Als relatief nieuw begrip in de context van e-learning krijgt ‘mobile learning’ steeds meer aandacht, wat ten dele kan worden verklaard door de ontwikkeling en verspreiding van mobiele technologie. Als we de pleitbezorgers van ‘mobile learning’ moeten geloven, dan wordt deze vorm van leren belangrijker en is het denkbaar dat sommige leerprocessen in de toekomst volledig op die wijze vormgegeven zullen worden. Probleem is dat een eenduidige definitie van ‘mobile learning’ nog altijd ontbreekt, dat er meningsverschillen zijn over de technologie die tot het domein van ‘mobile learning’ behoort, en dat er betrekkelijk weinig resultaten zijn van succesvolle inzet van mobiele technologie in leerprocessen. Daarbij wordt onder succesvol verstaan dat het heeft bijgedragen aan de effectiviteit van het leren, en daarmee aan een beter leerresultaat en een efficiënter leerproces, waarbij onder het laatste verstaan wordt dat het maximale leereffect wordt bereikt met een beperkte inzet van mensen en middelen. Deze notitie beoogt enige duidelijkheid te scheppen in de definitiekwestie en in de visies op leren die een rol spelen bij ‘mobile learning’. Vanuit dat perspectief wordt vervolgens ingegaan op kenmerken van mobiele technologie en ontwikkelingen die daarin verwacht worden. Aansluitend wordt er dieper ingegaan op leerprocessen en de rol die mobiele technologie daarin zou kunnen vervullen, waarna de notitie wordt afgesloten met een kijkkader om de mogelijke inzet en betekenis van ‘mobile learning’ in onderwijssituaties te kunnen duiden en beoordelen.
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This study addresses the burgeoning global shortage of healthcare workers and the consequential overburdening of medical professionals, a challenge that is anticipated to intensify by 2030 [1]. It explores the adoption and perceptions of AI-powered mobile medical applications (MMAs) by physicians in the Netherlands, investigating whether doctors discuss or recommend these applications to patients and the frequency of their use in clinical practice. The research reveals a cautious but growing acceptance of MMAs among healthcare providers. Medical mobile applications, with a substantial part of IA-driven applications, are being recognized for their potential to alleviate workload. The findings suggest an emergent trust in AI-driven health technologies, underscored by recommendations from peers, yet tempered by concerns over data security and patient mental health, indicating a need for ongoing assessment and validation of these applications
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In a study commissioned by the Association of Dutch Municipalities (VNG), the applied research group European Impact has compiled the results from interviews executed by approximately 240 European Studies students at The Hague University of Applied Sciences. The purpose of this report is to compare and contrast the situation of intra-EU labor migrants (hereafter referred to as EU mobile citizens) in regard to registration, housing, and information flows in 12 different municipalities across the EU. Based on semi-structured interviews with municipal workers and individuals from employment agencies/companies from the selected municipalities, the picture that emerges is one of divergence. There are significant variations regarding the registration procedure and information flows for EU mobile citizens across the selected municipalities. For registration, differences include where the registration takes place, the amount of collaboration between municipalities and employment agencies/companies on registering EU mobile citizens, and the importance of addresses in the registration process. Regarding information flows across the selected municipalities, there are significant variations in the amount and type of information available to EU mobile citizens, the number of languages information is available in,as well as how the information is organized (i.e. in a centralized or decentralized way). Furthermore, while all the member states in which the selected municipalities are located provide information regarding registration on the Single Digital Gateway, not all provide information about renting housing. As for housing, the results revealed that most of the selected municipalities face issues with housing and that EU mobile citizens typically find housing either via their employers or personal network. Based on the results, a list of potential best practices and policy areas that could be improved was compiled. Furthermore, in order to have a stronger overview of policy developments in the field of EU mobile citizens among different municipalities, the VNG could consider hosting a Community of Practice with different municipalities across the EU as well as monitoring Interreg Europe projects focused on improving the situation of EU mobile citizens.
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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.
Automation is a key enabler for the required productivity improvement in the agrifood sector. After years of GPS-steering systems in tractors, mobile robots start to enter the market. Localization is one of the core functions for these robots to operate properly on fields and in orchards. GNSS (Global Navigation Satellite System) solutions like GPS provide cm-precision performance in open sky, but buildings, poles and biomaterial may reduce system performance. On top, certain areas do not provide a dependable grid communication link for the necessary GPS corrections and geopolitics lead to jamming activities. Other means for localization are required for robust operation. VSLAM (Visual Simultaneous Localization And Mapping) is a complex software approach that imitates the way we as humans learn to find our ways in unknown environments. VSLAM technology uses camera input to detect features in the environment, position itself in that 3D environment while concurrently creating a map that is stored and compared for future encounters, allowing the robot to recognize known environments and continue building a complete, consistent map of the environment covered by its movement. The technology also allows continuous updating of the map in environments that evolve over time, which is a specific advantage for agrifood use cases with growing crops and trees. The technology is however relatively new, as required computational power only recently became available in tolerable cost range and it is not well-explored for industrialized applications in fields and orchards. Orientate investigates the merits of open-source SLAM algorithms on fields - with Pixelfarming Robotics and RapAgra - and in an orchard - with Hillbird - preceded by simulations and initial application on a HAN test vehicle driving in different terrains. The project learnings will be captured in educational material elaborating on VSLAM technology and its application potential in agrifood.
Lack of physical activity in urban contexts is an increasing health risk in The Netherlands and Brazil. Exercise applications (apps) are seen as potential ways of increasing physical activity. However, physical activity apps in app stores commonly lack a scientific base. Consequently, it remains unknown what specific content messages should contain and how messages can be personalized to the individual. Moreover, it is unknown how their effects depend on the physical urban environment in which people live and on personal characteristics and attitudes. The current project aims to get insight in how mobile personalized technology can motivate urban residents to become physically active. More specifically, we aim to gain insight into the effectiveness of elements within an exercise app (motivational feedback, goal setting, individualized messages, gaming elements (gamification) for making people more physically active, and how the effectiveness depends on characteristics of the individual and the urban setting. This results in a flexible exercise app for inactive citizens based on theories in data mining, machine learning, exercise psychology, behavioral change and gamification. The sensors on the mobile phone, together with sensors (beacons) in public spaces, combined with sociodemographic and land use information will generate a massive amount of data. The project involves analysis in two ways. First, a unique feature of our project is that we apply machine learning/data mining techniques to optimize the app specification for each individual in a dynamic and iterative research design (Sequential Multiple Assignment Randomised Trial (SMART)), by testing the effectiveness of specific messages given personal and urban characteristics. Second, the implementation of the app in Sao Paolo and Amsterdam will provide us with (big) data on use of functionalities, physical activity, motivation etc. allowing us to investigate in detail the effects of personalized technology on lifestyle in different geographical and cultural contexts.
