Crime script analysis as a methodology to analyse criminal processes is underdeveloped. This is apparent from the various approaches in which scholars apply crime scripting and present their cybercrime scripts. The plethora of scripting methods raise significant concerns about the reliability and validity of these scripting studies. In this methodological paper, we demonstrate how object-oriented modelling (OOM) could address some of the currently identified methodological issues, thereby refining crime script analysis. More specifically, we suggest to visualise crime scripts using static and dynamic modelling with the Unified Modelling Language (UML) to harmonise cybercrime scripts without compromising their depth. Static models visualise objects in a system or process, their attributes and their relationships. Dynamic models visualise actions and interactions during a process. Creating these models in addition to the typical textual narrative could aid analysts to more systematically consider, organise and relate key aspects of crime scripts. In turn, this approach might, amongst others, facilitate alternative ways of identifying intervention measures, theorising about offender decision-making, and an improved shared understanding of the crime phenomenon analysed. We illustrate the application of these models with a phishing script.
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This article deals with automatic object recognition. The goal is that in a certain grey-level image, possibly containing many objects, a certain object can be recognized and localized, based upon its shape. The assumption is that this shape has no special characteristics on which a dedicated recognition algorithm can be based (e.g. if we know that the object is circular, we could use a Hough transform or if we know that it is the only object with grey level 90, we can simply use thresholding). Our starting point is an object with a random shape. The image in which the object is searched is called the Search Image. A well known technique for this is Template Matching, which is described first.
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This paper is a report of a review conducted to provide an overview of the evidence in the literature on task-oriented training of stroke survivors and its relevance in daily nursing practice. Background: Stroke is the second leading cause of death and one of the leading causes of adult disability in the Western world. The use of neurodevelopmental treatment in the daily nursing care of stroke survivors does not improve clinical outcomes. Nurses are therefore exploring other forms of rehabilitation intervention, including task-oriented rehabilitation. Despite the growing number of studies showing evidence on task-oriented interventions, recommendations for daily nursing practice are lacking. A range of databases was searched to identify papers addressing taskoriented training in stroke rehabilitation, including Medline, CINAHL, Embase and the Cochrane Library of systematic reviews. Papers published in English between January 1996 and September 2007 were included. There were 42 papers in the final dataset, including nine systematic reviews. Review methods: The selected randomized controlled trials and systematic reviews were assessed for quality. Important characteristics and outcomes were extracted and summarized. Results: Studies of task-related training showed benefits for functional outcome compared with traditional therapies. Active use of task-oriented training with stroke survivors will lead to improvements in functional outcomes and overall healthrelated quality of life. Conclusion. Generally, task-oriented rehabilitation proved to be more effective. Many interventions are feasible for nurses and can be performed in a ward or at home. Nurses can and should play an important role in creating opportunities to practise meaningful functional tasks outside of regular therapy sessions.
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