This article reflects on the workshop Bridging the KAP-gap in global education, which was part of the DEEEP-conference Global Justice through Global Citizenship. The objective of the workshop was, to learn about strategies to bridge the KAP (Knowledge, Attitude, Practice) -gap and to gain ideas how to apply these strategies to participants’ own practices. The workshop turned into a slightly different direction and raised some fundamental questions: What could one expect of global education? Which others factors influence learners’ behaviour? To which manner does global education aim to change behaviour? Should global education aim to change behaviour? This article summarizes the outcomes of an evaluation which was done amongst alumni-students of the minor programme Global Development Issues of Fontys University of Applied Sciences and the main issues that were discussed during the workshop, also based on the integrated model of behavioural prediction. The article ends with some lessons learned, especially for the curriculumowners of the minor programme, who organised this workshop.
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Increasingly aware of the importance of active lifestyles, many people intend to exercise more. One of the main challenges is to translate exercise intentions into actual exercise behaviour, the so-called intention-behaviour gap. To investigate barriers and enablers that affect this gap, we conducted a 7-day diary study with 16 women. Participants indicated what their exercise intentions and behaviour were per day, and whether and why they changed retrospectively during the day. Through the diary study, we gain insights into (i) the intention-behaviour interplay, and (ii) the experienced barriers and enablers that influence this interplay throughout the day. Based on the findings, we contribute new implications for design in supporting people translating their intentions into exercise behaviour. We propose three design concepts to illustrate underlying design opportunities. The focus is on positively influencing the interplay of enablers and barriers of exercising and how these can be addressed through design
This study provides a comprehensive analysis of the AI-related skills and roles needed to bridge the AI skills gap in Europe. Using a mixed-method research approach, this study investigated the most in-demand AI expertise areas and roles by surveying 409 organizations in Europe, analyzing 2,563 AI-related job advertisements, and conducting 24 focus group sessions with 145 industry and policy experts. The findings underscore the importance of both general technical skills in AI related to big data, machine learning and deep learning, cyber and data security, large language models as well as AI soft skills such as problemsolving and effective communication. This study sets the foundation for future research directions, emphasizing the importance of upskilling initiatives and the evolving nature of AI skills demand, contributing to an EU-wide strategy for future AI skills development.
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Ten gevolge van de klimaatverandering Nederland bedreigt. De Verenigde Naties benoemt ‘17 Gobal Goals for a Sustainable Development’ nader gespecificeerd. Goal 13:” versterk de veerkracht en het aanpassingsvermogen aan klimaatgerelateerde gevaren en natuurrampen”. Deze klimaatverandering vraagt om een continue inzicht in de waterafvoercapaciteit van Nederlandse water-infrastructuur. Autonome vaartuigen maken een continue bemeting en realtime informatie van de vaarwegen mogelijk op basis waarvan waar snel actie ondernomen kan worden. Diverse partijen zowel publiek als privaat hebben de wens om continue en autonoom te varen en zijn afzonderlijk hiermee bezig zoals onder andere Rijkswaterstaat, Saeport Groningen en Provincie Overijssel . Het lectoraat mechatronica, dat succesvol onderzoek doet naar ‘autonome systemen in ongestructureerde omgevingen’ heeft veel kennis en ervaring op het gebied van grond (2D navigatie) en lucht robots (3D navigatie). Deze ontwikkelde technologieën zijn potentieel zeer geschikt voor navigatie op het water (2D, 2.5D) en onderwater (3D). Tijdens de vraaginventarisatie bleek er reeds veel interesse van partijen om kennis te delen en samen door te ontwikkelen. Er zijn semi-autonome vaartuigen beschikbaar hiervoor, maar bij de partijen ontbrak een totaal overzicht van de huidige stand van der technologie. Daarom wil het lectoraat Mechatronica samen met Marinminds, Aquatic Drones en DronExpert een onderzoek uitvoeren naar de ‘State of the Art’ betreft autonoom varen. In dit project zal dit onderzoek worden uitgevoerd door specificatie van de gewenste functionele bouwblokken (WP1), een state-of-the art van beschikbare technische oplossingen (WP2), een Gap-analysis tussen deze beide (WP3), verkennende experimenten hiernaar met behulp van een demonstrator (WP4) en een nieuwe specifiek gemaakte projectaanvraag (WP5). Dit cross-over project van de topsector HTSM/SmartIndustry met de topsector Water & Maritiem versterkt al direct de kennispositie van alle betrokken partijen, waardoor deze consortia sneller de vaarwegen klimaat-adaptief kunnen maken, zodat daarmee de Nederlandse (water) veiligheid beter wordt geborgd.
The maritime transport industry is facing a series of challenges due to the phasing out of fossil fuels and the challenges from decarbonization. The proposal of proper alternatives is not a straightforward process. While the current generation of ship design software offers results, there is a clear missed potential in new software technologies like machine learning and data science. This leads to the question: how can we use modern computational technologies like data analysis and machine learning to enhance the ship design process, considering the tools from the wider industry and the industry’s readiness to embrace new technologies and solutions? The obbjective of this PD project is to bridge the critical gap between the maritime industry's pressing need for innovative solutions for a more agile Ship Design Process; and the current limitations in software tools and methodologies available via the implementation into Ship Design specific software of the new generation of computational technologies available, as big data science and machine learning.
Today, embedded devices such as banking/transportation cards, car keys, and mobile phones use cryptographic techniques to protect personal information and communication. Such devices are increasingly becoming the targets of attacks trying to capture the underlying secret information, e.g., cryptographic keys. Attacks not targeting the cryptographic algorithm but its implementation are especially devastating and the best-known examples are so-called side-channel and fault injection attacks. Such attacks, often jointly coined as physical (implementation) attacks, are difficult to preclude and if the key (or other data) is recovered the device is useless. To mitigate such attacks, security evaluators use the same techniques as attackers and look for possible weaknesses in order to “fix” them before deployment. Unfortunately, the attackers’ resourcefulness on the one hand and usually a short amount of time the security evaluators have (and human errors factor) on the other hand, makes this not a fair race. Consequently, researchers are looking into possible ways of making security evaluations more reliable and faster. To that end, machine learning techniques showed to be a viable candidate although the challenge is far from solved. Our project aims at the development of automatic frameworks able to assess various potential side-channel and fault injection threats coming from diverse sources. Such systems will enable security evaluators, and above all companies producing chips for security applications, an option to find the potential weaknesses early and to assess the trade-off between making the product more secure versus making the product more implementation-friendly. To this end, we plan to use machine learning techniques coupled with novel techniques not explored before for side-channel and fault analysis. In addition, we will design new techniques specially tailored to improve the performance of this evaluation process. Our research fills the gap between what is known in academia on physical attacks and what is needed in the industry to prevent such attacks. In the end, once our frameworks become operational, they could be also a useful tool for mitigating other types of threats like ransomware or rootkits.
