In this paper, we provide an idea about how to utilize the deep neural network with large scale social network data to judge the quality of fashion images. Specifically, our aim is to build a deep neural network based model which is able to predict the popularity of fashion-related images. Convolutional Neural Network (CNN) and Multi-layer Perceptron (MLP) are the two major tools to construct the model architecture, in which the CNN is responsible for analyzing images and the MLP is responsible for analyzing other types of social network meta data. Based on this general idea, various tentative model structures are proposed, implemented, and compared in this research. To perform experiments, we constructed a fashion-related dataset which contains over 1 million records from the online social network. Though no real word prediction task has been tried yet, according to the result of dataset-based tests, our models demonstrate good abilities on predicting the popularity of fashion from the online social network using the Xception CNN. However, we also find a very interesting phenomenon, which intuitively indicates there may be limited correlation between popularity and visual design of a fashion due to the power and influence of the online social network.
The notion of edge computing has gained much attraction in recent years as an enabling technology for smart city and internet of things applications. In this paper we report the system challenges and solutions encountered when designing and deploying the Macao Polytechnic Institute Smart City sensing system. A small fleet that serves as proof-of-concept for a country wide urban sensing system in Macao, S.A.R. We focus our attention on how a careful system design can ensure smooth operations and mitigate the natural tension between fleet owners and smart city operators. The first are keen to maximize the fleet operations and reduce the downtime, the later are interested in using the fleets to harvest high-quality and fine granularity sensor data. In designing the Macao Polytechnic Institute vehicular cloud we approached the design constraints and proposed system solutions to minimize the impact of the sensing platform on the fleet operations.