There is emerging evidence that the performance of risk assessment instruments is weaker when used for clinical decision‐making than for research purposes. For instance, research has found lower agreement between evaluators when the risk assessments are conducted during routine practice. We examined the field interrater reliability of the Short‐Term Assessment of Risk and Treatability: Adolescent Version (START:AV). Clinicians in a Dutch secure youth care facility completed START:AV assessments as part of the treatment routine. Consistent with previous literature, interrater reliability of the items and total scores was lower than previously reported in non‐field studies. Nevertheless, moderate to good interrater reliability was found for final risk judgments on most adverse outcomes. Field studies provide insights into the actual performance of structured risk assessment in real‐world settings, exposing factors that affect reliability. This information is relevant for those who wish to implement structured risk assessment with a level of reliability that is defensible considering the high stakes.
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Author-supplied abstract: Developing large-scale complex systems in student projects is not common, due to various constraints like available time, student team sizes, or maximal complexity. However, we succeeded to design a project that was of high complexity and comparable to real world projects. The execution of the project and the results were both successful in terms of quality, scope, and student/teacher satisfaction. In this experience report we describe how we combined a variety of principles and properties in the project design and how these have contributed to the success of the project. This might help other educators with setting up student projects of comparable complexity which are similar to real world projects.
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In the rapidly evolving field of Machine Learning , selecting the most appropriate model for a given dataset is crucial. Understanding the characteristics of a dataset can significantly influence the outcomes of predictive modeling efforts, making the study of the properties of the dataset an essential component of data science. This study investigates the possibilities of using simulated human data for personalized applications, specifically for testing clustering approaches. In particular, the study focuses on the relationship between dataset characteristics and the selection of the optimal classification model for clusters of datasets. The results of this study provide critical insights for researchers and practitioners in machine learning, emphasizing the importance of dataset characteristics and variability in building and selecting robust models for diverse data conditions. The use of human simulation data provide valuable insights but requires further refinement to capture the full variability of real-world conditions.
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In this post I give an overview of the theory, tools, frameworks and best practices I have found until now around the testing (and debugging) of machine learning applications. I will start by giving an overview of the specificities of testing machine learning applications.
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This article delves into the acceptance of autonomous driving within society and its implications for the automotive insurance sector. The research encompasses two different studies conducted with meticulous analysis. The first study involves over 600 participants involved with the automotive industry who have not yet had the opportunity to experience autonomous driving technology. It primarily centers on the adaptation of insurance products to align with the imminent implementation of this technology. The second study is directed at individuals who have had the opportunity to test an autonomous driving platform first-hand. Specifically, it examines users’ experiences after conducting test drives on public roads using an autonomous research platform jointly developed by MAPFRE, Universidad Carlos III de Madrid, and Universidad Politécnica de Madrid. The study conducted demonstrates that the user acceptance of autonomous driving technology significantly increases after firsthand experience with a real autonomous car. This finding underscores the importance of bringing autonomous driving technology closer to end-users in order to improve societal perception. Furthermore, the results provide valuable insights for industry stakeholders seeking to navigate the market as autonomous driving technology slowly becomes an integral part of commercial vehicles. The findings reveal that a substantial majority (96% of the surveyed individuals) believe that autonomous vehicles will still require insurance. Additionally, 90% of respondents express the opinion that policies for autonomous vehicles should be as affordable or even cheaper than those for traditional vehicles. This suggests that people may not be fully aware of the significant costs associated with the systems enabling autonomous driving when considering their insurance needs, which puts the spotlight back on the importance of bringing this technology closer to the general public.
