several dead animals are also often buried illegally in clandestine graves in farm lands, which may result in the spread of diseases that have significant consequences to other animals and humans in general. Forensic investigators from both the national police (NP) and NWVA are often confronted with a dilemma: speed versus carefulness and precision. However, the current forensic investigation process of identifying and localizing clandestine graves are often labor intensive, time consuming and employ classical techniques, such as walking sticks and dogs (Police), which are not effective. Therefore, there is an urgent request from the forensic investigators to develop a new method to detect and localize clandestine graves quickly, efficiently and effectively. In this project, together with practitioners, knowledge institutes, SMEs and Field labs, practical research will be carried out to devise a new forensic investigation process to identify clandestine graves using an autonomous Crime Scene Investigative (CSI) drone. The new work process will exploit the newly adopted EU-wide drone regulation that relaxes a number of previously imposed flight restrictions. Moreover, it will effectively optimize the available drone and perception technologies in order to achieve the desired functionality, performance and operational safety in detecting/localizing clandestine graves autonomously. The proposed method will be demonstrated and validated in practical operational environments. This project will also make a demonstrable contribution to the renewal of higher professional education. The police and NVWA will be equipped with operating procedures, legislative knowledge, skills and technological expertise needed to effectively and efficiently performed their forensic investigations.
The project aim is to improve collusion resistance of real-world content delivery systems. The research will address the following topics: • Dynamic tracing. Improve the Laarhoven et al. dynamic tracing constructions [1,2] [A11,A19]. Modify the tally based decoder [A1,A3] to make use of dynamic side information. • Defense against multi-channel attacks. Colluders can easily spread the usage of their content access keys over multiple channels, thus making tracing more difficult. These attack scenarios have hardly been studied. Our aim is to reach the same level of understanding as in the single-channel case, i.e. to know the location of the saddlepoint and to derive good accusation scores. Preferably we want to tackle multi-channel dynamic tracing. • Watermarking layer. The watermarking layer (how to embed secret information into content) and the coding layer (what symbols to embed) are mostly treated independently. By using soft decoding techniques and exploiting the “nuts and bolts” of the embedding technique as an extra engineering degree of freedom, one should be able to improve collusion resistance. • Machine Learning. Finding a score function against unknown attacks is difficult. For non-binary decisions there exists no optimal procedure like Neyman-Pearson scoring. We want to investigate if machine learning can yield a reliable way to classify users as attacker or innocent. • Attacker cost/benefit analysis. For the various use cases (static versus dynamic, single-channel versus multi-channel) we will devise economic models and use these to determine the range of operational parameters where the attackers have a financial benefit. For the first three topics we have a fairly accurate idea how they can be achieved, based on work done in the CREST project, which was headed by the main applicant. Neural Networks (NNs) have enjoyed great success in recognizing patterns, particularly Convolutional NNs in image recognition. Recurrent NNs ("LSTM networks") are successfully applied in translation tasks. We plan to combine these two approaches, inspired by traditional score functions, to study whether they can lead to improved tracing. An often-overlooked reality is that large-scale piracy runs as a for-profit business. Thus countermeasures need not be perfect, as long as they increase the attack cost enough to make piracy unattractive. In the field of collusion resistance, this cost analysis has never been performed yet; even a simple model will be valuable to understand which countermeasures are effective.
