This study focuses on SME networks of design and high-tech companies in Southeast Netherland. By highlighting the personal networks of members across design and high-tech industries, the study attempts to identify the main brokers in this dynamic environment. In addition, we investigate whether specific characteristics are associated with these brokers. The main contribution of the paper lies in the fact that, in contrast to most other work, it is quantitative and that it focuses on brokers identified in an actual network (based on both suppliers and users of the knowledge infrastructure). Studying the phenomenon of brokerage provides us with clear insights into the concept of brokerage regarding SME networks in different fields. In particular we highlight how third parties contribute to the transfer and development of knowledge. Empirical results show, among others that the most influential brokers are found in the nonprofit and science sector and have a long track record in their branch.
This article examines the network structure, criminal cooperation, and external interactions of cybercriminal networks. Its contribution is empirical and inductive. The core of this study involved carrying out 10 case analyses on closed cybercrime investigations – all with financial motivations on the part of the offenders - in the UK and beyond. Each analysis involved investigator interview and access to unpublished law enforcement files. The comparison of these cases resulted in a wide range of findings on these cybercriminal networks, including: a common division between the scam/attack components and the money components; the presence of offline/local elements; a broad, and sometimes blurred, spectrum of cybercriminal behaviour and organisation. An overarching theme across the cases that we observe is that cybercriminal business models are relatively stable.
Presentation by Rutger Leukfeldt on Financially motivated cybercriminal networks, during workshop on Cybercrime Offenders. Cybercrime perpetrators are as diverse and complex as the cybercrime that they commit. For example, they come from different backgrounds and have different (egotistical, technical, monetary, ideological, political, professional, vengeful, sexual or other) motivations. They may or may not be professional criminals, and individuals or part of organised groups or networks (example of Advanced Persistent Threats). Some may commit crime on their own account or make their services available to others, and some may be supported by or be state actors. A better understanding of the types of perpetrators and their motivations and techniques can be instrumental for the prevention of cybercrime and for a more effective criminal justice response. The aim of this workshop is to contribute to such a better understanding and to initiate steps towards a typology of offenders.
The AR in Staged Entertainment project focuses on utilizing immersive technologies to strengthen performances and create resiliency in live events. In this project The Experiencelab at BUas explores this by comparing live as well as pre-recorded events that utilize Augmented Reality technology to provide an added layer to the experience of the user. Experiences will be measured among others through observational measurements using biometrics. This projects runs in the Experience lab of BUas with partners The Effenaar and 4DR Studio and is connected to the networks and goals related to Chronosphere, Digireal and Makerspace. Project is powered by Fieldlab Events (PPS / ClickNL)..
Digital innovations in the field of immersive Augmented Reality (AR) can be a solution to offer adults who are mentally, physically or financially unable to attend sporting events such as premier league football a stadium and match experience. This allows them to continue to connect with their social networks. In the intended project, AR content will be further developed with the aim of evoking the stadium experience of home matches as much as possible. The extent to which AR enriches the experience is then tested in an experiment, in which the experience of a football match with and without AR enrichment is measured in a stadium setting and in a home setting. The experience is measured with physiological signals. In addition, a subjective experience measure is also being developed and benchmarked (the experience impact score). Societal issueInclusion and health: The joint experience of (top) sports competitions forms a platform for vulnerable adults, with a limited social capital, to build up and maintain the social networks that are so necessary for them. AR to fight against social isolation and loneliness.
Production processes can be made ‘smarter’ by exploiting the data streams that are generated by the machines that are used in production. In particular these data streams can be mined to build a model of the production process as it was really executed – as opposed to how it was envisioned. This model can subsequently be analyzed and stress-tested to explore possible causes of production prob-lems and to analyze what-if scenarios, without disrupting the production process itself. It has been shown that such models can successfully be used to diagnose possible causes of production problems, including scrap products and machine defects. Ideally, they can even be used to model and analyze production processes that have not been implemented yet, based on data from existing production pro-cesses and techniques from artificial intelligence that can predict how the new process is likely to be-have in practice in terms of data that its machines generate. This is especially important in mass cus-tomization processes, where the process to create each product may be unique, and can only feasibly be tested using model- and data-driven techniques like the one proposed in this project. Against this background, the goal of this project is to develop a method and toolkit for mining, mod-elling and analyzing production processes, using the time series data that is generated by machines, to: (i) analyze the performance of an existing production process; (ii) diagnose causes of production prob-lems; and (iii) certify that a new – not yet implemented – production process leads to high-quality products. The method is developed by researching and combining techniques from the area of Artificial Intelli-gence with techniques from Operations Research. In particular, it uses: process mining to relate time series data to production processes; queueing networks to determine likely paths through the produc-tion processes and detect anomalies that may be the cause of production problems; and generative adversarial networks to generate likely future production scenarios and sample scenarios of production problems for diagnostic purposes. The techniques will be evaluated and adapted in implementations at the partners from industry, using a design science approach. In particular, implementations of the method are made for: explaining production problems; explaining machine defects; and certifying the correct operation of new production processes.
