Does naming and shaming states affect respect for human rights in those states? This article argues that incentives to change repressive behaviour when facing international condemnation vary across regime types. In democracies and hybrid regimes – which combine democratic and authoritarian elements – opposition parties and relatively free presses paradoxically make rulers less likely to change behaviour when facing international criticism. In contrast, autocracies, which lack these domestic sources of information on abuses, are more sensitive to international shaming. Using data on naming and shaming taken from Western press reports and Amnesty International, the authors demonstrate that naming and shaming is associated with improved human rights outcomes in autocracies, but with either no effect or a worsening of outcomes in democracies and hybrid regimes.
In digital and green city initiatives, smart mobility is a key aspect of developing smart cities and it is important for built-up areas worldwide. Double-parking and busy roadside activities such as frequent loading and unloading of trucks, have a negative impact on traffic situations, especially in cities with high transportation density. Hence, a real-time internet of things (IoT)-based system for surveillance of roadside loading and unloading bays is needed. In this paper, a fully integrated solution is developed by equipping high-definition smart cameras with wireless communication for traffic surveillance. Henceforth, this system is referred to as a computer vision-based roadside occupation surveillance system (CVROSS). Through a vision-based network, real-time roadside traffic images, such as images of loading or unloading activities, are captured automatically. By making use of the collected data, decision support on roadside occupancy and vacancy can be evaluated by means of fuzzy logic and visualized for users, thus enhancing the transparency of roadside activities. The CVROSS was designed and tested in Hong Kong to validate the accuracy of parking-gap estimation and system performance, aiming at facilitating traffic and fleet management for smart mobility.
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