The rapid development of online social networks (e.g., Twitter and Facebook) has promoted research related to social networks in which link prediction is a key problem. Although numerous attempts have been made for link prediction based on network structure, node attribute and so on, few of the current studies have considered the impact of information diffusion on link creation and prediction. This paper mainly addresses Sina Weibo, which is the largest microblog platform with Chinese characteristics, and proposes the hypothesis that information diffusion influences link creation and verifies the hypothesis based on real data analysis. We also detect an important feature from the information diffusion process, which is used to promote link prediction performance. Finally, the experimental results on Sina Weibo dataset have demonstrated the effectiveness of our methods.
Human activity recognition (HAR) based on sensor data is a significant problem in pervasive computing. In recent years, deep learning has become the dominating approach in this field, due to its high accuracy. However, it is difficult to make accurate identification for the activities of one individual using a model trained on data from other users. The decline on the accuracy of recognition restricts activity recognition in practice. At present, there is little research on the transferring of deep learning model in this field. This is the first time as we known, an empirical study was carried out on deep transfer learning between users with unlabeled data of target. We compared several widely-used algorithms and found that Maximum Mean Discrepancy (MMD) method is most suitable for HAR. We studied the distribution of features generated from sensor data. We improved the existing method from the aspect of features distribution with center loss and get better results. The observations and insights in this study have deepened the understanding of transfer learning in the activity recognition field and provided guidance for further research.
Automatic reading for water meter is one of the practical demands in smart city applications. Due to the high cost, it is not feasible to replace the old mechanical water meter with a new embedded electronic device. Recently, image recognition based meter reading methods have become research hotspots. However, illumination, occlusion, energy and computational consuming in IoT environment bring challenges to these methods. In this paper, we design and implement a smart water meter reading system to handle this issue. Specifically, we first propose a novel light-weight spliced convolution network to recognize the meter number, which simplifies standard 3 × 3 convolutions by splicing a certain number of 1 × 1 and 3 × 3 size kernel. We then prove the superiority of our network by theoretical analysis. Second, we have implemented the prototype which can handle huge real-time data base on the distributed cloud platform. Base on this system, our system can provide industrial service. Finally, we conduct real-world dataset to verify the performance of the system. The experimental results demonstrate that our proposed light-weight spliced convolution network can reduce nearly 10× computational consuming, 7× model space, and save 3× running time comparing with standard convolution network.INDEX TERMS Water meter reading, spliced convolution network, cyber-physical-system, smart city.
PurposeThis study aims to explore how to respond to market turbulence by big data analytics (BDA) capability and mass customization capability (MCC) from the perspective of organizational information processing theory (OIPT).Design/methodology/approachThis study examines the research hypotheses using hierarchical regression analysis by collecting data from 277 Chinese firms.FindingsThe results reveal that supply chain agility (SCA) completely mediates the impacts of technical skills on product-oriented and service-oriented MCC and the impact of data-driven decision-making culture (DDC) on service-oriented MCC. SCA also partially mediates the impacts of managerial skills on two dimensions of MCC and the impact of DDC on product-oriented MCC. In addition, market turbulence strengthens the impact of managerial skills on SCA.Originality/valueThis study provides insightful contributions and implications for enhancing MCC to cope with market turbulence.
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