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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Maritime Spatial Planning (MSP) is a politically guided and stakeholder-driven process involving a range of actors (i.e., planners, stakeholders, scientists, and citizens). Theories of boundary objects offer a lens to understand how actors, in the context of decision and policy-making in organizations, can coordinate without consensus. This seems particularly relevant when institutions and communities are relatively young, and the body of knowledge is fragmented and fluid, such as in the case of MSP. A key question is whether, and how boundary objects can be intentionally designed and used to facilitate social and policy learning in such communities. In this research, the focus is on the use of the MSP Challenge serious games as a boundary object to facilitate learning in ‘Communities of Practice’ (CoP) around MSP. Data were collected through questionnaires of 62 MSP Challenge workshops between 2016 and 2020 with more than 1100 participants. Additionally, 33 interviews with key stakeholders were conducted. The findings show that the MSP Challenge is widely used for various goals and in various settings and that they are interpreted differently by different users. The success of the MSP Challenge relies on the boundary space in which it is implemented, taking into account discrepancies in learning due to variations in the backgrounds and attitudes of the participants towards the object, the activity, and the setting in which it is deployed.
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