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
Information and communications technologies (ICTs) in human services are on the rise and raise concerns about their place and impact on the daily activities of professionals and clients. This article describes a study in which a social mobile application was developed for job coaches and employees and implemented in a pilot phase. The aim of the mobile application was to provide a better communication between employees and their job coaches and to provide more up-to-date information about the organization. The application consisted of a personal web environment and app with vacancies, personal news, events, tips, and promotions. A qualitative methodology was used in the form of focus groups and in-depth interviews. The results of this study show that the participants are partly positive about the social mobile application. It can be concluded that the use of mobile technologies can be beneficial in a range of human services practice settings for both professionals and clients and, therefore, requires more attention from the academic field to focus on this relatively new but promising theme.
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.
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.
Public safety is under enormous pressure. Demonstrations regularly result in riots and VIPs are often threatened even at their homes ! Criminal graffiti-gangs are threatening security professionals and costing the Dutch railways (NS), causing a loss of 10 M€ yearly. The safety incidents often escalate quickly, therefore, they require a very quick and correct scaling up of the security professionals. To do so, it is necessary for the security professionals to get very quick and accurate overview of the evolving situation using Mobile Drone intervention unit for quick response (Mobi Dick). The successfully completed project The Beast (9/11) has delivered a universal docking station with an automatic security drone. The drone takes off from a permanently installed docking station. Nest Fly emerged as a startup from this RAAK project, and it has already developed the prototype further to a first product. Based on extensive interaction with security professionals, it has been concluded that a permanently installed docking station is not suitable for all emergency cases. Therefore, a mobile, car-roof top mounted, docking station with a ready-for-take-off drone is required for the more severe and quickly escalating incidents. These situations require a drone taking off from the car-roof top mounted docking station while the vehicles continue to drive towards the incident. In this RAAK KIEM, a feasibility study will be executed by developing a car-roof top docking station. The concept will functionally be designed within the project (task 1). The two required subsystems car roof docking station (task 2) and dynamic take-off & landing (task 3) will technically be developed and integrated (task 4). The outcome of the experiments in this task will show the feasibly of the idea. Task 5 will ensure the results are disseminated in new cooperation’s, publications, and educational products.
The demand for mobile agents in industrial environments to perform various tasks is growing tremendously in recent years. However, changing environments, security considerations and robustness against failure are major persistent challenges autonomous agents have to face when operating alongside other mobile agents. Currently, such problems remain largely unsolved. Collaborative multi-platform Cyber- Physical-Systems (CPSs) in which different agents flexibly contribute with their relative equipment and capabilities forming a symbiotic network solving multiple objectives simultaneously are highly desirable. Our proposed SMART-AGENTS platform will enable flexibility and modularity providing multi-objective solutions, demonstrated in two industrial domains: logistics (cycle-counting in warehouses) and agriculture (pest and disease identification in greenhouses). Aerial vehicles are limited in their computational power due to weight limitations but offer large mobility to provide access to otherwise unreachable places and an “eagle eye” to inform about terrain, obstacles by taking pictures and videos. Specialized autonomous agents carrying optical sensors will enable disease classification and product recognition improving green- and warehouse productivity. Newly developed micro-electromechanical systems (MEMS) sensor arrays will create 3D flow-based images of surroundings even in dark and hazy conditions contributing to the multi-sensor system, including cameras, wireless signatures and magnetic field information shared among the symbiotic fleet. Integration of mobile systems, such as smart phones, which are not explicitly controlled, will provide valuable information about human as well as equipment movement in the environment by generating data from relative positioning sensors, such as wireless and magnetic signatures. Newly developed algorithms will enable robust autonomous navigation and control of the fleet in dynamic environments incorporating the multi-sensor data generated by the variety of mobile actors. The proposed SMART-AGENTS platform will use real-time 5G communication and edge computing providing new organizational structures to cope with scalability and integration of multiple devices/agents. It will enable a symbiosis of the complementary CPSs using a combination of equipment yielding efficiency and versatility of operation.
