What Would You Ask to Your Home if It Were Intelligent? Exploring User Expectations about Next-Generation Homes

Ambient Intelligence (AmI) research is giving birth to a multitude of futuristic home scenarios and applications; however a clear discrepancy between current installations and research-level designs can be easily noticed. Whether this gap is due to the natural distance between research and engineered applications or to mismatching of needs and solutions remains to be understood. This paper discusses the results of a survey about user expectations with respect to intelligent homes. Starting from a very simple and open question about what users would ask to their intelligent homes, we derived user perceptions about what intelligent homes can do, and we analyzed to what extent current research solutions, as well as commercially available systems, address these emerging needs. Interestingly, most user concerns about smart homes involve comfort and household tasks and most of them can be currently addressed by existing commercial systems, or by suitable combinations of them. A clear trend emerges from the poll findings: the technical gap between user expectations and current solutions is actually narrower and easier to bridge than it may appear, but users perceive this gap as wide and limiting, thus requiring the AmI community to establish a more effective communication with final users, with an increased attention to real-world deploymen

Spatial analysis, decision support systems (DSS) and land use design: the case-study of antique viability system in San Martino valley (Lombardy, Italy)

This paper concerns the development of a Decision Support System (DSS), which is a system able to support temporal and spatial choices about land use design, in order to project and manage the antique viability system in San Martino valley (located in Lombardy, Italy) The main purpose is providing to a project manager necessary information to help him to understand problems (in particular concerning the spatial system of viability), therefore assists him to analyze the question from different points of view. This process needs a particular informative architecture, based on a complex and relational structured system (DSS) able to produce response for the whole decision process. The DSS is interfaced with a GIS in order to manage cartography and alphanumeric files with geo-referenced data. It works on information which are supposed to be indispensable for the planners of the San Martino valley.

Pilot-testing a Cancer 101 Education Curriculum with the Fairbanks Native Association’s Women & Children’s Center for Inner Healing

Cancer is the leading cause of death among Alaska Native people Nevertheless, due to improved detection awareness about cancer prevention, early screening and advances in treatment survival rates are rising

The effects of entrepreneurship education

Entrepreneurship education ranks high on policy agendas in Europe and the US, but little research is available to assess its impact. To help close this gap we investigate whether entrepreneurship education a?ects intentions to be entrepreneurial uniformly or whether it leads to greater sorting of students. The latter can reduce the average intention to be entrepreneurial and yet be socially beneficial. This paper provides a model of learning in which entrepreneurship education generates signals to students. Drawing on the signals, students evaluate their aptitude for entrepreneurial tasks. The model is tested using data from a compulsory entrepreneurship course. Using ex ante and ex post survey responses from students, we find that intentions to found decline somewhat although the course has significant positive e?ects on students’ self-assessed entrepreneurial skills. The empirical analysis supports the hypothesis that students receive informative signals and learn about their entrepreneurial aptitude. We outline implications for educators and public policy

Adapting robot task planning to user preferences: an assistive shoe dressing example

The final publication is available at link.springer.comHealthcare robots will be the next big advance in humans’ domestic welfare, with robots able to assist elderly people and users with disabilities. However, each user has his/her own preferences, needs and abilities. Therefore, robotic assistants will need to adapt to them, behaving accordingly. Towards this goal, we propose a method to perform behavior adaptation to the user preferences, using symbolic task planning. A user model is built from the user’s answers to simple questions with a fuzzy inference system, and it is then integrated into the planning domain. We describe an adaptation method based on both the user satisfaction and the execution outcome, depending on which penalizations are applied to the planner’s rules. We demonstrate the application of the adaptation method in a simple shoe-fitting scenario, with experiments performed in a simulated user environment. The results show quick behavior adaptation, even when the user behavior changes, as well as robustness to wrong inference of the initial user model. Finally, some insights in a non-simulated world shoe-fitting setup are also provided.Peer ReviewedPostprint (author's final draft