Computer security incident response teams (CSIRTs) respond to a computer security incident when the need arises. Failure of these teams can have far-reaching effects for the economy and national security. CSIRTs often have to work on an ad hoc basis, in close cooperation with other teams, and in time constrained environments. It could be argued that under these working conditions CSIRTs would be likely to encounter problems. A needs assessment was done to see to which extent this argument holds true. We constructed an incident response needs model to assist in identifying areas that require improvement. We envisioned a model consisting of four assessment categories: Organization, Team, Individual and Instrumental. Central to this is the idea that both problems and needs can have an organizational, team, individual, or technical origin or a combination of these levels. To gather data we conducted a literature review. This resulted in a comprehensive list of challenges and needs that could hinder or improve, respectively, the performance of CSIRTs. Then, semi-structured in depth interviews were held with team coordinators and team members of five public and private sector Dutch CSIRTs to ground these findings in practice and to identify gaps between current and desired incident handling practices. This paper presents the findings of our needs assessment and ends with a discussion of potential solutions to problems with performance in incident response. https://doi.org/10.3389/fpsyg.2017.02179 LinkedIn: https://www.linkedin.com/in/rickvanderkleij1/
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In a multi-sensory environment, supported with embedded computer technology, the system can capture and interpret what the users are doing and assist or collaborate with the users in real-time. Such an environment should be aware of users intentions, tasks and feelings, and allow people to interact with the environment in a natural way: by moving, pointing and gesturing. In this paper we propose an architecture for such a smart environment consisting of three modules.
‘Creating the Difference’ is the theme of the 2014 edition of the Chi Sparks conference. It is also the challenge that the Human-Computer Interaction (HCI) community is facing today. HCI is a creative field where practitioners engage in design, production, and evaluation of interactions between people and digital technology. Creating excellent interfaces for people, they make a difference in media and systems that people are eager to use. Usability and user experience are fundamental for achieving this, as are abilities at the forefront of technology, but key to a successful difference is getting the right concepts, addressing genuine, intrinsic, human needs. Researchers and practitioners contribute to this area from theory as well as practice by sharing, discussing, and demonstrating new ideas and developments. This is how HCI creates a difference for society, for individuals, businesses, education, and organizations. The difference that an interactive product or service makes might lie in the concept of it but also in the making, the creation of details and the realisation. It is through powerful concepts and exceptional quality of realisation that innovation is truly achieved. At the Chi Sparks 2014 conference, researchers and practitioners in the HCI community convene to share and discuss their efforts on researching and developing methods, techniques, products, and services that enable people to have better interactions with systems and other people. The conference is hosted at The Hague University of Applied Sciences, and proudly built upon the previous conferences in Arnhem (2011) and Leiden (2009). Copyright van de individuele papers ligt bij de betreffende auteurs.
The focus of the research is 'Automated Analysis of Human Performance Data'. The three interconnected main components are (i)Human Performance (ii) Monitoring Human Performance and (iii) Automated Data Analysis . Human Performance is both the process and result of the person interacting with context to engage in tasks, whereas the performance range is determined by the interaction between the person and the context. Cheap and reliable wearable sensors allow for gathering large amounts of data, which is very useful for understanding, and possibly predicting, the performance of the user. Given the amount of data generated by such sensors, manual analysis becomes infeasible; tools should be devised for performing automated analysis looking for patterns, features, and anomalies. Such tools can help transform wearable sensors into reliable high resolution devices and help experts analyse wearable sensor data in the context of human performance, and use it for diagnosis and intervention purposes. Shyr and Spisic describe Automated Data Analysis as follows: Automated data analysis provides a systematic process of inspecting, cleaning, transforming, and modelling data with the goal of discovering useful information, suggesting conclusions and supporting decision making for further analysis. Their philosophy is to do the tedious part of the work automatically, and allow experts to focus on performing their research and applying their domain knowledge. However, automated data analysis means that the system has to teach itself to interpret interim results and do iterations. Knuth stated: Science is knowledge which we understand so well that we can teach it to a computer; and if we don't fully understand something, it is an art to deal with it.[Knuth, 1974]. The knowledge on Human Performance and its Monitoring is to be 'taught' to the system. To be able to construct automated analysis systems, an overview of the essential processes and components of these systems is needed.Knuth Since the notion of an algorithm or a computer program provides us with an extremely useful test for the depth of our knowledge about any given subject, the process of going from an art to a science means that we learn how to automate something.
Het aantal winkelbezoekers loopt in Europa al jaren terug, vooral in economisch zwakkere regio’s. Dit geldt in het bijzonder voor ouderen, waarvan de verwachting is dat ze in de toekomst fysieke winkels nog meer de rug zullen toekeren. Om de winkelervaring te verbeteren, investeren winkeliers steeds meer in opkomende digitale technologieën zoals apps, interactieve en digitale schermen, sociale robots en zelfscankassa’s. Deze instore technologieën slaan vooral bij jongere klanten aan, oudere klanten blijken door hun beperkingen (o.a. zien, horen, mobiliteit, informatieverwerking en digitale vaardigheden) nog steeds veel barrières te ervaren bij het bezoek aan winkels en het gebruik van instore technologieën. Dit is niet alleen nadelig voor winkeliers omdat ouderen een substantieel, stijgend, en koopkrachtig deel van de bevolking vertegenwoordigen dat relatief trouw is aan regionale winkelgebieden, maar het zet ook de inclusie van ouderen in Europa onder druk omdat winkelbezoek bijdraagt aan hun sociale welbevinden. Met dit onderzoeksproject onderzoekt het nieuwe consortium van twee hogescholen en drie buitenlandse universiteiten hoe instore technologieën ouderen in Europa kunnen helpen bij het wegnemen van barrières om tot een goede winkelervaring te komen. Het project brengt de onderzoeksprogramma’s van het lectorenplatform Retail Innovation Platform (Hogeschool van Amsterdam, Hogeschool Saxion), de Retail en Marketingtechnologie groep (University of Bristol), de human-computer interaction groep (University of Calabria), en de engaging co-design research group (Aalto University) samen. Het project sluit aan bij nationale en Europese initiatieven zoals de Kennis- en Innovatieagenda Sleuteltechnologieën 2024-2027, The DIGITAL Europe Programme en de Strategy for the rights of persons with disabilities 2021-2030. Door de relaties tussen ouderen, opkomende digitale technologie, en winkelgedrag over verschillende Europese regio’s te onderzoeken, sluit het project tevens aan bij Interreg Europa en het Europees Fonds voor Regionale Ontwikkeling.
Volgens onderzoek van McKinsey is marketing het vakgebied waar AI de meeste waarde toe gaat voegen, onder andere op het gebied van personalisatie. Hierdoor verandert het stakeholderveld waarin de marketeer personalisatie-algoritmen toepast significant, zo werkt hij steeds vaker samen met data scientists, AI-architecten en data engineers. Dit onderzoek richt zich op de vraag welke handvatten marketeers nodig hebben om tot een verantwoorde personalisatie-praktijk te komen.
