A business model of sharing economy has rapidly developed. It is predicted to profoundly influent the society. However, because imperfect management might ruin its operation and future growth, the sharing economy model is rife with conflicts and paradox in terms of logic, boundary, and influence. Taking the Free-floating bicycle sharing (FFBS) as an example, although it is becoming more and more popular in many big cities, the service has led to many problems, including road congestion, parking disorder, and mismanagement of bicycle information. As a result, its sustainable development is severely affected. In this study, we used FFBS as a case study to investigate the relations among government, companies and users. We interviewed varies stakeholders and discussed their different perspectives of collaborative governance. It showed that the parking location, usage frequency, number of parked bicycles, and bicycle health status could be used as indicators to monitor the service situations. We also identified that the wanton occupancy of public space was the major problem of FFBS. To address these issues, we developed a real-time information management platform with information visualization, aimed to help the government, the company, and the users to gather the bicycle information and coordinate their sharing usage. This platform could promote the sustainability of FFBS system and foster the development of other forms of sharing economy in the future.
The creativity of an excellent design work generally comes from the inspiration and innovation of its main visual features. The similarity between the main visual elements is the most important indicator for detecting plagiarism of design concepts, which is important to protect cultural heritage and copyright. The purpose of this paper is to develop an efficient similarity evaluation scheme for graphic design. A novel deep visual saliency feature extraction generative adversarial network is proposed to deal with the problem of lack of training examples. It consists of two networks: one predicts visual a saliency feature map from an input image; the other takes the output of the first to distinguish whether a visual saliency feature map is a predicted one or ground truth. Different from traditional saliency generative adversarial networks, a residual refinement module is connected after the encoding and decoding network. Design importance maps generated by professional designers are used to guide the network training. A saliency-based segmentation method is developed to not only locate the optimal layout regions but also notice insignificant regions. Priorities are then assigned to different visual elements. Experimental results show that the proposed model obtains state-of-the-art performance among various similarity measurement methods.
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