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.
Insomnia has a negative impact on mental health and is a potential risk factor for impulsive and problematic behavior. This multicenter, cross-sectional study investigated the prevalence of insomnia and underlying and maintaining factors in a group of forensic psychiatric inpatients (N = 281). Insomnia severity, subjective sleep quality, sleep hygiene and erroneous cognitions about sleep were measured with the Insomnia Severity Index, Pittsburgh Sleep Quality Index, Sleep Hygiene questionnaire and Dysfunctional Beliefs and Attitudes about Sleep, respectively. Impulsivity was derived from risk assessment instruments routinely completed by trained professionals. Almost half of the patients (48.7%) appeared to suffer from insomnia. Particularly moderate-severe insomnia (26.7%) was significantly associated with worse sleep quality, poorer sleep hygiene, stronger endorsement of dysfunctional sleep cognitions and higher impulsivity scores. It can be concluded that insomnia is rather common in forensic psychiatric patients. Insomnia appears related to various sleep hygiene behaviors and sleep-related cognitions, and probably also to diverse situational and environmental factors as well as a lack of autonomy. Cognitive behavioral therapy for insomnia, with some adjustments specific for this population, may be an effective intervention, ameliorating sleep problems and possibly also emotional and behavioral dysregulation.
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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.
Net als in het boek van Dan Brown, de ‘Da Vinci Code’, is de politie altijd op zoek naar aanwijzingen die naar de dader kunnen leiden. Waar in het boek allerlei cryptische symbolen en codes verborgen achtergelaten worden als aanwijzingen, zal in de praktijk bij forensisch onderzoek van de politie of het NFI, sporen gevonden moeten worden op een plaats delict. Het onderwerp van dit projectvoorstel, DaVinciQD, ligt op het dateren van een van dat soort sporen, namelijk vingersporen. Er wordt standaard in forensisch onderzoek naar vingersporen gezocht en indien gedetecteerd en veiliggesteld, worden zij ter plaatse of in het forensisch lab onderzocht en vervolgens vergeleken met een grote databank. Relevant is het om te bepalen of een vingerspoor afkomstig is van de dader en dus relevant voor het forensisch onderzoek. Om dit te bepalen is het niet alleen noodzakelijk om een vingerafdruk zichtbaar te maken en te koppelen aan een persoon, maar ook om deze te kunnen relateren aan het tijdsframe van het gepleegde misdrijf. Daarom de vraag om een methode te ontwikkelen die in staat is om vingerafdrukken te dateren. Het bepalen van het moment van achterlaten van een vingerspoor is cruciaal enerzijds om te bepalen of deze relevant is voor het lopende onderzoek, maar ook in de context van bewijsvoering en een eventuele veroordeling van een dader. Een consortium bestaande uit de onderzoeksgroepen Advanced Forensic Technology en NanoBio van Saxion, het Nederlands Forensisch Instituut, de Nationale Politie, de Universiteit Twente en enkele private bedrijven, zal een methode ontwikkelen om met behulp van quantum dots de datering van vingersporen mogelijk maken. De methode zal niet alleen in het lab, maar ook in de praktijk van de forensisch onderzoeker getest en gevalideerd worden.
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.
