When analysing the legitimacy of the welfare state, perceptions of the overuse and underuse of welfare are of great importance. Previous literature suggests that many people perceive overuse (misuse or fraud), and there is evidence that people also perceive underuse (non-take-up) of welfare benefits. Perceptions of overuse have therefore been called ‘the Achilles’ heel of welfare state legitimacy'. We analyse data from the European Social Survey for 25 countries and investigate the occurrence and the individual and contextual determinants of overuse and underuse perceptions. We find that both overuse and underuse perceptions are prevalent in all European countries. However, whereas overuse perceptions are more related to ideology, collective images of welfare recipients and selective welfare regimes, underuse perceptions are more shaped by self-interest and the levels of unemployment and social spending in a country. Instead of one Achilles' heel, welfare state legitimacy seems to have two weak spots.Key words: Benefit abuse, European Social Survey, non-take-up, welfare attitudes, welfare states
From the article: Using Roger Crisp’s arguments for well-being as the ultimate source of moral reasoning, this paper argues that there are no ultimate, non-derivative reasons to program robots with moral concepts such as moral obligation, morally wrong or morally right. Although these moral concepts should not be used to program robots, they are not to be abandoned by humans since there are still reasons to keep using them, namely: as an assessment of the agent, to take a stand or to motivate and reinforce behaviour. Because robots are completely rational agents they don’t need these additional motivations, they can suffice with a concept of what promotes well-being. How a robot knows which action promotes well-being to the greatest degree is still up for debate, but a combination of top-down and bottom-up approaches seem to be the best way. The final publication is available at IOS Press through http://dx.doi.org/10.3233/978-1-61499-708-5-184
An illustrative non-technical review was published on Towards Data Science regarding our recent Journal paper “Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning”.While new technologies have changed almost every aspect of our lives, the construction field seems to be struggling to catch up. Currently, the structural condition of a building is still predominantly manually inspected. In simple terms, even nowadays when a structure needs to be inspected for any damage, an engineer will manually check all the surfaces and take a bunch of photos while keeping notes of the position of any cracks. Then a few more hours need to be spent at the office to sort all the photos and notes trying to make a meaningful report out of it. Apparently this a laborious, costly, and subjective process. On top of that, safety concerns arise since there are parts of structures with access restrictions and difficult to reach. To give you an example, the Golden Gate Bridge needs to be periodically inspected. In other words, up to very recently there would be specially trained people who would climb across this picturesque structure and check every inch of it.
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