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Technology designed to sense behavior, often neglects to directly incorporate subjective input from (elderly) users. This paper presents experiences in deploying technology that considers the elderly user and their subjective input as a way to enrich sensor data systems and empower the user. For this purpose, the paper draws on: (1) Observations of shortcomings in terms of capturing objective data from sensors as experienced in long-term deploymentt in the homes of older adults; (2) The design and evaluation of a wide range of applications especially designed to enable older adults to give subjective input on how they are doing, including an interactive television quiz, a talking picture frame and a tangible mood board, and (3) The development and field study of one application, the ‘Mood button’ in particular, that was tested in real-world sensing settings to work with a commercial sensing system. In doing this, this work aims to contribute towards successful sensing deployments and tools that give more control to the (elderly) end-user.
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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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Learning and acting on social conventions is problematic for low-literates and non-natives, causing problems with societal participation and citizenship. Using the Situated Cognitive Engineering method, requirements for the design of social conventions learning software are derived from demographic information, adult learning frameworks and ICT learning principles. Evaluating a sample of existing Dutch social conventions learning applications on these requirements shows that none of them meet all posed criteria. Finally, Virtual Reality is suggested as a possible future technology improvement.
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BACKGROUND: Rapid technological development has been opening new possibilities for children with disabilities. In particular, robots can enable and create new opportunities in therapy, rehabilitation, education, or leisure. OBJECTIVE: The aim of this article is to share experiences, challenges and learned lessons by the authors, all of them with experience conducting research in the field of robotics for children with disabilities, and to propose future directions for research and development. METHODS: The article is the result of several consensus meetings to establish future research priorities in this field. CONCLUSIONS: This article outlines a research agenda for the future of robotics in childcare and supports the establishment of R4C – Robots for Children, a network of experts aimed at sharing ideas, promoting innovative research, and developing good practices on the use of robots for children with disabilities. RESULTS: Robots have a huge potential to support children with disabilities: they can play the role of a play buddy, of a mediator when interacting with other children or adults, they can promote social interaction, and transfer children from the role of a spectator of the surrounding world to the role of an active participant. To fulfill their potential, robots have to be “smart”, stable and reliable, easy to use and program, and give the just-right amount of support adapted to the needs of the child. Interdisciplinary collaboration combined with user centered design is necessary to make robotic applications successful. Furthermore, real-life contexts to test and implement robotic interventions are essential to refine them according to real needs.
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This study evaluates the maximum theoretical exposure to radiofrequency (RF) electromag- netic fields (EMFs) from a Fifth-generation (5G) New Radio (NR) base station (BS) while using four commonly used mobile applications: YouTube for video streaming, WhatsApp for voice calls, Instagram for posting pictures and videos, and running a Video game. Three factors that might affect exposure, i.e., distance of the measurement positions from the BS, measurement time, and induced traffic, were examined. Exposure was assessed through both instantaneous and time-averaged extrapolated field strengths using the Maximum Power Extrapolation (MPE) method. The former was calculated for every measured SS-RSRP (Secondary Synchronization Reference Signal Received Power) power sample obtained with a sampling resolution of 1 second, whereas the latter was obtained using a 1-min moving average applied on the applications’ instantaneous extrapolated field strengths datasets. Regarding distance, two measurement positions (MPs) were selected: MP1 at 56 meters and MP2 at 170 meters. Next, considering the measurement time, all mobile application tests were initially set to run for 30 minutes at both MPs, whereas the video streaming test (YouTube) was run for an additional 150 minutes to investigate the temporal evolution of field strengths. Considering the traffic, throughput data vs. both instantaneous and time-averaged extrapolated field strengths were observed for all four mobile applications. In addition, at MP1, a 30-minute test without a User Equipment (UE) device was conducted to analyze exposure levels in the absence of induced traffic. The findings indicated that the estimated field strengths for mobile applications varied. It was observed that distance and time had a more significant impact than the volume of data traffic generated (throughput). Notably, the exposure levels in all tests were considerably lower than the public exposure thresholds set by the ICNIRP guidelines.INDEX TERMS 5G NR, C-band, human exposure assessment, mobile applications, traffic data, maximum extrapolation method, RF-EMF.
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